Top 100 Data Science and Data Analytics Interview Questions and Answers

Data Science Bias Variance Trade-off
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Below and the Top 100 Data Science and Data Analytics Interview Questions and Answers dumps.

What is Data Science? 

Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. How is this different from what statisticians have been doing for years? The answer lies in the difference between explaining and predicting: statisticians work a posteriori, explaining the results and designing a plan; data scientists use historical data to make predictions.

How does data cleaning play a vital role in the analysis? 

Data cleaning can help in analysis because:

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  • Cleaning data from multiple sources helps transform it into a format that data analysts or data scientists can work with.
  • Data Cleaning helps increase the accuracy of the model in machine learning.
  • It is a cumbersome process because as the number of data sources increases, the time taken to clean the data increases exponentially due to the number of sources and the volume of data generated by these sources.
  • It might take up to 80% of the time for just cleaning data making it a critical part of the analysis task

What is linear regression? What do the terms p-value, coefficient, and r-squared value mean? What is the significance of each of these components?

Reference  

Imagine you want to predict the price of a house. That will depend on some factors, called independent variables, such as location, size, year of construction… if we assume there is a linear relationship between these variables and the price (our dependent variable), then our price is predicted by the following function: Y = a + bX
The p-value in the table is the minimum I (the significance level) at which the coefficient is relevant. The lower the p-value, the more important is the variable in predicting the price. Usually we set a 5% level, so that we have a 95% confidentiality that our variable is relevant.
The p-value is used as an alternative to rejection points to provide the smallest level of significance at which the null hypothesis would be rejected. A smaller p-value means that there is stronger evidence in favor of the alternative hypothesis.
The coefficient value signifies how much the mean of the dependent variable changes given a one-unit shift in the independent variable while holding other variables in the model constant. This property of holding the other variables constant is crucial because it allows you to assess the effect of each variable in isolation from the others.
R squared (R2) is a statistical measure that represents the proportion of the variance for a dependent variable that’s explained by an independent variable or variables in a regression model.

Credit: Steve Nouri

What is sampling? How many sampling methods do you know? 

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Reference

Data sampling is a statistical analysis technique used to select, manipulate and analyze a representative subset of data points to identify patterns and trends in the larger data set being examined. It enables data scientists, predictive modelers and other data analysts to work with a small, manageable amount of data about a statistical population to build and run analytical models more quickly, while still producing accurate findings.

Sampling can be particularly useful with data sets that are too large to efficiently analyze in full – for example, in big data analytics applications or surveys. Identifying and analyzing a representative sample is more efficient and cost-effective than surveying the entirety of the data or population.
An important consideration, though, is the size of the required data sample and the possibility of introducing a sampling error. In some cases, a small sample can reveal the most important information about a data set. In others, using a larger sample can increase the likelihood of accurately representing the data as a whole, even though the increased size of the sample may impede ease of manipulation and interpretation.
There are many different methods for drawing samples from data; the ideal one depends on the data set and situation. Sampling can be based on probability, an approach that uses random numbers that correspond to points in the data set to ensure that there is no correlation between points chosen for the sample. Further variations in probability sampling include:

Simple random sampling: Software is used to randomly select subjects from the whole population.
• Stratified sampling: Subsets of the data sets or population are created based on a common factor,
and samples are randomly collected from each subgroup. A sample is drawn from each strata (using a random sampling method like simple random sampling or systematic sampling).
o EX: In the image below, let’s say you need a sample size of 6. Two members from each
group (yellow, red, and blue) are selected randomly. Make sure to sample proportionally:
In this simple example, 1/3 of each group (2/6 yellow, 2/6 red and 2/6 blue) has been
sampled. If you have one group that’s a different size, make sure to adjust your
proportions. For example, if you had 9 yellow, 3 red and 3 blue, a 5-item sample would
consist of 3/9 yellow (i.e. one third), 1/3 red and 1/3 blue.
• Cluster sampling: The larger data set is divided into subsets (clusters) based on a defined factor, then a random sampling of clusters is analyzed. The sampling unit is the whole cluster; Instead of sampling individuals from within each group, a researcher will study whole clusters.
o EX: In the image below, the strata are natural groupings by head color (yellow, red, blue).
A sample size of 6 is needed, so two of the complete strata are selected randomly (in this
example, groups 2 and 4 are chosen).

Data Science Stratified Sampling - Cluster Sampling
Data Science Stratified Sampling – Cluster Sampling

– Cluster Sampling[/caption]

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  • Multistage sampling: A more complicated form of cluster sampling, this method also involves dividing the larger population into a number of clusters. Second-stage clusters are then broken out based on a secondary factor, and those clusters are then sampled and analyzed. This staging could continue as multiple subsets are identified, clustered and analyzed.
    • Systematic sampling: A sample is created by setting an interval at which to extract data from the larger population – for example, selecting every 10th row in a spreadsheet of 200 items to create a sample size of 20 rows to analyze.

Sampling can also be based on non-probability, an approach in which a data sample is determined and extracted based on the judgment of the analyst. As inclusion is determined by the analyst, it can be more difficult to extrapolate whether the sample accurately represents the larger population than when probability sampling is used.

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Non-probability data sampling methods include:
• Convenience sampling: Data is collected from an easily accessible and available group.
• Consecutive sampling: Data is collected from every subject that meets the criteria until the predetermined sample size is met.
• Purposive or judgmental sampling: The researcher selects the data to sample based on predefined criteria.
• Quota sampling: The researcher ensures equal representation within the sample for all subgroups in the data set or population (random sampling is not used).

Quota sampling
Quota sampling

Once generated, a sample can be used for predictive analytics. For example, a retail business might use data sampling to uncover patterns about customer behavior and predictive modeling to create more effective sales strategies.

Credit: Steve Nouri

What are the assumptions required for linear regression?

There are four major assumptions:

There is a linear relationship between the dependent variables and the regressors, meaning the model you are creating actually fits the data,
• The errors or residuals of the data are normally distributed and independent from each other,
• There is minimal multicollinearity between explanatory variables, and
• Homoscedasticity. This means the variance around the regression line is the same for all values of the predictor variable.

What is a statistical interaction?

Reference: Statistical Interaction

Basically, an interaction is when the effect of one factor (input variable) on the dependent variable (output variable) differs among levels of another factor. When two or more independent variables are involved in a research design, there is more to consider than simply the “main effect” of each of the independent variables (also termed “factors”). That is, the effect of one independent variable on the dependent variable of interest may not be the same at all levels of the other independent variable. Another way to put this is that the effect of one independent variable may depend on the level of the other independent
variable. In order to find an interaction, you must have a factorial design, in which the two (or more) independent variables are “crossed” with one another so that there are observations at every
combination of levels of the two independent variables. EX: stress level and practice to memorize words: together they may have a lower performance. 

What is selection bias? 

Reference

Selection (or ‘sampling’) bias occurs when the sample data that is gathered and prepared for modeling has characteristics that are not representative of the true, future population of cases the model will see.
That is, active selection bias occurs when a subset of the data is systematically (i.e., non-randomly) excluded from analysis.

Selection bias is a kind of error that occurs when the researcher decides what has to be studied. It is associated with research where the selection of participants is not random. Therefore, some conclusions of the study may not be accurate.

The types of selection bias include:
Sampling bias: It is a systematic error due to a non-random sample of a population causing some members of the population to be less likely to be included than others resulting in a biased sample.
Time interval: A trial may be terminated early at an extreme value (often for ethical reasons), but the extreme value is likely to be reached by the variable with the largest variance, even if all variables have a similar mean.
Data: When specific subsets of data are chosen to support a conclusion or rejection of bad data on arbitrary grounds, instead of according to previously stated or generally agreed criteria.
Attrition: Attrition bias is a kind of selection bias caused by attrition (loss of participants)
discounting trial subjects/tests that did not run to completion.

What is an example of a data set with a non-Gaussian distribution?

Reference

The Gaussian distribution is part of the Exponential family of distributions, but there are a lot more of them, with the same sort of ease of use, in many cases, and if the person doing the machine learning has a solid grounding in statistics, they can be utilized where appropriate.

Binomial: multiple toss of a coin Bin(n,p): the binomial distribution consists of the probabilities of each of the possible numbers of successes on n trials for independent events that each have a probability of p of
occurring.

Bernoulli: Bin(1,p) = Be(p)
Poisson: Pois(λ)

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What is bias-variance trade-off?

Bias: Bias is an error introduced in the model due to the oversimplification of the algorithm used (does not fit the data properly). It can lead to under-fitting.
Low bias machine learning algorithms — Decision Trees, k-NN and SVM
High bias machine learning algorithms — Linear Regression, Logistic Regression

Variance: Variance is error introduced in the model due to a too complex algorithm, it performs very well in the training set but poorly in the test set. It can lead to high sensitivity and overfitting.
Possible high variance – polynomial regression

Normally, as you increase the complexity of your model, you will see a reduction in error due to lower bias in the model. However, this only happens until a particular point. As you continue to make your model more complex, you end up over-fitting your model and hence your model will start suffering from high variance.

bias-variance trade-off

Bias-Variance trade-off: The goal of any supervised machine learning algorithm is to have low bias and low variance to achieve good prediction performance.

1. The k-nearest neighbor algorithm has low bias and high variance, but the trade-off can be changed by increasing the value of k which increases the number of neighbors that contribute to the prediction and in turn increases the bias of the model.
2. The support vector machine algorithm has low bias and high variance, but the trade-off can be changed by increasing the C parameter that influences the number of violations of the margin allowed in the training data which increases the bias but decreases the variance.
3. The decision tree has low bias and high variance, you can decrease the depth of the tree or use fewer attributes.
4. The linear regression has low variance and high bias, you can increase the number of features or use another regression that better fits the data.

There is no escaping the relationship between bias and variance in machine learning. Increasing the bias will decrease the variance. Increasing the variance will decrease bias.

What is a confusion matrix?

The confusion matrix is a 2X2 table that contains 4 outputs provided by the binary classifier.

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A data set used for performance evaluation is called a test data set. It should contain the correct labels and predicted labels. The predicted labels will exactly the same if the performance of a binary classifier is perfect. The predicted labels usually match with part of the observed labels in real-world scenarios.
A binary classifier predicts all data instances of a test data set as either positive or negative. This produces four outcomes: TP, FP, TN, FN. Basic measures derived from the confusion matrix:

What is the difference between “long” and “wide” format data?

In the wide-format, a subject’s repeated responses will be in a single row, and each response is in a separate column. In the long-format, each row is a one-time point per subject. You can recognize data in wide format by the fact that columns generally represent groups (variables).

difference between “long” and “wide” format data

What do you understand by the term Normal Distribution?

Data is usually distributed in different ways with a bias to the left or to the right or it can all be jumbled up. However, there are chances that data is distributed around a central value without any bias to the left or right and reaches normal distribution in the form of a bell-shaped curve.

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Data Science: Normal Distribution

The random variables are distributed in the form of a symmetrical, bell-shaped curve. Properties of Normal Distribution are as follows:

1. Unimodal (Only one mode)
2. Symmetrical (left and right halves are mirror images)
3. Bell-shaped (maximum height (mode) at the mean)
4. Mean, Mode, and Median are all located in the center
5. Asymptotic

What is correlation and covariance in statistics?

Correlation is considered or described as the best technique for measuring and also for estimating the quantitative relationship between two variables. Correlation measures how strongly two variables are related. Given two random variables, it is the covariance between both divided by the product of the two standard deviations of the single variables, hence always between -1 and 1.

correlation and covariance

Covariance is a measure that indicates the extent to which two random variables change in cycle. It explains the systematic relation between a pair of random variables, wherein changes in one variable reciprocal by a corresponding change in another variable.

correlation and covariance in statistics

What is the difference between Point Estimates and Confidence Interval? 

Point Estimation gives us a particular value as an estimate of a population parameter. Method of Moments and Maximum Likelihood estimator methods are used to derive Point Estimators for population parameters.

A confidence interval gives us a range of values which is likely to contain the population parameter. The confidence interval is generally preferred, as it tells us how likely this interval is to contain the population parameter. This likeliness or probability is called Confidence Level or Confidence coefficient and represented by 1 − ∝, where ∝ is the level of significance.

What is the goal of A/B Testing?

It is a hypothesis testing for a randomized experiment with two variables A and B.
The goal of A/B Testing is to identify any changes to the web page to maximize or increase the outcome of interest. A/B testing is a fantastic method for figuring out the best online promotional and marketing strategies for your business. It can be used to test everything from website copy to sales emails to search ads. An example of this could be identifying the click-through rate for a banner ad.

What is p-value?

When you perform a hypothesis test in statistics, a p-value can help you determine the strength of your results. p-value is the minimum significance level at which you can reject the null hypothesis. The lower the p-value, the more likely you reject the null hypothesis.

What do you understand by statistical power of sensitivity and how do you calculate it? 

Sensitivity is commonly used to validate the accuracy of a classifier (Logistic, SVM, Random Forest etc.). Sensitivity = [ TP / (TP +TN)]

Why is Re-sampling done?

A Gentle Introduction to Statistical Sampling and Resampling

  • Sampling is an active process of gathering observations with the intent of estimating a population variable.
  • Resampling is a methodology of economically using a data sample to improve the accuracy and quantify the uncertainty of a population parameter. Resampling methods, in fact, make use of a nested resampling method.

Once we have a data sample, it can be used to estimate the population parameter. The problem is that we only have a single estimate of the population parameter, with little idea of the variability or uncertainty in the estimate. One way to address this is by estimating the population parameter multiple times from our data sample. This is called resampling. Statistical resampling methods are procedures that describe how to economically use available data to estimate a population parameter. The result can be both a more accurate estimate of the parameter (such as taking the mean of the estimates) and a quantification of the uncertainty of the estimate (such as adding a confidence interval).

Resampling methods are very easy to use, requiring little mathematical knowledge. A downside of the methods is that they can be computationally very expensive, requiring tens, hundreds, or even thousands of resamples in order to develop a robust estimate of the population parameter.

The key idea is to resample from the original data — either directly or via a fitted model — to create replicate datasets, from which the variability of the quantiles of interest can be assessed without longwinded and error-prone analytical calculation. Because this approach involves repeating the original data analysis procedure with many replicate sets of data, these are sometimes called computer-intensive methods. Each new subsample from the original data sample is used to estimate the population parameter. The sample of estimated population parameters can then be considered with statistical tools in order to quantify the expected value and variance, providing measures of the uncertainty of the
estimate. Statistical sampling methods can be used in the selection of a subsample from the original sample.

A key difference is that process must be repeated multiple times. The problem with this is that there will be some relationship between the samples as observations that will be shared across multiple subsamples. This means that the subsamples and the estimated population parameters are not strictly identical and independently distributed. This has implications for statistical tests performed on the sample of estimated population parameters downstream, i.e. paired statistical tests may be required. 

Two commonly used resampling methods that you may encounter are k-fold cross-validation and the bootstrap.

  • Bootstrap. Samples are drawn from the dataset with replacement (allowing the same sample to appear more than once in the sample), where those instances not drawn into the data sample may be used for the test set.
  • k-fold Cross-Validation. A dataset is partitioned into k groups, where each group is given the opportunity of being used as a held out test set leaving the remaining groups as the training set. The k-fold cross-validation method specifically lends itself to use in the evaluation of predictive models that are repeatedly trained on one subset of the data and evaluated on a second held-out subset of the data.  

Resampling is done in any of these cases:

  • Estimating the accuracy of sample statistics by using subsets of accessible data or drawing randomly with replacement from a set of data points
  • Substituting labels on data points when performing significance tests
  • Validating models by using random subsets (bootstrapping, cross-validation)

What are the differences between over-fitting and under-fitting?

In statistics and machine learning, one of the most common tasks is to fit a model to a set of training data, so as to be able to make reliable predictions on general untrained data.

In overfitting, a statistical model describes random error or noise instead of the underlying relationship.
Overfitting occurs when a model is excessively complex, such as having too many parameters relative to the number of observations. A model that has been overfitted, has poor predictive performance, as it overreacts to minor fluctuations in the training data.

Underfitting occurs when a statistical model or machine learning algorithm cannot capture the underlying trend of the data. Underfitting would occur, for example, when fitting a linear model to non-linear data.
Such a model too would have poor predictive performance.

How to combat Overfitting and Underfitting?

To combat overfitting:
1. Add noise
2. Feature selection
3. Increase training set
4. L2 (ridge) or L1 (lasso) regularization; L1 drops weights, L2 no
5. Use cross-validation techniques, such as k folds cross-validation
6. Boosting and bagging
7. Dropout technique
8. Perform early stopping
9. Remove inner layers
To combat underfitting:
1. Add features
2. Increase time of training

What is regularization? Why is it useful?

Regularization is the process of adding tuning parameter (penalty term) to a model to induce smoothness in order to prevent overfitting. This is most often done by adding a constant multiple to an existing weight vector. This constant is often the L1 (Lasso – |∝|) or L2 (Ridge – ∝2). The model predictions should then minimize the loss function calculated on the regularized training set.

What Is the Law of Large Numbers? 

It is a theorem that describes the result of performing the same experiment a large number of times. This theorem forms the basis of frequency-style thinking. It says that the sample means, the sample variance and the sample standard deviation converge to what they are trying to estimate. According to the law, the average of the results obtained from a large number of trials should be close to the expected value and will tend to become closer to the expected value as more trials are performed.

What Are Confounding Variables?

In statistics, a confounder is a variable that influences both the dependent variable and independent variable.

If you are researching whether a lack of exercise leads to weight gain:
lack of exercise = independent variable
weight gain = dependent variable
A confounding variable here would be any other variable that affects both of these variables, such as the age of the subject.

What is Survivorship Bias?

It is the logical error of focusing aspects that support surviving some process and casually overlooking those that did not work because of their lack of prominence. This can lead to wrong conclusions in numerous different means. For example, during a recession you look just at the survived businesses, noting that they are performing poorly. However, they perform better than the rest, which is failed, thus being removed from the time series.

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Explain how a ROC curve works?

The ROC curve is a graphical representation of the contrast between true positive rates and false positive rates at various thresholds. It is often used as a proxy for the trade-off between the sensitivity (true positive rate) and false positive rate.

Data Science ROC Curve

What is TF/IDF vectorization?

TF-IDF is short for term frequency-inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus. It is often used as a weighting factor in information retrieval and text mining.

Data Science TF IDF Vectorization

The TF-IDF value increases proportionally to the number of times a word appears in the document but is offset by the frequency of the word in the corpus, which helps to adjust for the fact that some words appear more frequently in general.

Python or R – Which one would you prefer for text analytics?

We will prefer Python because of the following reasons:
• Python would be the best option because it has Pandas library that provides easy to use data structures and high-performance data analysis tools.
• R is more suitable for machine learning than just text analysis.
• Python performs faster for all types of text analytics.

How does data cleaning play a vital role in the analysis? 

Data cleaning can help in analysis because:

  • Cleaning data from multiple sources helps transform it into a format that data analysts or data scientists can work with.
  • Data Cleaning helps increase the accuracy of the model in machine learning.
  • It is a cumbersome process because as the number of data sources increases, the time taken to clean the data increases exponentially due to the number of sources and the volume of data generated by these sources.
  • It might take up to 80% of the time for just cleaning data making it a critical part of the analysis task

Differentiate between univariate, bivariate and multivariate analysis. 

Univariate analyses are descriptive statistical analysis techniques which can be differentiated based on one variable involved at a given point of time. For example, the pie charts of sales based on territory involve only one variable and can the analysis can be referred to as univariate analysis.

The bivariate analysis attempts to understand the difference between two variables at a time as in a scatterplot. For example, analyzing the volume of sale and spending can be considered as an example of bivariate analysis.

Multivariate analysis deals with the study of more than two variables to understand the effect of variables on the responses.

Explain Star Schema

It is a traditional database schema with a central table. Satellite tables map IDs to physical names or descriptions and can be connected to the central fact table using the ID fields; these tables are known as lookup tables and are principally useful in real-time applications, as they save a lot of memory. Sometimes star schemas involve several layers of summarization to recover information faster.

What is Cluster Sampling?

Cluster sampling is a technique used when it becomes difficult to study the target population spread across a wide area and simple random sampling cannot be applied. Cluster Sample is a probability sample where each sampling unit is a collection or cluster of elements.

For example, a researcher wants to survey the academic performance of high school students in Japan. He can divide the entire population of Japan into different clusters (cities). Then the researcher selects a number of clusters depending on his research through simple or systematic random sampling.

What is Systematic Sampling? 

Systematic sampling is a statistical technique where elements are selected from an ordered sampling frame. In systematic sampling, the list is progressed in a circular manner so once you reach the end of the list, it is progressed from the top again. The best example of systematic sampling is equal probability method.

What are Eigenvectors and Eigenvalues? 

Eigenvectors are used for understanding linear transformations. In data analysis, we usually calculate the eigenvectors for a correlation or covariance matrix. Eigenvectors are the directions along which a particular linear transformation acts by flipping, compressing or stretching.
Eigenvalue can be referred to as the strength of the transformation in the direction of eigenvector or the factor by which the compression occurs.

Give Examples where a false positive is important than a false negative?

Let us first understand what false positives and false negatives are:

  • False Positives are the cases where you wrongly classified a non-event as an event a.k.a Type I error
  • False Negatives are the cases where you wrongly classify events as non-events, a.k.a Type II error.

Example 1: In the medical field, assume you have to give chemotherapy to patients. Assume a patient comes to that hospital and he is tested positive for cancer, based on the lab prediction but he actually doesn’t have cancer. This is a case of false positive. Here it is of utmost danger to start chemotherapy on this patient when he actually does not have cancer. In the absence of cancerous cell, chemotherapy will do certain damage to his normal healthy cells and might lead to severe diseases, even cancer.

Example 2: Let’s say an e-commerce company decided to give $1000 Gift voucher to the customers whom they assume to purchase at least $10,000 worth of items. They send free voucher mail directly to 100 customers without any minimum purchase condition because they assume to make at least 20% profit on sold items above $10,000. Now the issue is if we send the $1000 gift vouchers to customers who have not actually purchased anything but are marked as having made $10,000 worth of purchase

Give Examples where a false negative important than a false positive? And vice versa?

Example 1 FN: What if Jury or judge decides to make a criminal go free?

Example 2 FN: Fraud detection.

Example 3 FP: customer voucher use promo evaluation: if many used it and actually if was not true, promo sucks

Give Examples where both false positive and false negatives are equally important? 

In the Banking industry giving loans is the primary source of making money but at the same time if your repayment rate is not good you will not make any profit, rather you will risk huge losses.
Banks don’t want to lose good customers and at the same point in time, they don’t want to acquire bad customers. In this scenario, both the false positives and false negatives become very important to measure.

What is the Difference between a Validation Set and a Test Set?

A Training Set:
• to fit the parameters i.e. weights

A Validation set:
• part of the training set
• for parameter selection
• to avoid overfitting

A Test set:
• for testing or evaluating the performance of a trained machine learning model, i.e. evaluating the
predictive power and generalization.

What is cross-validation?

Reference: k-fold cross validation 

Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data sample. The procedure has a single parameter called k that refers to the number of groups that a given data sample is to be split into. As such, the procedure is often called k-fold cross-validation. When a specific value for k is chosen, it may be used in place of k in the reference to the model, such as k=10 becoming 10-fold cross-validation. Mainly used in backgrounds where the objective is forecast, and one wants to estimate how accurately a model will accomplish in practice.

Cross-validation is primarily used in applied machine learning to estimate the skill of a machine learning model on unseen data. That is, to use a limited sample in order to estimate how the model is expected to perform in general when used to make predictions on data not used during the training of the model.

It is a popular method because it is simple to understand and because it generally results in a less biased or less optimistic estimate of the model skill than other methods, such as a simple train/test split.

The general procedure is as follows:
1. Shuffle the dataset randomly.
2. Split the dataset into k groups
3. For each unique group:
a. Take the group as a hold out or test data set
b. Take the remaining groups as a training data set
c. Fit a model on the training set and evaluate it on the test set
d. Retain the evaluation score and discard the model
4. Summarize the skill of the model using the sample of model evaluation scores

Data Science Cross Validation

There is an alternative in Scikit-Learn called Stratified k fold, in which the split is shuffled to make it sure you have a representative sample of each class and a k fold in which you may not have the assurance of it (not good with a very unbalanced dataset).

What is Machine Learning?

Machine learning is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine Learning explores the study and construction of algorithms that can learn from and make predictions on data. You select a model to train and then manually perform feature extraction. Used to devise complex models and algorithms that lend themselves to a prediction which in commercial use is known as predictive analytics.

What is Supervised Learning? 

Supervised learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples.

Algorithms: Support Vector Machines, Regression, Naive Bayes, Decision Trees, K-nearest Neighbor Algorithm and Neural Networks

Example: If you built a fruit classifier, the labels will be “this is an orange, this is an apple and this is a banana”, based on showing the classifier examples of apples, oranges and bananas.

What is Unsupervised learning?

Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labelled responses.

Algorithms: Clustering, Anomaly Detection, Neural Networks and Latent Variable Models

Example: In the same example, a fruit clustering will categorize as “fruits with soft skin and lots of dimples”, “fruits with shiny hard skin” and “elongated yellow fruits”.

What are the various Machine Learning algorithms?

Machine Learning Algorithms

What is “Naive” in a Naive Bayes?

Reference: Naive Bayes Classifier on Wikipedia

Naive Bayes methods are a set of supervised learning algorithms based on applying Bayes’ theorem with the “naive” assumption of conditional independence between every pair of features given the value of the class variable. Bayes’ theorem states the following relationship, given class variable y and dependent feature vector X1through Xn:

Machine Learning Algorithms Naive Bayes
Machine Learning Algorithms Naive Bayes

What is PCA (Principal Component Analysis)? When do you use it?

Reference: PCA on wikipedia

Principal component analysis (PCA) is a statistical method used in Machine Learning. It consists in projecting data in a higher dimensional space into a lower dimensional space by maximizing the variance of each dimension.

The process works as following. We define a matrix A with > rows (the single observations of a dataset – in a tabular format, each single row) and @ columns, our features. For this matrix we construct a variable space with as many dimensions as there are features. Each feature represents one coordinate axis. For each feature, the length has been standardized according to a scaling criterion, normally by scaling to unit variance. It is determinant to scale the features to a common scale, otherwise the features with a greater magnitude will weigh more in determining the principal components. Once plotted all the observations and computed the mean of each variable, that mean will be represented by a point in the center of our plot (the center of gravity). Then, we subtract each observation with the mean, shifting the coordinate system with the center in the origin. The best fitting line resulting is the line that best accounts for the shape of the point swarm. It represents the maximum variance direction in the data. Each observation may be projected onto this line in order to get a coordinate value along the PC-line. This value is known as a score. The next best-fitting line can be similarly chosen from directions perpendicular to the first.
Repeating this process yields an orthogonal basis in which different individual dimensions of the data are uncorrelated. These basis vectors are called principal components.

Machine Learning Algorithms PCA

PCA is mostly used as a tool in exploratory data analysis and for making predictive models. It is often used to visualize genetic distance and relatedness between populations.

SVM (Support Vector Machine)  algorithm

Reference: SVM on wikipedia

Classifying data is a common task in machine learning. Suppose some given data points each belong to one of two classes, and the goal is to decide which class a new data point will be in. In the case of supportvector machines, a data point is viewed as a p-dimensional vector (a list of p numbers), and we want to know whether we can separate such points with a (p − 1)-dimensional hyperplane. This is called a linear classifier. There are many hyperplanes that might classify the data. One reasonable choice as the best hyperplane is the one that represents the largest separation, or margin, between the two classes. So, we
choose the hyperplane so that the distance from it to the nearest data point on each side is maximized. If such a hyperplane exists, it is known as the maximum-margin hyperplane and the linear classifier it defines is known as a maximum-margin classifier; or equivalently, the perceptron of optimal stability. The best hyper plane that divides the data is H3.

  • SVMs are helpful in text and hypertext categorization, as their application can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings.
  • Some methods for shallow semantic parsing are based on support vector machines.
  • Classification of images can also be performed using SVMs. Experimental results show that SVMs achieve significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback.
  • Classification of satellite data like SAR data using supervised SVM.
  • Hand-written characters can be recognized using SVM.

What are the support vectors in SVM? 

Machine Learning Algorithms Support Vectors

In the diagram, we see that the sketched lines mark the distance from the classifier (the hyper plane) to the closest data points called the support vectors (darkened data points). The distance between the two thin lines is called the margin.

To extend SVM to cases in which the data are not linearly separable, we introduce the hinge loss function, max (0, 1 – yi(w∙ xi − b)). This function is zero if x lies on the correct side of the margin. For data on the wrong side of the margin, the function’s value is proportional to the distance from the margin. 

What are the different kernels in SVM?

There are four types of kernels in SVM.
1. LinearKernel
2. Polynomial kernel
3. Radial basis kernel
4. Sigmoid kernel

What are the most known ensemble algorithms? 

Reference: Ensemble Algorithms

The most popular trees are: AdaBoost, Random Forest, and  eXtreme Gradient Boosting (XGBoost).

AdaBoost is best used in a dataset with low noise, when computational complexity or timeliness of results is not a main concern and when there are not enough resources for broader hyperparameter tuning due to lack of time and knowledge of the user.

Random forests should not be used when dealing with time series data or any other data where look-ahead bias should be avoided, and the order and continuity of the samples need to be ensured. This algorithm can handle noise relatively well, but more knowledge from the user is required to adequately tune the algorithm compared to AdaBoost.

The main advantages of XGBoost is its lightning speed compared to other algorithms, such as AdaBoost, and its regularization parameter that successfully reduces variance. But even aside from the regularization parameter, this algorithm leverages a learning rate (shrinkage) and subsamples from the features like random forests, which increases its ability to generalize even further. However, XGBoost is more difficult to understand, visualize and to tune compared to AdaBoost and random forests. There is a multitude of hyperparameters that can be tuned to increase performance.

What is Deep Learning?

Deep Learning is nothing but a paradigm of machine learning which has shown incredible promise in recent years. This is because of the fact that Deep Learning shows a great analogy with the functioning of the neurons in the human brain.

Deep Learning

What is the difference between machine learning and deep learning?

Deep learning & Machine learning: what’s the difference?

Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Machine learning can be categorized in the following four categories.
1. Supervised machine learning,
2. Semi-supervised machine learning,
3. Unsupervised machine learning,
4. Reinforcement learning.

Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.

Machine Learning vs Deep Learning

• The main difference between deep learning and machine learning is due to the way data is
presented in the system. Machine learning algorithms almost always require structured data, while deep learning networks rely on layers of ANN (artificial neural networks).

• Machine learning algorithms are designed to “learn” to act by understanding labeled data and then use it to produce new results with more datasets. However, when the result is incorrect, there is a need to “teach them”. Because machine learning algorithms require bulleted data, they are not suitable for solving complex queries that involve a huge amount of data.

• Deep learning networks do not require human intervention, as multilevel layers in neural
networks place data in a hierarchy of different concepts, which ultimately learn from their own mistakes. However, even they can be wrong if the data quality is not good enough.

• Data decides everything. It is the quality of the data that ultimately determines the quality of the result.

• Both of these subsets of AI are somehow connected to data, which makes it possible to represent a certain form of “intelligence.” However, you should be aware that deep learning requires much more data than a traditional machine learning algorithm. The reason for this is that deep learning networks can identify different elements in neural network layers only when more than a million data points interact. Machine learning algorithms, on the other hand, are capable of learning by pre-programmed criteria.

What is the reason for the popularity of Deep Learning in recent times? 

Now although Deep Learning has been around for many years, the major breakthroughs from these techniques came just in recent years. This is because of two main reasons:
• The increase in the amount of data generated through various sources
• The growth in hardware resources required to run these models
GPUs are multiple times faster and they help us build bigger and deeper deep learning models in comparatively less time than we required previously

What is reinforcement learning?

Reinforcement Learning allows to take actions to max cumulative reward. It learns by trial and error through reward/penalty system. Environment rewards agent so by time agent makes better decisions.
Ex: robot=agent, maze=environment. Used for complex tasks (self-driving cars, game AI).

RL is a series of time steps in a Markov Decision Process:

1. Environment: space in which RL operates
2. State: data related to past action RL took
3. Action: action taken
4. Reward: number taken by agent after last action
5. Observation: data related to environment: can be visible or partially shadowed

What are Artificial Neural Networks?

Artificial Neural networks are a specific set of algorithms that have revolutionized machine learning. They are inspired by biological neural networks. Neural Networks can adapt to changing the input, so the network generates the best possible result without needing to redesign the output criteria.

Artificial Neural Networks works on the same principle as a biological Neural Network. It consists of inputs which get processed with weighted sums and Bias, with the help of Activation Functions.

Machine Learning Artificial Neural Network

How Are Weights Initialized in a Network?

There are two methods here: we can either initialize the weights to zero or assign them randomly.

Initializing all weights to 0: This makes your model similar to a linear model. All the neurons and every layer perform the same operation, giving the same output and making the deep net useless.

Initializing all weights randomly: Here, the weights are assigned randomly by initializing them very close to 0. It gives better accuracy to the model since every neuron performs different computations. This is the most commonly used method.

What Is the Cost Function? 

Also referred to as “loss” or “error,” cost function is a measure to evaluate how good your model’s performance is. It’s used to compute the error of the output layer during backpropagation. We push that error backwards through the neural network and use that during the different training functions.
The most known one is the mean sum of squared errors.

Machine Learning Cost Function

What Are Hyperparameters?

With neural networks, you’re usually working with hyperparameters once the data is formatted correctly.
A hyperparameter is a parameter whose value is set before the learning process begins. It determines how a network is trained and the structure of the network (such as the number of hidden units, the learning rate, epochs, batches, etc.).

What Will Happen If the Learning Rate is Set inaccurately (Too Low or Too High)? 

When your learning rate is too low, training of the model will progress very slowly as we are making minimal updates to the weights. It will take many updates before reaching the minimum point.
If the learning rate is set too high, this causes undesirable divergent behavior to the loss function due to drastic updates in weights. It may fail to converge (model can give a good output) or even diverge (data is too chaotic for the network to train).

What Is The Difference Between Epoch, Batch, and Iteration in Deep Learning? 

Epoch – Represents one iteration over the entire dataset (everything put into the training model).
Batch – Refers to when we cannot pass the entire dataset into the neural network at once, so we divide the dataset into several batches.
Iteration – if we have 10,000 images as data and a batch size of 200. then an epoch should run 50 iterations (10,000 divided by 50).

What Are the Different Layers on CNN?

Reference: Layers of CNN 

Machine Learning Layers of CNN

The Convolutional neural networks are regularized versions of multilayer perceptron (MLP). They were developed based on the working of the neurons of the animal visual cortex.

The objective of using the CNN:

The idea is that you give the computer this array of numbers and it will output numbers that describe the probability of the image being a certain class (.80 for a cat, .15 for a dog, .05 for a bird, etc.). It works similar to how our brain works. When we look at a picture of a dog, we can classify it as such if the picture has identifiable features such as paws or 4 legs. In a similar way, the computer is able to perform image classification by looking for low-level features such as edges and curves and then building up to more abstract concepts through a series of convolutional layers. The computer uses low-level features obtained at the initial levels to generate high-level features such as paws or eyes to identify the object.

There are four layers in CNN:
1. Convolutional Layer – the layer that performs a convolutional operation, creating several smaller picture windows to go over the data.
2. Activation Layer (ReLU Layer) – it brings non-linearity to the network and converts all the negative pixels to zero. The output is a rectified feature map. It follows each convolutional layer.
3. Pooling Layer – pooling is a down-sampling operation that reduces the dimensionality of the feature map. Stride = how much you slide, and you get the max of the n x n matrix
4. Fully Connected Layer – this layer recognizes and classifies the objects in the image.

Q60: What Is Pooling on CNN, and How Does It Work?

Pooling is used to reduce the spatial dimensions of a CNN. It performs down-sampling operations to reduce the dimensionality and creates a pooled feature map by sliding a filter matrix over the input matrix.

What are Recurrent Neural Networks (RNNs)? 

Reference: RNNs

RNNs are a type of artificial neural networks designed to recognize the pattern from the sequence of data such as Time series, stock market and government agencies etc.

Recurrent Neural Networks (RNNs) add an interesting twist to basic neural networks. A vanilla neural network takes in a fixed size vector as input which limits its usage in situations that involve a ‘series’ type input with no predetermined size.

Machine Learning RNN

RNNs are designed to take a series of input with no predetermined limit on size. One could ask what’s\ the big deal, I can call a regular NN repeatedly too?

Machine Learning Regular NN

Sure can, but the ‘series’ part of the input means something. A single input item from the series is related to others and likely has an influence on its neighbors. Otherwise it’s just “many” inputs, not a “series” input (duh!).
Recurrent Neural Network remembers the past and its decisions are influenced by what it has learnt from the past. Note: Basic feed forward networks “remember” things too, but they remember things they learnt during training. For example, an image classifier learns what a “1” looks like during training and then uses that knowledge to classify things in production.
While RNNs learn similarly while training, in addition, they remember things learnt from prior input(s) while generating output(s). RNNs can take one or more input vectors and produce one or more output vectors and the output(s) are influenced not just by weights applied on inputs like a regular NN, but also by a “hidden” state vector representing the context based on prior input(s)/output(s). So, the same input could produce a different output depending on previous inputs in the series.

Machine Learning Vanilla NN

In summary, in a vanilla neural network, a fixed size input vector is transformed into a fixed size output vector. Such a network becomes “recurrent” when you repeatedly apply the transformations to a series of given input and produce a series of output vectors. There is no pre-set limitation to the size of the vector. And, in addition to generating the output which is a function of the input and hidden state, we update the hidden state itself based on the input and use it in processing the next input.

What is the role of the Activation Function?

The Activation function is used to introduce non-linearity into the neural network helping it to learn more complex function. Without which the neural network would be only able to learn linear function which is a linear combination of its input data. An activation function is a function in an artificial neuron that delivers an output based on inputs.

Machine Learning libraries for various purposes

Machine Learning Libraries

What is an Auto-Encoder?

Reference: Auto-Encoder

Auto-encoders are simple learning networks that aim to transform inputs into outputs with the minimum possible error. This means that we want the output to be as close to input as possible. We add a couple of layers between the input and the output, and the sizes of these layers are smaller than the input layer. The auto-encoder receives unlabeled input which is then encoded to reconstruct the input. 

An autoencoder is a type of artificial neural network used to learn efficient data coding in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore signal “noise”. Along with the reduction side, a reconstructing side is learnt, where the autoencoder tries to generate from the reduced encoding a representation as close as possible to its original input, hence its name. Several variants exist to the basic model, with the aim of forcing the learned representations of the input to assume useful properties.
Autoencoders are effectively used for solving many applied problems, from face recognition to acquiring the semantic meaning of words.

Machine Learning Auto_Encoder

What is a Boltzmann Machine?

Boltzmann machines have a simple learning algorithm that allows them to discover interesting features that represent complex regularities in the training data. The Boltzmann machine is basically used to optimize the weights and the quantity for the given problem. The learning algorithm is very slow in networks with many layers of feature detectors. “Restricted Boltzmann Machines” algorithm has a single layer of feature detectors which makes it faster than the rest.

Machine Learning Boltzmann Machine

What Is Dropout and Batch Normalization?

Dropout is a technique of dropping out hidden and visible nodes of a network randomly to prevent overfitting of data (typically dropping 20 per cent of the nodes). It doubles the number of iterations needed to converge the network. It used to avoid overfitting, as it increases the capacity of generalization.

Batch normalization is the technique to improve the performance and stability of neural networks by normalizing the inputs in every layer so that they have mean output activation of zero and standard deviation of one

Why Is TensorFlow the Most Preferred Library in Deep Learning?

TensorFlow provides both C++ and Python APIs, making it easier to work on and has a faster compilation time compared to other Deep Learning libraries like Keras and PyTorch. TensorFlow supports both CPU and GPU computing devices.

What is Tensor in TensorFlow?

A tensor is a mathematical object represented as arrays of higher dimensions. Think of a n-D matrix. These arrays of data with different dimensions and ranks fed as input to the neural network are called “Tensors.”

What is the Computational Graph?

Everything in a TensorFlow is based on creating a computational graph. It has a network of nodes where each node operates. Nodes represent mathematical operations, and edges represent tensors. Since data flows in the form of a graph, it is also called a “DataFlow Graph.”

What is logistic regression?

• Logistic Regression models a function of the target variable as a linear combination of the predictors, then converts this function into a fitted value in the desired range.

• Binary or Binomial Logistic Regression can be understood as the type of Logistic Regression that deals with scenarios wherein the observed outcomes for dependent variables can be only in binary, i.e., it can have only two possible types.

• Multinomial Logistic Regression works in scenarios where the outcome can have more than two possible types – type A vs type B vs type C – that are not in any particular order.

No alternative text description for this image

No alternative text description for this image

Credit:

How is logistic regression done? 

Logistic regression measures the relationship between the dependent variable (our label of what we want to predict) and one or more independent variables (our features) by estimating probability using its underlying logistic function (sigmoid).

Explain the steps in making a decision tree. 

1. Take the entire data set as input
2. Calculate entropy of the target variable, as well as the predictor attributes
3. Calculate your information gain of all attributes (we gain information on sorting different objects from each other)
4. Choose the attribute with the highest information gain as the root node
5. Repeat the same procedure on every branch until the decision node of each branch is finalized
For example, let’s say you want to build a decision tree to decide whether you should accept or decline a job offer. The decision tree for this case is as shown:

Machine Learning Decision Tree

It is clear from the decision tree that an offer is accepted if:
• Salary is greater than $50,000
• The commute is less than an hour
• Coffee is offered

How do you build a random forest model?

A random forest is built up of a number of decision trees. If you split the data into different packages and make a decision tree in each of the different groups of data, the random forest brings all those trees together.

Steps to build a random forest model:

1. Randomly select ; features from a total of = features where  k<< m
2. Among the ; features, calculate the node D using the best split point
3. Split the node into daughter nodes using the best split
4. Repeat steps two and three until leaf nodes are finalized
5. Build forest by repeating steps one to four for > times to create > number of trees

Differentiate between univariate, bivariate, and multivariate analysis. 

Univariate data contains only one variable. The purpose of the univariate analysis is to describe the data and find patterns that exist within it.

Machine Learning Univariate Data

The patterns can be studied by drawing conclusions using mean, median, mode, dispersion or range, minimum, maximum, etc.

Bivariate data involves two different variables. The analysis of this type of data deals with causes and relationships and the analysis is done to determine the relationship between the two variables.

Bivariate data

Here, the relationship is visible from the table that temperature and sales are directly proportional to each other. The hotter the temperature, the better the sales.

Multivariate data involves three or more variables, it is categorized under multivariate. It is similar to a bivariate but contains more than one dependent variable.

Example: data for house price prediction
The patterns can be studied by drawing conclusions using mean, median, and mode, dispersion or range, minimum, maximum, etc. You can start describing the data and using it to guess what the price of the house will be.

What are the feature selection methods used to select the right variables?

There are two main methods for feature selection.
Filter Methods
This involves:
• Linear discrimination analysis
• ANOVA
• Chi-Square
The best analogy for selecting features is “bad data in, bad answer out.” When we’re limiting or selecting the features, it’s all about cleaning up the data coming in.

Wrapper Methods
This involves:
• Forward Selection: We test one feature at a time and keep adding them until we get a good fit
• Backward Selection: We test all the features and start removing them to see what works
better
• Recursive Feature Elimination: Recursively looks through all the different features and how they pair together

Wrapper methods are very labor-intensive, and high-end computers are needed if a lot of data analysis is performed with the wrapper method.

You are given a data set consisting of variables with more than 30 percent missing values. How will you deal with them? 

If the data set is large, we can just simply remove the rows with missing data values. It is the quickest way; we use the rest of the data to predict the values.

For smaller data sets, we can impute missing values with the mean, median, or average of the rest of the data using pandas data frame in python. There are different ways to do so, such as: df.mean(), df.fillna(mean)

Other option of imputation is using KNN for numeric or classification values (as KNN just uses k closest values to impute the missing value).

How will you calculate the Euclidean distance in Python?

plot1 = [1,3]

plot2 = [2,5]

The Euclidean distance can be calculated as follows:

euclidean_distance = sqrt((plot1[0]-plot2[0])**2 + (plot1[1]- plot2[1])**2)

What are dimensionality reduction and its benefits? 

Dimensionality reduction refers to the process of converting a data set with vast dimensions into data with fewer dimensions (fields) to convey similar information concisely.

This reduction helps in compressing data and reducing storage space. It also reduces computation time as fewer dimensions lead to less computing. It removes redundant features; for example, there’s no point in storing a value in two different units (meters and inches).

How should you maintain a deployed model?

The steps to maintain a deployed model are (CREM):

1. Monitor: constant monitoring of all models is needed to determine their performance accuracy.
When you change something, you want to figure out how your changes are going to affect things.
This needs to be monitored to ensure it’s doing what it’s supposed to do.
2. Evaluate: evaluation metrics of the current model are calculated to determine if a new algorithm is needed.
3. Compare: the new models are compared to each other to determine which model performs the best.
4. Rebuild: the best performing model is re-built on the current state of data.

How can a time-series data be declared as stationery?

  1. The mean of the series should not be a function of time.
Machine Learning Stationery Time Series Data: Mean
  1. The variance of the series should not be a function of time. This property is known as homoscedasticity.
Machine Learning Stationery Time Series Data: Variance
  1. The covariance of the i th term and the (i+m) th term should not be a function of time.
Machine Learning Stationery Time Series Data: CoVariance

‘People who bought this also bought…’ recommendations seen on Amazon are a result of which algorithm?

The recommendation engine is accomplished with collaborative filtering. Collaborative filtering explains the behavior of other users and their purchase history in terms of ratings, selection, etc.
The engine makes predictions on what might interest a person based on the preferences of other users. In this algorithm, item features are unknown.
For example, a sales page shows that a certain number of people buy a new phone and also buy tempered glass at the same time. Next time, when a person buys a phone, he or she may see a recommendation to buy tempered glass as well.

What is a Generative Adversarial Network?

Suppose there is a wine shop purchasing wine from dealers, which they resell later. But some dealers sell fake wine. In this case, the shop owner should be able to distinguish between fake and authentic wine. The forger will try different techniques to sell fake wine and make sure specific techniques go past the shop owner’s check. The shop owner would probably get some feedback from wine experts that some of the wine is not original. The owner would have to improve how he determines whether a wine is fake or authentic.
The forger’s goal is to create wines that are indistinguishable from the authentic ones while the shop owner intends to tell if the wine is real or not accurately.

Machine Learning GAN illustration

• There is a noise vector coming into the forger who is generating fake wine.
• Here the forger acts as a Generator.
• The shop owner acts as a Discriminator.
• The Discriminator gets two inputs; one is the fake wine, while the other is the real authentic wine.
The shop owner has to figure out whether it is real or fake.

So, there are two primary components of Generative Adversarial Network (GAN) named:
1. Generator
2. Discriminator

The generator is a CNN that keeps keys producing images and is closer in appearance to the real images while the discriminator tries to determine the difference between real and fake images. The ultimate aim is to make the discriminator learn to identify real and fake images.

You are given a dataset on cancer detection. You have built a classification model and achieved an accuracy of 96 percent. Why shouldn’t you be happy with your model performance? What can you do about it?

Cancer detection results in imbalanced data. In an imbalanced dataset, accuracy should not be based as a measure of performance. It is important to focus on the remaining four percent, which represents the patients who were wrongly diagnosed. Early diagnosis is crucial when it comes to cancer detection and can greatly improve a patient’s prognosis.

Hence, to evaluate model performance, we should use Sensitivity (True Positive Rate), Specificity (True Negative Rate), F measure to determine the class wise performance of the classifier.

We want to predict the probability of death from heart disease based on three risk factors: age, gender, and blood cholesterol level. What is the most appropriate algorithm for this case?

The most appropriate algorithm for this case is logistic regression.

After studying the behavior of a population, you have identified four specific individual types that are valuable to your study. You would like to find all users who are most similar to each individual type. Which algorithm is most appropriate for this study? 

As we are looking for grouping people together specifically by four different similarities, it indicates the value of k. Therefore, K-means clustering is the most appropriate algorithm for this study.

You have run the association rules algorithm on your dataset, and the two rules {banana, apple} => {grape} and {apple, orange} => {grape} have been found to be relevant. What else must be true? 

{grape, apple} must be a frequent itemset.

Your organization has a website where visitors randomly receive one of two coupons. It is also possible that visitors to the website will not receive a coupon. You have been asked to determine if offering a coupon to website visitors has any impact on their purchase decisions. Which analysis method should you use?

One-way ANOVA: in statistics, one-way analysis of variance is a technique that can be used to compare means of two or more samples. This technique can be used only for numerical response data, the “Y”, usually one variable, and numerical or categorical input data, the “X”, always one variable, hence “oneway”.
The ANOVA tests the null hypothesis, which states that samples in all groups are drawn from populations with the same mean values. To do this, two estimates are made of the population variance. The ANOVA produces an F-statistic, the ratio of the variance calculated among the means to the variance within the samples. If the group means are drawn from populations with the same mean values, the variance between the group means should be lower than the variance of the samples, following the central limit
theorem. A higher ratio therefore implies that the samples were drawn from populations with different mean values.

What are the feature vectors?

A feature vector is an n-dimensional vector of numerical features that represent an object. In machine learning, feature vectors are used to represent numeric or symbolic characteristics (called features) of an object in a mathematical way that’s easy to analyze.

What is root cause analysis?

Root cause analysis was initially developed to analyze industrial accidents but is now widely used in other areas. It is a problem-solving technique used for isolating the root causes of faults or problems. A factor is called a root cause if its deduction from the problem-fault-sequence averts the final undesirable event from recurring.

Do gradient descent methods always converge to similar points?

They do not, because in some cases, they reach a local minimum or a local optimum point. You would not reach the global optimum point. This is governed by the data and the starting conditions.

 In your choice of language, write a program that prints the numbers ranging from one to 50. But for multiples of three, print “Fizz” instead of the number and for the multiples of five, print “Buzz.” For numbers which are multiples of both three and five, print “FizzBuzz.”

Python Fibonacci algorithm

What are the different Deep Learning Frameworks?

PyTorch: PyTorch is an open source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook’s AI Research lab. It is free and open-source software released under the Modified BSD license.
TensorFlow: TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library and is also used for machine learning applications such as neural networks. Licensed by Apache License 2.0. Developed by Google Brain Team.
Microsoft Cognitive Toolkit: Microsoft Cognitive Toolkit describes neural networks as a series of computational steps via a directed graph.
Keras: Keras is an open-source neural-network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible. Licensed by MIT.

Data Sciences and Data Mining Glossary

Credit: Dr. Matthew North
Antecedent: In an association rules data mining model, the antecedent is the attribute which precedes the consequent in an identified rule. Attribute order makes a difference when calculating the confidence percentage, so identifying which attribute comes first is necessary even if the reciprocal of the association is also a rule.

Archived Data: Data which have been copied out of a live production database and into a data warehouse or other permanent system where they can be accessed and analyzed, but not by primary operational business systems.

Association Rules: A data mining methodology which compares attributes in a data set across all observations to identify areas where two or more attributes are frequently found together. If their frequency of coexistence is high enough throughout the data set, the association of those attributes can be said to be a rule.

Attribute: In columnar data, an attribute is one column. It is named in the data so that it can be referred to by a model and used in data mining. The term attribute is sometimes interchanged with the terms ‘field’, ‘variable’, or ‘column’.

Average: The arithmetic mean, calculated by summing all values and dividing by the count of the values.

Binomial: A data type for any set of values that is limited to one of two numeric options.

Binominal: In RapidMiner, the data type binominal is used instead of binomial, enabling both numerical and character-based sets of values that are limited to one of two options.

Business Understanding: See Organizational Understanding: The first step in the CRISP-DM process, usually referred to as Business Understanding, where the data miner develops an understanding of an organization’s goals, objectives, questions, and anticipated outcomes relative to data mining tasks. The data miner must understand why the data mining task is being undertaken before proceeding to gather and understand data.

Case Sensitive: A situation where a computer program recognizes the uppercase version of a letter or word as being different from the lowercase version of the same letter or word.

Classification: One of the two main goals of conducting data mining activities, with the other being prediction. Classification creates groupings in a data set based on the similarity of the observations’ attributes. Some data mining methodologies, such as decision trees, can predict an observation’s classification.

Code: Code is the result of a computer worker’s work. It is a set of instructions, typed in a specific grammar and syntax, that a computer can understand and execute. According to Lawrence Lessig, it is one of four methods humans can use to set and control boundaries for behavior when interacting with computer systems.

Coefficient: In data mining, a coefficient is a value that is calculated based on the values in a data set that can be used as a multiplier or as an indicator of the relative strength of some attribute or component in a data mining model.

Column: See Attribute. In columnar data, an attribute is one column. It is named in the data so that it can be referred to by a model and used in data mining. The term attribute is sometimes interchanged with the terms ‘field’, ‘variable’, or ‘column’.

Comma Separated Values (CSV): A common text-based format for data sets where the divisions between attributes (columns of data) are indicated by commas. If commas occur naturally in some of the values in the data set, a CSV file will misunderstand these to be attribute separators, leading to misalignment of attributes.

Conclusion: See Consequent: In an association rules data mining model, the consequent is the attribute which results from the antecedent in an identified rule. If an association rule were characterized as “If this, then that”, the consequent would be that—in other words, the outcome.

Confidence (Alpha) Level: A value, usually 5% or 0.05, used to test for statistical significance in some data mining methods. If statistical significance is found, a data miner can say that there is a 95% likelihood that a calculated or predicted value is not a false positive.

Confidence Percent: In predictive data mining, this is the percent of calculated confidence that the model has calculated for one or more possible predicted values. It is a measure for the likelihood of false positives in predictions. Regardless of the number of possible predicted values, their collective confidence percentages will always total to 100%.

Consequent: In an association rules data mining model, the consequent is the attribute which results from the antecedent in an identified rule. If an association rule were characterized as “If this, then that”, the consequent would be that—in other words, the outcome.

Correlation: A statistical measure of the strength of affinity, based on the similarity of observational values, of the attributes in a data set. These can be positive (as one attribute’s values go up or down, so too does the correlated attribute’s values); or negative (correlated attributes’ values move in opposite directions). Correlations are indicated by coefficients which fall on a scale between -1 (complete negative correlation) and 1 (complete positive correlation), with 0 indicating no correlation at all between two attributes.

CRISP-DM: An acronym for Cross-Industry Standard Process for Data Mining. This process was jointly developed by several major multi-national corporations around the turn of the new millennium in order to standardize the approach to mining data. It is comprised of six cyclical steps: Business (Organizational) Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, Deployment.

Cross-validation: A method of statistically evaluating a training data set for its likelihood of producing false positives in a predictive data mining model.

Data: Data are any arrangement and compilation of facts. Data may be structured (e.g. arranged in columns (attributes) and rows (observations)), or unstructured (e.g. paragraphs of text, computer log file).

Data Analysis: The process of examining data in a repeatable and structured way in order to extract meaning, patterns or messages from a set of data.

Data Mart: A location where data are stored for easy access by a broad range of people in an organization. Data in a data mart are generally archived data, enabling analysis in a setting that does not impact live operations.

Data Mining: A computational process of analyzing data sets, usually large in nature, using both statistical and logical methods, in order to uncover hidden, previously unknown, and interesting patterns that can inform organizational decision making.

Data Preparation: The third in the six steps of CRISP-DM. At this stage, the data miner ensures that the data to be mined are clean and ready for mining. This may include handling outliers or other inconsistent data, dealing with missing values, reducing attributes or observations, setting attribute roles for modeling, etc.

Data Set: Any compilation of data that is suitable for analysis.

Data Type: In a data set, each attribute is assigned a data type based on the kind of data stored in the attribute. There are many data types which can be generalized into one of three areas: Character (Text) based; Numeric; and Date/Time. Within these categories, RapidMiner has several data types. For example, in the Character area, RapidMiner has Polynominal, Binominal, etc.; and in the Numeric area it has Real, Integer, etc.

Data Understanding: The second in the six steps of CRISP-DM. At this stage, the data miner seeks out sources of data in the organization, and works to collect, compile, standardize, define and document the data. The data miner develops a comprehension of where the data have come from, how they were collected and what they mean.

Data Warehouse: A large-scale repository for archived data which are available for analysis. Data in a data warehouse are often stored in multiple formats (e.g. by week, month, quarter and year), facilitating large scale analyses at higher speeds. The data warehouse is populated by extracting data from operational systems so that analyses do not interfere with live business operations.

Database: A structured organization of facts that is organized such that the facts can be reliably and repeatedly accessed. The most common type of database is a relational database, in which facts (data) are arranged in tables of columns and rows. The data are then accessed using a query language, usually SQL (Structured Query Language), in order to extract meaning from the tables.

Decision Tree: A data mining methodology where leaves and nodes are generated to construct a predictive tree, whereby a data miner can see the attributes which are most predictive of each possible outcome in a target (label) attribute.

Denormalization: The process of removing relational organization from data, reintroducing redundancy into the data, but simultaneously eliminating the need for joins in a relational database, enabling faster querying.

Dependent Variable (Attribute): The attribute in a data set that is being acted upon by the other attributes. It is the thing we want to predict, the target, or label, attribute in a predictive model.

Deployment: The sixth and final of the six steps of CRISP-DM. At this stage, the data miner takes the results of data mining activities and puts them into practice in the organization. The data miner watches closely and collects data to determine if the deployment is successful and ethical. Deployment can happen in stages, such as through pilot programs before a full-scale roll out.

Descartes’ Rule of Change: An ethical framework set forth by Rene Descartes which states that if an action cannot be taken repeatedly, it cannot be ethically taken even once.

Design Perspective: The view in RapidMiner where a data miner adds operators to a data mining stream, sets those operators’ parameters, and runs the model.

Discriminant Analysis: A predictive data mining model which attempts to compare the values of all observations across all attributes and identify where natural breaks occur from one category to another, and then predict which category each observation in the data set will fall into.

Ethics: A set of moral codes or guidelines that an individual develops to guide his or her decision making in order to make fair and respectful decisions and engage in right actions. Ethical standards are higher than legally required minimums.

Evaluation: The fifth of the six steps of CRISP-DM. At this stage, the data miner reviews the results of the data mining model, interprets results and determines how useful they are. He or she may also conduct an investigation into false positives or other potentially misleading results.

False Positive: A predicted value that ends up not being correct.

Field: See Attribute: In columnar data, an attribute is one column. It is named in the data so that it can be referred to by a model and used in data mining. The term attribute is sometimes interchanged with the terms ‘field’, ‘variable’, or ‘column’.

Frequency Pattern: A recurrence of the same, or similar, observations numerous times in a single data set.

Fuzzy Logic: A data mining concept often associated with neural networks where predictions are made using a training data set, even though some uncertainty exists regarding the data and a model’s predictions.

Gain Ratio: One of several algorithms used to construct decision tree models.

Gini Index: An algorithm created by Corrodo Gini that can be used to generate decision tree models.

Heterogeneity: In statistical analysis, this is the amount of variety found in the values of an attribute.

Inconsistent Data: These are values in an attribute in a data set that are out-of-the-ordinary among the whole set of values in that attribute. They can be statistical outliers, or other values that simply don’t make sense in the context of the ‘normal’ range of values for the attribute. They are generally replaced or remove during the Data Preparation phase of CRISP-DM.

Independent Variable (Attribute): These are attributes that act on the dependent attribute (the target, or label). They are used to help predict the label in a predictive model.

Jittering: The process of adding a small, random decimal to discrete values in a data set so that when they are plotted in a scatter plot, they are slightly apart from one another, enabling the analyst to better see clustering and density.

Join: The process of connecting two or more tables in a relational database together so that their attributes can be accessed in a single query, such as in a view.

Kant’s Categorical Imperative: An ethical framework proposed by Immanuel Kant which states that if everyone cannot ethically take some action, then no one can ethically take that action.

k-Means Clustering: A data mining methodology that uses the mean (average) values of the attributes in a data set to group each observation into a cluster of other observations whose values are most similar to the mean for that cluster.

Label: In RapidMiner, this is the role that must be set in order to use an attribute as the dependent, or target, attribute in a predictive model.

Laws: These are regulatory statutes which have associated consequences that are established and enforced by a governmental agency. According to Lawrence Lessig, these are one of the four methods for establishing boundaries to define and regulate social behavior.

Leaf: In a decision tree data mining model, this is the terminal end point of a branch, indicating the predicted outcome for observations whose values follow that branch of the tree.

Linear Regression: A predictive data mining method which uses the algebraic formula for calculating the slope of a line in order to predict where a given observation will likely fall along that line.

Logistic Regression: A predictive data mining method which uses a quadratic formula to predict one of a set of possible outcomes, along with a probability that the prediction will be the actual outcome.

Markets: A socio-economic construct in which peoples’ buying, selling, and exchanging behaviors define the boundaries of acceptable or unacceptable behavior. Lawrence Lessig offers this as one of four methods for defining the parameters of appropriate behavior.

Mean: See Average: The arithmetic mean, calculated by summing all values and dividing by the count of the values. 

Median: With the Mean and Mode, this is one of three generally used Measures of Central Tendency. It is an arithmetic way of defining what ‘normal’ looks like in a numeric attribute. It is calculated by rank ordering the values in an attribute and finding the one in the middle. If there are an even number of observations, the two in the middle are averaged to find the median.

Meta Data: These are facts that describe the observational values in an attribute. Meta data may include who collected the data, when, why, where, how, how often; and usually include some descriptive statistics such as the range, average, standard deviation, etc.

Missing Data: These are instances in an observation where one or more attributes does not have a value. It is not the same as zero, because zero is a value. Missing data are like Null values in a database, they are either unknown or undefined. These are usually replaced or removed during the Data Preparation phase of CRISP-DM.

Mode: With Mean and Median, this is one of three common Measures of Central Tendency. It is the value in an attribute which is the most common. It can be numerical or text. If an attribute contains two or more values that appear an equal number of times and more than any other values, then all are listed as the mode, and the attribute is said to be Bimodal or Multimodal.

Model: A computer-based representation of real-life events or activities, constructed upon the basis of data which represent those events.

Name (Attribute): This is the text descriptor of each attribute in a data set. In RapidMiner, the first row of an imported data set should be designated as the attribute name, so that these are not interpreted as the first observation in the data set.

Neural Network: A predictive data mining methodology which tries to mimic human brain processes by comparing the values of all attributes in a data set to one another through the use of a hidden layer of nodes. The frequencies with which the attribute values match, or are strongly similar, create neurons which become stronger at higher frequencies of similarity.

n-Gram: In text mining, this is a combination of words or word stems that represent a phrase that may have more meaning or significance that would the single word or stem.

Node: A terminal or mid-point in decision trees and neural networks where an attribute branches or forks away from other terminal or branches because the values represented at that point have become significantly different from all other values for that attribute.

Normalization: In a relational database, this is the process of breaking data out into multiple related tables in order to reduce redundancy and eliminate multivalued dependencies.

Null: The absence of a value in a database. The value is unrecorded, unknown, or undefined. See Missing Values.

Observation: A row of data in a data set. It consists of the value assigned to each attribute for one record in the data set. It is sometimes called a tuple in database language.

Online Analytical Processing (OLAP): A database concept where data are collected and organized in a way that facilitates analysis, rather than practical, daily operational work. Evaluating data in a data warehouse is an example of OLAP. The underlying structure that collects and holds the data makes analysis faster, but would slow down transactional work.

Online Transaction Processing (OLTP): A database concept where data are collected and organized in a way that facilitates fast and repeated transactions, rather than broader analytical work. Scanning items being purchased at a cash register is an example of OLTP. The underlying structure that collects and holds the data makes transactions faster, but would slow down analysis.

Operational Data: Data which are generated as a result of day-to-day work (e.g. the entry of work orders for an electrical service company).

Operator: In RapidMiner, an operator is any one of more than 100 tools that can be added to a data mining stream in order to perform some function. Functions range from adding a data set, to setting an attribute’s role, to applying a modeling algorithm. Operators are connected into a stream by way of ports connected by splines.

Organizational Data: These are data which are collected by an organization, often in aggregate or summary format, in order to address a specific question, tell a story, or answer a specific question. They may be constructed from Operational Data, or added to through other means such as surveys, questionnaires or tests.

Organizational Understanding: The first step in the CRISP-DM process, usually referred to as Business Understanding, where the data miner develops an understanding of an organization’s goals, objectives, questions, and anticipated outcomes relative to data mining tasks. The data miner must understand why the data mining task is being undertaken before proceeding to gather and understand data.

Parameters: In RapidMiner, these are the settings that control values and thresholds that an operator will use to perform its job. These may be the attribute name and role in a Set Role operator, or the algorithm the data miner desires to use in a model operator.

Port: The input or output required for an operator to perform its function in RapidMiner. These are connected to one another using splines.

Prediction: The target, or label, or dependent attribute that is generated by a predictive model, usually for a scoring data set in a model.

Premise: See Antecedent: In an association rules data mining model, the antecedent is the attribute which precedes the consequent in an identified rule. Attribute order makes a difference when calculating the confidence percentage, so identifying which attribute comes first is necessary even if the reciprocal of the association is also a rule.

Privacy: The concept describing a person’s right to be let alone; to have information about them kept away from those who should not, or do not need to, see it. A data miner must always respect and safeguard the privacy of individuals represented in the data he or she mines.

Professional Code of Conduct: A helpful guide or documented set of parameters by which an individual in a given profession agrees to abide. These are usually written by a board or panel of experts and adopted formally by a professional organization.

Query: A method of structuring a question, usually using code, that can be submitted to, interpreted, and answered by a computer.

Record: See Observation: A row of data in a data set. It consists of the value assigned to each attribute for one record in the data set. It is sometimes called a tuple in database language.

Relational Database: A computerized repository, comprised of entities that relate to one another through keys. The most basic and elemental entity in a relational database is the table, and tables are made up of attributes. One or more of these attributes serves as a key that can be matched (or related) to a corresponding attribute in another table, creating the relational effect which reduces data redundancy and eliminates multivalued dependencies.

Repository: In RapidMiner, this is the place where imported data sets are stored so that they are accessible for modeling.

Results Perspective: The view in RapidMiner that is seen when a model has been run. It is usually comprised of two or more tabs which show meta data, data in a spreadsheet-like view, and predictions and model outcomes (including graphical representations where applicable).

Role (Attribute): In a data mining model, each attribute must be assigned a role. The role is the part the attribute plays in the model. It is usually equated to serving as an independent variable (regular), or dependent variable (label).

Row: See Observation: A row of data in a data set. It consists of the value assigned to each attribute for one record in the data set. It is sometimes called a tuple in database language.

Sample: A subset of an entire data set, selected randomly or in a structured way. This usually reduces a data set down, allowing models to be run faster, especially during development and proof-of-concept work on a model.

Scoring Data: A data set with the same attributes as a training data set in a predictive model, with the exception of the label. The training data set, with the label defined, is used to create a predictive model, and that model is then applied to a scoring data set possessing the same attributes in order to predict the label for each scoring observation.

Social Norms: These are the sets of behaviors and actions that are generally tolerated and found to be acceptable in a society. According to Lawrence Lessig, these are one of four methods of defining and regulating appropriate behavior.

Spline: In RapidMiner, these lines connect the ports between operators, creating the stream of a data mining model.

Standard Deviation: One of the most common statistical measures of how dispersed the values in an attribute are. This measure can help determine whether or not there are outliers (a common type of inconsistent data) in a data set.

Standard Operating Procedures: These are organizational guidelines that are documented and shared with employees which help to define the boundaries for appropriate and acceptable behavior in the business setting. They are usually created and formally adopted by a group of leaders in the organization, with input from key stakeholders in the organization.

Statistical Significance: In statistically-based data mining activities, this is the measure of whether or not the model has yielded any results that are mathematically reliable enough to be used. Any model lacking statistical significance should not be used in operational decision making.

Stemming: In text mining, this is the process of reducing like-terms down into a single, common token (e.g. country, countries, country’s, countryman, etc. → countr).

Stopwords: In text mining, these are small words that are necessary for grammatical correctness, but which carry little meaning or power in the message of the text being mined. These are often articles, prepositions or conjunctions, such as ‘a’, ‘the’, ‘and’, etc., and are usually removed in the Process Document operator’s sub-process.

Stream: This is the string of operators in a data mining model, connected through the operators’ ports via splines, that represents all actions that will be taken on a data set in order to mine it.

Structured Query Language (SQL): The set of codes, reserved keywords and syntax defined by the American National Standards Institute used to create, manage and use relational databases.

Sub-process: In RapidMiner, this is a stream of operators set up to apply a series of actions to all inputs connected to the parent operator.

Support Percent: In an association rule data mining model, this is the percent of the time that when the antecedent is found in an observation, the consequent is also found. Since this is calculated as the number of times the two are found together divided by the total number of they could have been found together, the Support Percent is the same for reciprocal rules.

Table: In data collection, a table is a grid of columns and rows, where in general, the columns are individual attributes in the data set, and the rows are observations across those attributes. Tables are the most elemental entity in relational databases.

Target Attribute: See Label; Dependent Variable: The attribute in a data set that is being acted upon by the other attributes. It is the thing we want to predict, the target, or label, attribute in a predictive model.

Technology: Any tool or process invented by mankind to do or improve work.

Text Mining: The process of data mining unstructured text-based data such as essays, news articles, speech transcripts, etc. to discover patterns of word or phrase usage to reveal deeper or previously unrecognized meaning.

Token (Tokenize): In text mining, this is the process of turning words in the input document(s) into attributes that can be mined.

Training Data: In a predictive model, this data set already has the label, or dependent variable defined, so that it can be used to create a model which can be applied to a scoring data set in order to generate predictions for the latter.

Tuple: See Observation: A row of data in a data set. It consists of the value assigned to each attribute for one record in the data set. It is sometimes called a tuple in database language.

Variable: See Attribute: In columnar data, an attribute is one column. It is named in the data so that it can be referred to by a model and used in data mining. The term attribute is sometimes interchanged with the terms ‘field’, ‘variable’, or ‘column’.

View: A type of pseudo-table in a relational database which is actually a named, stored query. This query runs against one or more tables, retrieving a defined number of attributes that can then be referenced as if they were in a table in the database. Views can limit users’ ability to see attributes to only those that are relevant and/or approved for those users to see. They can also speed up the query process because although they may contain joins, the key columns for the joins can be indexed and cached, making the view’s query run faster than it would if it were not stored as a view. Views can be useful in data mining as data miners can be given read-only access to the view, upon which they can build data mining models, without having to have broader administrative rights on the database itself.

What is the Central Limit Theorem and why is it important?

An Introduction to the Central Limit Theorem

Answer: Suppose that we are interested in estimating the average height among all people. Collecting data for every person in the world is impractical, bordering on impossible. While we can’t obtain a height measurement from everyone in the population, we can still sample some people. The question now becomes, what can we say about the average height of the entire population given a single sample.
The Central Limit Theorem addresses this question exactly. Formally, it states that if we sample from a population using a sufficiently large sample size, the mean of the samples (also known as the sample population) will be normally distributed (assuming true random sampling), the mean tending to the mean of the population and variance equal to the variance of the population divided by the size of the sampling.
What’s especially important is that this will be true regardless of the distribution of the original population.

Central Limit Theorem
Central Limit Theorem: Population Distribution

As we can see, the distribution is pretty ugly. It certainly isn’t normal, uniform, or any other commonly known distribution. In order to sample from the above distribution, we need to define a sample size, referred to as N. This is the number of observations that we will sample at a time. Suppose that we choose
N to be 3. This means that we will sample in groups of 3. So for the above population, we might sample groups such as [5, 20, 41], [60, 17, 82], [8, 13, 61], and so on.
Suppose that we gather 1,000 samples of 3 from the above population. For each sample, we can compute its average. If we do that, we will have 1,000 averages. This set of 1,000 averages is called a sampling distribution, and according to Central Limit Theorem, the sampling distribution will approach a normal distribution as the sample size N used to produce it increases. Here is what our sample distribution looks like for N = 3.

Simple Mean Distribution with N=3
Simple Mean Distribution with N=3

As we can see, it certainly looks uni-modal, though not necessarily normal. If we repeat the same process with a larger sample size, we should see the sampling distribution start to become more normal. Let’s repeat the same process again with N = 10. Here is the sampling distribution for that sample size.

Sample Mean Distribution with N = 10
Sample Mean Distribution with N = 10

Credit: Steve Nouri

What is bias-variance trade-off?

Bias: Bias is an error introduced in the model due to the oversimplification of the algorithm used (does not fit the data properly). It can lead to under-fitting.
Low bias machine learning algorithms — Decision Trees, k-NN and SVM
High bias machine learning algorithms — Linear Regression, Logistic Regression

Variance: Variance is error introduced in the model due to a too complex algorithm, it performs very well in the training set but poorly in the test set. It can lead to high sensitivity and overfitting.
Possible high variance – polynomial regression

Normally, as you increase the complexity of your model, you will see a reduction in error due to lower bias in the model. However, this only happens until a particular point. As you continue to make your model more complex, you end up over-fitting your model and hence your model will start suffering from high variance.

bias-variance trade-off

Bias-Variance trade-off: The goal of any supervised machine learning algorithm is to have low bias and low variance to achieve good prediction performance.

1. The k-nearest neighbor algorithm has low bias and high variance, but the trade-off can be changed by increasing the value of k which increases the number of neighbors that contribute to the prediction and in turn increases the bias of the model.
2. The support vector machine algorithm has low bias and high variance, but the trade-off can be changed by increasing the C parameter that influences the number of violations of the margin allowed in the training data which increases the bias but decreases the variance.
3. The decision tree has low bias and high variance, you can decrease the depth of the tree or use fewer attributes.
4. The linear regression has low variance and high bias, you can increase the number of features or use another regression that better fits the data.

There is no escaping the relationship between bias and variance in machine learning. Increasing the bias will decrease the variance. Increasing the variance will decrease bias.

The Best Medium-Hard Data Analyst SQL Interview Questions

compiled by Google Data Analyst Zachary Thomas!

The Best Medium-Hard Data Analyst SQL Interview Questions

Self-Join Practice Problems: MoM Percent Change

Context: Oftentimes it’s useful to know how much a key metric, such as monthly active users, changes between months.
Say we have a table logins in the form:

SQL Self-Join Practice Mom Percent Change

Task: Find the month-over-month percentage change for monthly active users (MAU).

Solution:
(This solution, like other solution code blocks you will see in this doc, contains comments about SQL syntax that may differ between flavors of SQL or other comments about the solutions as listed)

SQL MoM Solution2

Tree Structure Labeling with SQL

Context: Say you have a table tree with a column of nodes and a column corresponding parent nodes

Task: Write SQL such that we label each node as a “leaf”, “inner” or “Root” node, such that for the nodes above we get:

A solution which works for the above example will receive full credit, although you can receive extra credit for providing a solution that is generalizable to a tree of any depth (not just depth = 2, as is the case in the example above).

Solution: This solution works for the example above with tree depth = 2, but is not generalizable beyond that.

An alternate solution, that is generalizable to any tree depth:
Acknowledgement: this more generalizable solution was contributed by Fabian Hofmann

An alternate solution, without explicit joins:
Acknowledgement: William Chargin on 5/2/20 noted that WHERE parent IS NOT NULL is needed to make this solution return Leaf instead of NULL.

Retained Users Per Month with SQL

Acknowledgement: this problem is adapted from SiSense’s “Using Self Joins to Calculate Your Retention, Churn, and Reactivation Metrics” blog post

PART 1:
Context: Say we have login data in the table logins:

Task: Write a query that gets the number of retained users per month. In this case, retention for a given month is defined as the number of users who logged in that month who also logged in the immediately previous month.

Solution:

PART 2:

Task: Now we’ll take retention and turn it on its head: Write a query to find how many users last month did not come back this month. i.e. the number of churned users

Solution:

Note that there are solutions to this problem that can use LEFT or RIGHT joins.

PART 3:
Context: You now want to see the number of active users this month who have been reactivated — in other words, users who have churned but this month they became active again. Keep in mind a user can reactivate after churning before the previous month. An example of this could be a user active in February (appears in logins), no activity in March and April, but then active again in May (appears in logins), so they count as a reactivated user for May .

Task: Create a table that contains the number of reactivated users per month.

Solution:

Cumulative Sums with SQL

Acknowledgement: This problem was inspired by Sisense’s “Cash Flow modeling in SQL” blog post
Context: Say we have a table transactions in the form:

Where cash_flow is the revenues minus costs for each day.

Task: Write a query to get cumulative cash flow for each day such that we end up with a table in the form below:

Solution using a window function (more effcient):

Alternative Solution (less efficient):

Rolling Averages with SQL

Acknowledgement: This problem is adapted from Sisense’s “Rolling Averages in MySQL and SQL Server” blog post
Note: there are different ways to compute rolling/moving averages. Here we’ll use a preceding average which means that the metric for the 7th day of the month would be the average of the preceding 6 days and that day itself.
Context: Say we have table signups in the form:

Task: Write a query to get 7-day rolling (preceding) average of daily sign ups

Solution1:

Solution2: (using windows, more efficient)

Multiple Join Conditions in SQL

Acknowledgement: This problem was inspired by Sisense’s “Analyzing Your Email with SQL” blog post
Context: Say we have a table emails that includes emails sent to and from zach@g.com:

Task: Write a query to get the response time per email (id) sent to zach@g.com . Do not include ids that did not receive a response from zach@g.com. Assume each email thread has a unique subject. Keep in mind a thread may have multiple responses back-and-forth between zach@g.com and another email address.

Solution:

SQL Window Function Practice Problems

#1: Get the ID with the highest value
Context: Say we have a table salaries with data on employee salary and department in the following format:

Task: Write a query to get the empno with the highest salary. Make sure your solution can handle ties!

#2: Average and rank with a window function (multi-part)

PART 1:
Context: Say we have a table salaries in the format:

Task: Write a query that returns the same table, but with a new column that has average salary per depname. We would expect a table in the form:

Solution:

PART 2:
Task: Write a query that adds a column with the rank of each employee based on their salary within their department, where the employee with the highest salary gets the rank of 1. We would expect a table in the form:

Solution:

 

Reference: 800 Data Science Questions & Answers doc by

800 Data Science Questions & Answers doc by Steve Nouri

Direct download here

DataWarehouse Cheat Sheet

Datawarehouse cheat Sheet
BFG Movie letter-signed
BFG Movie letter

What are Differences between Supervised and Unsupervised Learning?

Supervised UnSupervised
Input data is labelled Input data is unlabeled
Split in training/validation/test No split
Used for prediction Used for analysis
Classification and Regression Clustering, dimension reduction, and density estimation

Python Cheat Sheet

Python Beginners Cheat Sheet

Data Sciences Cheat Sheet

Data Sciences Cheat Sheet

Panda Cheat Sheet

Pandas Cheat Sheet

Learn SQL with Practical Exercises

SQL is definitely one of the most fundamental skills needed to be a data scientist.

This is a comprehensive handbook that can help you to learn SQL (Structured Query Language), which could be directly downloaded here

Credit: D Armstrong

Learn SQL with Practical_Exercises

Data Visualization: A comprehensive VIP Matplotlib Cheat sheet

A comprehensive VIP Matplotlib cheatsheet

Credit: Matplotlib

Download it here

Power BI for Intermediates

Power BI for Intermediates

Download it here

Credit: Soheil Bakhshi and Bruce Anderson

How to get a job in data science – a semi-harsh Q/A guide.

How to get a job in data science – a semi-harsh Q/A guide.

HOW DO I GET A JOB IN DATA SCIENCE?

Hey you. Yes you, person asking “how do I get a job in data science/analytics/MLE/AI whatever BS job with data in the title?”. I got news for you. There are two simple rules to getting one of these jobs.

Have experience.

Don’t have no experience.

There are approximately 1000 entry level candidates who think they’re qualified because they did a 24 week bootcamp for every entry level job. I don’t need to be a statistician to tell you your odds of landing one of these aren’t great.

HOW DO I GET EXPERIENCE?

Are you currently employed? If not, get a job. If you are, figure out a way to apply data science in your job, then put it on your resume. Mega bonus points here if you can figure out a way to attribute a dollar value to your contribution. Talk to your supervisor about career aspirations at year-end/mid-year reviews. Maybe you’ll find a way to transfer to a role internally and skip the whole resume ignoring phase. Alternatively, network. Be friends with people who are in the roles you want to be in, maybe they’ll help you find a job at their company.

WHY AM I NOT GETTING INTERVIEWS?

IDK. Maybe you don’t have the required experience. Maybe there are 500+ other people applying for the same position. Maybe your resume stinks. If you’re getting 1/20 response rate, you’re doing great. Quit whining.

IS XYZ DEGREE GOOD FOR DATA SCIENCE?

Does your degree involve some sort of non-remedial math higher than college algebra? Does your degree involve taking any sort of programming classes? If yes, congratulations, your degree will pass most base requirements for data science. Is it the best? Probably not, unless you’re CS or some really heavy math degree where half your classes are taught in Greek letters. Don’t come at me with those art history and underwater basket weaving degrees unless you have multiple years experience doing something else.

SHOULD I DO XYZ BOOTCAMP/MICROMASTERS?

Do you have experience? No? This ain’t gonna help you as much as you think it might. Are you experienced and want to learn more about how data science works? This could be helpful.

SHOULD I DO XYZ MASTER’S IN DATA SCIENCE PROGRAM?

Congratulations, doing a Master’s is usually a good idea and will help make you more competitive as a candidate. Should you shell out 100K for one when you can pay 10K for one online? Probably not. In all likelihood, you’re not gonna get $90K in marginal benefit from the more expensive program. Pick a known school (probably avoid really obscure schools, the name does count for a little) and you’ll be fine. Big bonus here if you can sucker your employer into paying for it.

WILL XYZ CERTIFICATE HELP MY RESUME?

Does your certificate say “AWS” or “AZURE” on it? If not, no.

DO I NEED TO KNOW XYZ MATH TOPIC?

Yes. Stop asking. Probably learn probability, be familiar with linear algebra, and understand what the hell a partial derivative is. Learn how to test hypotheses. Ultimately you need to know what the heck is going on math-wise in your predictions otherwise the company is going to go bankrupt and it will be all your fault.

WHAT IF I’M BAD AT MATH?

Do some studying or something. MIT opencourseware has a bunch of free recorded math classes. If you want to learn some Linear Algebra, Gilbert Strang is your guy.

WHAT PROGRAMMING LANGUAGES SHOULD I LEARN?

STOP ASKING THIS QUESTION. I CAN GOOGLE “HOW TO BE A DATA SCIENTIST” AND EVERY SINGLE GARBAGE TDS ARTICLE WILL TELL YOU SQL AND PYTHON/R. YOU’RE LUCKY YOU DON’T HAVE TO DEAL WITH THE JOY OF SEGMENTATION FAULTS TO RUN A SIMPLE LINEAR REGRESSION.

SHOULD I LEARN PYTHON OR R?

Both. Python is more widely used and tends to be more general purpose than R. R is better at statistics and data analysis, but is a bit more niche. Take your pick to start, but ultimately you’re gonna want to learn both you slacker.

SHOULD I MAKE A PORTFOLIO?

Yes. And don’t put some BS housing price regression, iris classification, or titanic survival project on it either. Next question.

WHAT SHOULD I DO AS A PROJECT?

IDK what are you interested in? If you say twitter sentiment stock market prediction go sit in the corner and think about what you just said. Every half brained first year student who can pip install sklearn and do model.fit() has tried unsuccessfully to predict the stock market. The efficient market hypothesis is a thing for a reason. There are literally millions of other free datasets out there you have one of the most powerful search engines at your fingertips to go find them. Pick something you’re interested in, find some data, and analyze it.

DO I NEED TO BE GOOD WITH PEOPLE? (courtesy of /u/bikeskata)

Yes! First, when you’re applying, no one wants to work with a weirdo. You should be able to have a basic conversation with people, and they shouldn’t come away from it thinking you’ll follow them home and wear their skin as a suit. Once you get a job, you’ll be interacting with colleagues, and you’ll need them to care about your analysis. Presumably, there are non-technical people making decisions you’ll need to bring in as well. If you can’t explain to a moderately intelligent person why they should care about the thing that took you 3 days (and cost $$$ in cloud computing costs), you probably won’t have your position for long. You don’t need to be the life of the party, but you should be pleasant to be around.

Credit: u/save_the_panda_bears

Top 75 Data Science Youtube channel

1- Alex The Analyst
2- Tina Huang
3- Abhishek Thakur
4- Michael Galarnyk
5- How to Get an Analytics Job
6- Ken Jee
7- Data Professor
8- Nicholas Renotte
9- KNN Clips
10- Ternary Data: Data Engineering Consulting
11- AI Basics with Mike
12- Matt Brattin
13- Chronic Coder
14- Intersnacktional
15- Jenny Tumay
16- Coding Professor
17- DataTalksClub
18- Ken’s Nearest Neighbors Podcast
19- Karolina Sowinska
20- Lander Analytics
21- Lights OnData
22- CodeEmporium
23- Andreas Mueller
24- Nate at StrataScratch
25- Kaggle
26- Data Interview Pro
27- Jordan Harrod
28- Leo Isikdogan
29- Jacob Amaral
30- Bukola
31- AndrewMoMoney
32- Andreas Kretz
33- Python Programmer
34- Machine Learning with Phil
35- Art of Visualization
36- Machine Learning University