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**What are the top 3 methods used to find Autoregressive Parameters in Data Science?**

In order to find autoregressive parameters, you will first need to understand what autoregression is. **Autoregression is a statistical method used to create a model that describes data as a function of linear regression of lagged values of the dependent variable**. In other words, it is a model that uses past values of a dependent variable in order to predict future values of the same dependent variable.

In time series analysis,** autoregression is the use of previous values in a time series to predict future values.** In other words, it is a form of regression where the dependent variable is forecasted using a linear combination of past values of the independent variable. The parameter values for the autoregression model are estimated using the method of least squares.

The autoregressive parameters are the coefficients in the autoregressive model. These coefficients can be estimated in a number of ways, including ordinary least squares (OLS), maximum likelihood (ML), or least squares with L1 regularization (LASSO). Once estimated, the autoregressive parameters can be used to predict future values of the dependent variable.

To find the autoregressive parameters, you need to use a method known as** least squares regression**. This method finds the parameters that minimize the sum of the squared residuals. The residual is simply the difference between the predicted value and the actual value. So, in essence, you are finding the parameters that best fit the data.

**How to Estimate Autoregressive Parameters?**

There are three main ways to estimate autoregressive parameters: ordinary least squares (OLS), maximum likelihood (ML), or least squares with L1 regularization (LASSO).

**Ordinary Least Squares**: Ordinary least squares is the simplest and most common method for estimating autoregressive parameters. This method estimates the parameters by minimizing the sum of squared errors between actual and predicted values.

**Maximum Likelihood**: Maximum likelihood is another common method for estimating autoregressive parameters. This method estimates the parameters by maximizing the likelihood function. The likelihood function is a mathematical function that quantifies the probability of observing a given set of data given certain parameter values.

**Least Squares with L1 Regularization**: Least squares with L1 regularization is another method for estimating autoregressive parameters. This method estimates the parameters by minimizing the sum of squared errors between actual and predicted values while also penalizing models with many parameters. L1 regularization penalizes models by adding an extra term to the error function that is proportional to the sum of absolute values of the estimator coefficients.

**Finding Autoregressive Parameters:** The Math Behind It

To find the autoregressive parameters using least squares regression, you first need to set up your data in a certain way. You need to have your dependent variable in one column and your independent variables in other columns. For example, let’s say you want to use three years of data to predict next year’s sales (the dependent variable). Your data would look something like this:

| Year | Sales |

|——|——-|

| 2016 | 100 |

| 2017 | 150 |

| 2018 | 200 |

Next, you need to calculate the means for each column. For our sales example, that would look like this:

$$ \bar{Y} = \frac{100+150+200}{3} = 150$$

Now we can calculate each element in what’s called the variance-covariance matrix:

$$ \operatorname {Var} (X)=\sum _{i=1}^{n}\left({x_{i}}-{\bar {x}}\right)^{2} $$

and

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$$ \operatorname {Cov} (X,Y)=\sum _{i=1}^{n}\left({x_{i}}-{\bar {x}}\right)\left({y_{i}}-{\bar {y}}\right) $$

For our sales example, that calculation would look like this:

$$ \operatorname {Var} (Y)=\sum _{i=1}^{3}\left({y_{i}}-{\bar {y}}\right)^{2}=(100-150)^{2}+(150-150)^{2}+(200-150)^{2})=2500 $$

and

$$ \operatorname {Cov} (X,Y)=\sum _{i=1}^{3}\left({x_{i}}-{\bar {x}}\right)\left({y_{i}}-{\bar {y}}\right)=(2016-2017)(100-150)+(2017-2017)(150-150)+(2018-2017)(200-150))=-500 $$

Now we can finally calculate our autoregressive parameters! We do that by solving this equation:

$$ \hat {\beta }=(X^{\prime }X)^{-1}X^{\prime }Y=\frac {1}{2500}\times 2500\times (-500)=0.20 $$\.20 . That’s it! Our autoregressive parameter is 0\.20 . Once we have that parameter, we can plug it into our autoregressive equation:

$$ Y_{t+1}=0\.20 Y_t+a_1+a_2+a_3footnote{where $a_1$, $a_2$, and $a_3$ are error terms assuming an AR(3)} .$$ And that’s how you solve for autoregressive parameters! Of course, in reality you would be working with much larger datasets, but the underlying principles are still the same. Once you have your autoregressive parameters, you can plug them into the equation and start making predictions!.

**Which Method Should You Use?**

The estimation method you should use depends on your particular situation and goals. If you are looking for simple and interpretable results, then Ordinary Least Squares may be the best method for you. If you are looking for more accurate predictions, then Maximum Likelihood or Least Squares with L1 Regularization may be better methods for you.

**Autoregressive models STEP BY STEP:**

1) **Download data**: The first step is to download some data. This can be done by finding a publicly available dataset or by using your own data if you have any. For this example, we will be using data from the United Nations Comtrade Database.

2) **Choose your variables**: Once you have your dataset, you will need to choose the variables you want to use in your autoregression model. In our case, we will be using the import and export values of goods between countries as our independent variables.

3) **Estimate your model:** After choosing your independent variables, you can estimate your autoregression model using the method of least squares. OLS estimation can be done in many statistical software packages such as R or STATA.

4) **Interpret your results**: Once you have estimated your model, it is important to interpret the results in order to understand what they mean. The coefficients represent the effect that each independent variable has on the dependent variable. In our case, the coefficients represent the effect that imports and exports have on trade balance. A positive coefficient indicates that an increase in the independent variable leads to an increase in the dependent variable while a negative coefficient indicates that an increase in the independent variable leads to a decrease in the dependent variable.

5)**Make predictions:** Finally, once you have interpreted your results, you can use your autoregression model to make predictions about future values of the dependent variable based on past values of the independent variables.

**Conclusion:** In this blog post, we have discussed what autoregression is and how to find autoregressive parameters.

Estimating an autoregression model is a relatively simple process that can be done in many statistical software packages such as R or STATA.

In statistics and machine learning, autoregression is a modeling technique used to describe the linear relationship between a dependent variable and one more independent variables. To find the autoregressive parameters, you can use a method known as least squares regression which minimizes the sum of squared residuals. This blog post also explains how to set up your data for calculating least squares regression as well as how to calculate Variance and Covariance before finally calculating your autoregressive parameters. After finding your parameters you can plug them into an autoregressive equation to start making predictions about future events!

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We have also discussed three different methods for estimating those parameters: Ordinary Least Squares, Maximum Likelihood, and Least Squares with L1 Regularization. **The appropriate estimation method depends on your particular goals and situation.**

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**Machine Learning – Software Classification**

# Autoregressive Model

Autoregressive generative models can estimate complex continuous data distributions such as trajectory rollouts in an RL environment, image intensities, and audio. Traditional techniques discretize continuous data into various bins and approximate the continuous data distribution using categorical distributions over the bins. This approximation is parameter inefficient as it cannot express abrupt changes in density without using a significant number of additional bins. Adaptive Categorical Discretization (ADACAT) is proposed in this paper as a parameterization of 1-D conditionals that is expressive, parameter efficient, and multimodal. A vector of interval widths and masses is used to parameterize the distribution known as ADACAT. Figure 1 showcases the difference between the traditional uniform categorical discretization approach with the proposed ADACAT.

Each component of the ADACAT distribution has non-overlapping support, making it a specific subfamily of mixtures of uniform distributions. ADACAT generalizes uniformly discretized 1-D categorical distributions. The proposed architecture allows for variable bin widths and more closely approximates the modes of two Gaussians mixture than a uniformly discretized categorical, making it highly expressive than the latter. Additionally, a distribution’s support is discretized using quantile-based discretization, which bins data into groups with similar measured data points. ADACAT uses deep autoregressive frameworks to factorize the joint density into numerous 1-D conditional ADACAT distributions in problems with more than one dimension.

Continue reading | *Check out the* *paper* *and* *github link.*

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- Book Recommendations for Network Graphs?by /u/theRealDavidDavis (Data Science) on January 29, 2023 at 4:09 pm
I have a basic foundation in in subject and I have even developed my own code to generate some interactive 3D graph networks however I also recognize that I am no expert in the subject so I'm looking for some reccomendations on some good books for self study. Ideally, the book would focus more on the math and logic as I can develop the code to implement this myself and realistically the applications at work call for something more powerful than networkX. submitted by /u/theRealDavidDavis [link] [comments]

- [D] Simple Questions Threadby /u/AutoModerator (Machine Learning) on January 29, 2023 at 4:00 pm
Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]

- [P] Automating a Youtube Shorts channel with Huggingface Transformers and After Effectsby /u/Ch1nada (Machine Learning) on January 29, 2023 at 3:54 pm
I’ll try to get into detail about the implementation and difficulties in case it is useful for anyone else trying to do something similar with an applied ML project, so there’s a TLDR at the end if you’d like the short version/result. At the end of last year I convinced myself to start 2023 by creating a side-project that I'd actually finish and deploy and perhaps earn some “passive” income (spoiler, not so passive after all :P), and after some brainstorming I settled on making an automated Youtube channel about finance news since I had just gotten into investing. Shorts seemed to be more manageable and monetization is changing in February so I went with that. My rough initial idea was to get online articles, summarize them, make a basic compilation with some combination of pymovie, opencv and stock photos and done. I was pretty worried about the summarization, since in my ML day job I mainly work with vision or sensor data in manufacturing not NLP. Also, I quickly realized pymovie with still images and some overlayed text was not very attractive for viewers (starting with myself). Fast-forward a few days, and after some research online I came across two things, Huggingface transformers (yep, I know I’ve been living under a rock :P) and After Effects scripting. From here, it became mainly about figuring out exactly which ML models I needed to fine-tune for finance / social media and for what, then putting it all together. The entire workflow looks something like this: the bot fetches online daily news about a topic (stocks or crypto), then sentiment analysis is performed on the title and the full text is summarized into a single sentence. I fine-tuned SBERT on ~1.5M posts from /r/worldnews publicly available in Google Cloud BigQuery so that it could predict a “social engagement” score that could be used to rank and filter the news that would make it into the video. Finally, all of this is combined into a single JSON object written into a .js file that can be used by another “content creator” script to render the video from a template using aerender in Python. The content of this template is generated dynamically based on the contents of the .js file via AE Expressions. This module also uses the TTS lib to generate voice-overs for the text, and is also responsible for generating the title (using NLTK to identify the main subjects of each title) and the video’s description. Pexel stock videos are used for the background. In principle automating the upload to Youtube could also be done, but at this stage I’m handling this manually as the JSON generation is not as robust as I’d like, so the output file often needs to be tweaked and fixed before the video can be finalized and uploaded. An examples is the summary being too short or vague when taken out of the context of the original article. If you increase the max_length of the summarizer to compensate, it can easily become too long to for the overlay to fit the pre-defined dimensions, or the total audio length can be too long for the max duration of a youtube short. With some more work I’m confident the whole process can be automated further. For those interested, feel free to check the result here: Byte Size Bot channel If you have any questions or suggestions I’d be happy to hear them. TLDR: Coded an automated (not 100% yet, but will get there) Youtube Shorts channel about finance news to create a passive income stream. Ended up being way harder, more fun and not so “passive” than my initial expectations. submitted by /u/Ch1nada [link] [comments]

- hi guys require instagram dataset for building a hashtag generator for self learning. Please guide me from where I can get this. Thanksby /u/rsinda (Data Science) on January 29, 2023 at 3:53 pm
submitted by /u/rsinda [link] [comments]

- Looking from DS background for a side projectby /u/ChestAgitated5206 (Data Science) on January 29, 2023 at 3:21 pm
Hi people, I'm looking for a data scientist to partner with on a side project I'm working. I'd love if the person working on also part time like me. Building a free product for now so no money involve but open to paid consultation if you feel like you can help. An example on what I am trying to build: https://www.tella.tv/video/cldh231d200090fmacmkj14s2/view submitted by /u/ChestAgitated5206 [link] [comments]

- Airport ridesharing data?by /u/frankOFWGKTA (Data Science) on January 29, 2023 at 2:58 pm
Hi All, I am working on a project trying to analyze ride-sharing to and from airports. I have emailed airports and ridesharing companies and searched Kaggle and other sites. Now I'm looking for some more information/advice. Does anyone have any suggestions on where I could find ridesharing data? Or where I could scrape it from? Or anyone that I could contact. Or any other subreddits I should try? Thanks! submitted by /u/frankOFWGKTA [link] [comments]

- [D] GPT-Index vs Langchainby /u/TikkunCreation (Machine Learning) on January 29, 2023 at 2:42 pm
Someone I work with wrote the below for our internal team (shared with permission) and I thought some here may find it helpful. Recently, I built an app that uses GPT-Index & LangChain to provide an answer to a question based on a piece of text as context. I found GPT-Index to be much easier and straightforward to integrate, but it seems like LangChain has more features and is more powerful. Here's my experience integrating both of them. GPT-Index First thing I did was review their docs to make sure I understood what GPT-Index was, what it could do, and how I was going to use it I went back and forth a couple times figuring out how I was going to use it. Then I found the quickstart guide It seemed like the quickstart guide would work so I followed the guide and after a few tries, I was getting solid responses to the questions I asked it LangChain I followed the same step, reviewing their docs. LangChain's docs has more to it because it seems like it does more, so this step took longer It was tough for me to figure out how I needed to use LangChain. I had to ask for some help to better understand our use case Once I thought I knew how I was going to use LangChain, I began coding. I ran into more errors with LangChain It seems that my first approach wasn't correct, so I switched to something similar and I was finally getting a response. The response was 'I don't know'... I didn't know what to do about it Then I checked out the logs of the data being passed through and found that the context was being cut off. To make sure it worked, I asked a question relating to the text that was getting passed through. The response seemed to make sense, so now I know better where the issue is at I still need to fix the context being cut off. I followed the docs of LangChain very closely, so I'm wondering if the docs are old or if I have the wrong implementation So overall, if GPT Index solves your use case, start with that. We also did a variant built without GPT-Index and without langchain, and that one worked well too. We occasionally share stuff like this on genainews.org though the newsletter is mostly about new ai startups and products so figured it good to post here. Anyone else here that's worked with both GPT-Index and langchain and can offer additional thoughts? submitted by /u/TikkunCreation [link] [comments]

- Which countries /orgz let you take more than 1 month long leaves?by /u/anotheraccount97 (Data Science) on January 29, 2023 at 2:38 pm
I saw a poll on r/cscareerquestionsEU which asked the number of weeks SWEs get as vacation in Switzerland. Most people replied 5 and 6 weeks. I guess there's a lot of declared holidays too (~30 days in a year). Unlimited sick leave is common across EU I guess. Which countries have good pay and the highest possibility that I can take a 1 month long block leave (bring completely out of reach)? And which countries or organizations have the highest amount of leaves in general that people do end up taking? I'm going to be doing an MS in AI/DS soon in the US. But can apply to some Unis in EU as well. I'm learning that US wouldn't let me take much leaves. Also any other Careers that let you take 3 weeks to longer than 2 month leaves?😬 submitted by /u/anotheraccount97 [link] [comments]

- Good resources for covering SVMs?by /u/HyperKingK (Data Science) on January 29, 2023 at 2:23 pm
SVMs were covered quite poorly in my Machine Learning course last semester, the teacher quickly glossed over the maths, only ever mentioning the key bits of it like "Langrangian" or "Kuhn-Tucker conditions". Most resources I referred just went "the math is out of the scope of this textbook" Now I still have no clue what or how SVMs find the Maximal Margin Hyperplane. I am looking for resources that help me understand. I have no problem sinking some time into learning the maths (I believe it's called optimization theory?) behind it either. Thanks! submitted by /u/HyperKingK [link] [comments]

- Interview questions about kernel SVMby /u/ArchibaldChain (Data Science) on January 29, 2023 at 1:55 pm
I encountered a question about Kernel SVM in a interview test to a internship, but I am not certain about the answer. Can anyone try to answer it? To use a Gaussian kernel SVM, we usually normaliz the feature. Which one or few of following options is (are) True? a. （I don't quite remember this option. This one I pretty sure is True, but next two I am uncertain about. Can anyone help me remember it?) b. Sometimes some features cannot be normalized like categorical feature. c. Gaussian kernel SVM cannot be done without feature normalization. submitted by /u/ArchibaldChain [link] [comments]

- Should I finish my economics degree asap or focus on learning DS?by /u/Pitiful-Carpet8141 (Data Science) on January 29, 2023 at 1:29 pm
Hi there, I have 2 semesters left to get graduated in economics and so far the subjects that I enjoyed the most have been econometrics and statistics. In fact, I just finished a DS minor this semester which was pretty enjoyable for me. The thing is, I'm starting to look for bootcamps or self-taught so I could start developing my career in DS as soon as possible, but I'm not sure if this is the right choice. To make it clear, I would 100% finish the degree but maybe not as my first priority if I could get a DS job. Would you recommend finishing my degree and then focusing on DS or is it a good choice to prioritize learning DS and trying to get a job? Thanks in advance, submitted by /u/Pitiful-Carpet8141 [link] [comments]

- [P] Targeted Summarization - A tool for information extractionby /u/helliun (Machine Learning) on January 29, 2023 at 12:48 pm
submitted by /u/helliun [link] [comments]

- Can keeping channels more than '3' in images crash CNNs?by /u/jhanjeek (Data Science) on January 29, 2023 at 12:21 pm
I have an encoder model which was working fine with single channel 1024,1024 images, I'm trying to patch the original images (mega pixel images) to 256, 256, 64 images. I've changed my encoder input to match the images input that the model will get. The model call function is working fine, loss is getting calculated fine, but I'm getting the following error with tape.gradient: 2023-01-29 17:11:01.868555: F tensorflow/stream_executor/cuda/cuda_dnn.cc:593] Check failed: cudnnSetTensorNdDescriptor(handle_.get(), elem_type, nd, dims.data(), strides.data()) == CUDNN_STATUS_SUCCESS (3 vs. 0)batch_descriptor: {count: 10 feature_map_count: 64 spatial: 0 0 value_min: 0.000000 value_max: 0.000000 layout: BatchYXDepth} C:\ProgramData\Anaconda3\lib\site-packages\joblib\externals\loky\backend\resource_tracker.py:318: UserWarning: resource_tracker: There appear to be 2 leaked folder objects to clean up at shutdown warnings.warn('resource_tracker: There appear to be %d ' C:\ProgramData\Anaconda3\lib\site-packages\joblib\externals\loky\backend\resource_tracker.py:333: UserWarning: resource_tracker: C:\Users\kjhan\AppData\Local\Temp\joblib_memmapping_folder_12248_772bbeeeccff43089fa0e6d75271eebd_97f2f7c6edd04b468a4360bf96b91b84: FileNotFoundError(2, 'The system cannot find the path specified') warnings.warn('resource_tracker: %s: %r' % (name, e)) C:\ProgramData\Anaconda3\lib\site-packages\joblib\externals\loky\backend\resource_tracker.py:333: UserWarning: resource_tracker: C:\Users\kjhan\AppData\Local\Temp\joblib_memmapping_folder_12248_29db2f1e8ff54416b9a78c6f69dcff23_40a85063390f46d38d15c1877f99acc8: FileNotFoundError(2, 'The system cannot find the path specified') warnings.warn('resource_tracker: %s: %r' % (name, e)) [I 17:11:10.131 NotebookApp] KernelRestarter: restarting kernel (1/5), keep random ports kernel 286d1cc6-8ddd-46f9-baf7-5e1b05a2d033 restarted My code is as below class encoder(tf.keras.layers.Layer): def __init__(self,size:tuple): super(encoder, self).__init__() #encoder Module self.input_cnn = keras.layers.InputLayer(input_shape=(size[0],size[1],size[2])) self.conv_1 = keras.layers.Conv2D(input_shape=(size[0],size[1],size[2]),filters=16,kernel_size=(3,3),padding='same',activation='relu') self.conv_2 = keras.layers.MaxPool2D(pool_size=(2,2),strides=(2,2)) self.conv_3 = keras.layers.Conv2D(filters = 16,kernel_size=(4,4),strides=(2,2),padding='same',activation='relu') self.conv_4 = keras.layers.Conv2D(filters = 32,kernel_size=(4,4),strides=(4,4),padding='same',activation='relu') self.conv_5 = keras.layers.BatchNormalization() self.conv_6 = keras.layers.MaxPool2D(pool_size=(2,2),strides=(2,2)) self.conv_7 = keras.layers.Conv2D(filters = 64,kernel_size=(8,8),strides=(8,8),padding='same',activation='relu') self.conv_8 = keras.layers.MaxPool2D(pool_size=(2,2),strides=(2,2)) self.conv_9 = keras.layers.BatchNormalization() self.conv_10 = keras.layers.Conv2D(filters = 1 ,kernel_size=(3,3),strides=(1,1),padding='same',activation='relu') def call(self,inputs,training = True): x = self.input_cnn(inputs) x = self.conv_1(x) x = self.conv_2(x) x = self.conv_3(x) x = self.conv_4(x) if training == True: x = self.conv_5(x,training = True) else: x = self.conv_5(x,training = False) x = self.conv_6(x) x = self.conv_7(x) x = self.conv_8(x) if training == True: x = self.conv_9(x,training = True) else: x = self.conv_9(x,training = False) x = self.conv_10(x) return x size 0 is 256 size 1 is 256 size 2 is 64 Train_step from main model: def __init__(self, size: tuple, optimizer = keras.optimizers.Adam(learning_rate=1e-3),loss_fn = keras.losses.BinaryCrossentropy(from_logits=False),metric = tf.keras.metrics.Accuracy()): super(BCDClassifier, self).__init__() self.input_cat = keras.layers.InputLayer(input_shape = (2,)) self.encode = encoder(size) self.flatten = keras.layers.Flatten() self.concat = keras.layers.Concatenate(axis = 1) self.classify = classifier(32) self.optimizer = optimizer self.loss_fn = loss_fn self.loss_tracker = keras.metrics.Mean(name="loss") self.acc_tracker = metric self.f1_tracker = tfa.metrics.F1Score(num_classes=2, threshold=0.5, average = 'micro') self.sk_metric_acc = accuracy_score self.sk_metric_f1 = f1_score self.acc_history = [] self.loss_history = [] self.f1_history = [] def call(self, cat_batch, view_batch, images_batch, training = True): x1 = self.encode(images_batch,training) x2 = self.input_cat(cat_batch) x1 = self.flatten(x1) x12 = self.concat([x1,x2]) x12 = self.classify(x12) return x12 def train_step(self,cat_batch, views_batch, images_batch, target_batch, training = True): with tf.GradientTape() as tape: logits = self(cat_batch, views_batch, images_batch,training) loss_value = self.loss_fn(target_batch, logits) grads = tape.gradient(loss_value, self.trainable_weights) self.optimizer.apply_gradients(zip(grads, self.trainable_weights)) self.loss_tracker.update_state(loss_value) pred = [] target = [] threshold = 0.5 for val in logits.numpy(): if isinstance(val,np.ndarray): for v_1 in val: if isinstance(v_1,np.ndarray): for v_2 in v_1: if v_2 > threshold: pred.append(1.0) else: pred.append(0.0) else: if v_1 > threshold: pred.append(1.0) else: pred.append(0.0) else: if val > threshold: pred.append(1.0) else: pred.append(0.0) for val in target_batch: if isinstance(val,np.ndarray): for v_1 in val: if isinstance(v_1,np.ndarray): for v_2 in v_1: target.append(v_2) else: target.append(v_1) else: target.append(val) acc = self.sk_metric_acc(target,pred) f1 = self.sk_metric_f1(target,pred) #self.f1_tracker.update_state(target_batch,logits) return {"Loss": self.loss_tracker.result(), "Accuracy": acc, 'F1-score':f1} Can someone please help me figure out this error? submitted by /u/jhanjeek [link] [comments]

- OMSCS in GaTech vs MS DS in UT Austinby /u/karraju (Data Science) on January 29, 2023 at 11:55 am
HI folks I am toying with the idea of a OMSCS vs MS DS in UT Austin or MS DS from Colorado, Boulder. I am a bit worried about being weak (have logn forgotten my programming skills) in Data Structures/Algorithms and so have some conerns on doing OMSCS. What would this group advise? Thanks. submitted by /u/karraju [link] [comments]

- Is it possible to educate myself using online resources to get a job as a junior data scientist with no prior background in the field? If so, I would really appreciate any resource that can be shared. Thanks.by /u/Keep_learning88 (Data Science) on January 29, 2023 at 9:59 am
submitted by /u/Keep_learning88 [link] [comments]

- [D] How do people keep up with ML news that is not NLP related?by /u/shaner92 (Machine Learning) on January 29, 2023 at 9:48 am
Lately, NLP is taking up most of the public space, much of AI news is focused on LLM after Chat-GPT took the spotlight. How do non-NLP people keep up with news? I recently saw a post on reddit where tree models are still being improved. There are other topics too, like the recent trend in Model Explainability which feels to have slowed down. I'd guess this all gets into the more categorical questions which I am wrapping up with 'How do YOU get your ML news'? How does information gathering differ between those in Applied ML and AI researchers (or even further, between those in Business Analytics and those in more 'AI' fields) What sort of interesting things are out there in the world of ML now? (model or non-model related) In looking for Use Cases, does this partially come down to your field? (Finance reads finance news, pharma reads pharma news) Many of the AI/ML Newsletters which I subscribed to when I was less experienced seemed to be full of variety, but as they are all converging to NLP recently maybe it is time to cleanse the subscriptions, or find some new resources. submitted by /u/shaner92 [link] [comments]

- What test to use to test differences in proportions of total?by /u/zsa23761 (Data Science) on January 29, 2023 at 6:46 am
Let’s say I’m trying to increase the proportion of total female customers on my site. If I test 2 landing pages and my target metric is proportion of total purchases that are women, what test should I use? Let’s say we have the following sample data: Control: 400 male customers, 600 female customers Treatment: 350 male customers, 600 female customers What test can I use to test whether the increase in female proportion is statistically significant? submitted by /u/zsa23761 [link] [comments]

- A new way to accelerate your data science workflowby /u/CORNMONSTER_2022 (Data Science) on January 29, 2023 at 5:54 am
When working with large datasets or executing complex operations using pandas, you may experience Out of Memory (OOM) errors or long waiting periods for the results. Xorbits can be an ideal solution for these issues. Xorbits is a scalable Python data science framework that aims to scale the Python data science stack while keeping the API compatibility. You can get an out-of-box performance gain by changing `import pandas as pd` to `import xorbits.pandas as pd`. Compared with other pandas-like solutions, Xorbits offers stronger performance, simpler deployment, and better API compatibility. With TPC-H benchmarks at scale factor 100, Xorbits is 7x faster than Dask. For the benchmark results compared to other pandas-like systems, please visit: https://xorbits.io/benchmark https://preview.redd.it/uyavcs2irqea1.png?width=2353&format=png&auto=webp&s=455d690ca78bfe69a3e05abc0dd0d09ca24dded6 If you are interested in learning more about Xorbits, please visit our project's Github for more information: https://github.com/xprobe-inc/xorbits submitted by /u/CORNMONSTER_2022 [link] [comments]

- Has anyone gotten a job with a data science certificate?by /u/madlove17 (Data Science) on January 29, 2023 at 5:24 am
I'm in the U.S. and I've enrolled in the WIOWA (Workforce Innovation and Opportunity Act) program since I'm a dislocated worker. I'm interested in getting a cert through UCLA's Extension course program. If you've gotten certs what was your experience? submitted by /u/madlove17 [link] [comments]

- Advice on Learning Pathway(s) and where personal interests intersect with DSby /u/SilentWitness13 (Data Science) on January 29, 2023 at 5:05 am
Hi folks, Currently working through the IBM DS and Google Analytics certificate. I am a complete rookie in DS. I am interested in where linguistics and behavioral economics intersect with data science in terms of the types of jobs/research fields to target as it seems like 'Data Scientist' is somewhat of a catch all term for HR departments. In terms of a following a learning pathway, employers (HR?) seem to all look for a degree, however online postgrad and BsS computer science course seem overpriced and, outside of data science specific programs, not as good as combining bootcamps, certificates and other online learning routes. Is this the case? In terms of building a portfolio of work, or a learning journal that can be linked to CV, does anyone have any recommendations? Many thanks for any help! submitted by /u/SilentWitness13 [link] [comments]