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Examining the Fragmented Data on Black Entrepreneurship in North America
The data surrounding Black entrepreneurship in the United States and Canada is fragmented. This means that it is difficult to assess the number of Black-owned businesses and their economic impact accurately. To make sure that we are able to get an accurate picture of the state of Black entrepreneurship in these two countries, it is essential to find ways to standardize data collection and assessment.
Despite the significant contributions of Black entrepreneurs and business owners, there is a lack of reliable data for assessing their economic and financial impact. The existing information is often inconsistent, misinterpreted, or incomplete. This blog post will examine the current state of Black entrepreneurship in North America, identify where there are gaps in the available data, and propose some methods to standardize measures and assessments.
The existing data on Black entrepreneurship in North America is fragmented across different sources. To begin with, there is no common definition or classification system used to measure Black-owned businesses. In addition, many studies rely on self-reported data which can be unreliable due to issues such as survey fatigue or respondent bias. Moreover, most of the available statistics are focused on businesses owned by African Americans rather than other ethnicities that comprise the broad category of “Black” such as Afro-Caribbeans or Afro-Latinos. Consequently, there is a need for more comprehensive data that includes all ethno-racial groups within this category.
In addition, access to capital remains a major barrier for Black entrepreneurs which further complicates our understanding of these businesses. Financing options are limited due to systemic racism and discrimination that have prevented many from obtaining traditional loans from banks or other private lenders. Therefore it is important to consider alternative financing sources when analyzing the financial health of these businesses.
Data Collection Challenges
Collecting accurate data on Black entrepreneurship can be challenging due to a lack of reliable sources. Many government agencies collect data on businesses by size, industry sector, ownership type, or geographic location; however, these categories often do not provide enough information about the demographics of business owners or their employees. Furthermore, some agencies may not collect any demographic information at all. As a result, there is no single source of comprehensive and consistent information on Black entrepreneurs in either country.
The Need for Standardized Data Collection
In order to assess the impact that Black entrepreneurs have on their communities and economies, it is necessary to be able to access accurate data regarding their presence. Unfortunately, currently available data does not provide a comprehensive view of this information. It is therefore important for policy makers, government officials, entrepreneurs, students, and other relevant stakeholders to work together to find solutions that will allow us to collect accurate data on Black-owned businesses across the two countries.
To standardize measures and assessments of Black entrepreneurship, it is essential to develop a unified definition and classification system across jurisdictions as well as consistent methods for collecting data. It should also include specific questions about race/ethnicity that allow researchers to collect more detailed information about each group’s particular needs and challenges. Furthermore, reliable baseline data should be collected regularly so that progress can be tracked over time. Finally, it will be important to focus not only on traditional sources of financing but also alternative funding options such as crowdfunding platforms or angel investors who may provide more accessible financing options for some entrepreneurs.
Methods for Standardizing Data Collection
One way that we can begin standardizing data collection on Black-owned businesses is by creating a unified database of business owners that includes information such as location, industry type, number of employees, annual revenue, etc. This would make it easier for researchers and policy makers to assess the economic impact of these businesses with more accuracy than is currently possible with fragmented data sources. Additionally, conducting surveys and interviews with business owners can also help us better understand how they operate their businesses and what challenges they face when trying to grow their companies.
Another method that could be used is by increasing access to capital for these entrepreneurs through public-private partnerships or other initiatives focused on providing them with the resources they need in order to grow their businesses. This could include grants or low interest loans which would give them more financial stability and enable them to expand their operations or hire additional employees if needed. Finally, implementing education programs specifically designed for aspiring Black entrepreneurs could also help bridge some of the gaps in knowledge that many start up founders may have when starting a business.
In conclusion, we must recognize the importance of reliable data when assessing the economic impact of Black entrepreneurship in North America as well as identifying opportunities for growth and improvement within this sector. While there are still gaps in our knowledge about this subject matter, standardized measures and assessments can help us fill those gaps and gain a better understanding of how best to support these businesses going forward. With better access to capital and resources tailored specifically towards their needs, we can ensure that Black entrepreneurs continue making valuable contributions to our economies both now and into the future.
There are numerous methods available for standardizing measures and assessments of Black entrepreneurship across North America. By working together collaboratively between various stakeholders such as policy makers, government officials, entrepreneurs themselves, students, etc., we can take steps towards reconciling the fragmented data on this subject matter so that we can gain a better understanding of its impact in our society today. With more accurate information at our disposal we will be better equipped to develop meaningful solutions aimed at empowering black entrepreneurs in these two countries moving forward.
Some useful resources for black entrepreneurship in North America:
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Let’s Find out how to Make Money From Web Scraping?
First of all, What is web scraping?
Web Scraping (also termed Screen Scraping, Web Data Extraction, Web Harvesting etc.) is a technique employed to extract large amounts of data from websites whereby the data is extracted and saved to a local file in your computer or to a database in table (spreadsheet) format.
Web scraping can be a very useful skill to learn for anyone looking to start or further their career in data. Web scraping is the process of extracting data from websites, and it can be used to collect everything from images to contact information. While it may sound complicated, web scraping is actually quite simple once you get the hang of it. And best of all, it’s a skill that can be used to make money.
There are a number of ways to make money from web scraping. One popular way is to use web scraping for sport arbitrage. Sport arbitrage is the practice of betting on two different outcomes of the same event in order to profit from the difference in odds. Web scrapers can be used to quickly and easily find arbitrage opportunities by comparing the odds of different bookmakers.
Another way to make money from web scraping is to use it for e-commerce. Web scrapers can be used to collect product information and pricing data from multiple websites, making it easy to compare prices and find the best deals. This can be a great way to save money when shopping online, or even to start your own e-commerce business.
Of course, web scraping can also be used for more altruistic purposes.
If you want to make money with the knowledge of web scraping, you create a bot that successfully gets the valuable data you wished for, then sell the data or bot, or use it to buy or sell or make money on betting via sure bet.
There are some ways to make money using web scraping without selling data: Sport Arbitrage, Stock market, eCommerce, Niche News Aggregation (pick a niche, like celebrity news sites, scrape the top 10 sites, etc), Daily News (pay for a subscription to get past major site paywalls, then make the data free or discounted),Offline, intranet, or hard-to-access data, Lead Generation, Machine learning (Google images), Price monitoring (Ebay), Lead generation (Yelp) [scraping contact info for local biz], Market research (Brewdog) [scraping types of beer and their ratings, for example), App Development (Find Real Estate, Homes for Sale, Apartments & Houses for Rent | realtor.com®) [I can only assume scraping realty data and copying it], Academic Research (Techcrunch), Find Relevant Top Hashtags, etc…
Scraping data from betting sites is a good way to make money because you don’t have to sell data you obtained, but only use that data in your favor. If you never scraped a betting site, I recommend you first check my step by step tutorial Scraping a Betting Site in 10 Minutes where I show the basics of scraping a bookmaker.
It doesn’t matter what sports you like; chances are you or someone you know at least once earned some money betting on their favorite team. You might’ve won because of good luck or knowledge of the sport, but probably you’ve also lost because you can’t always guess what’s going to happen in the future. But what if you could make a profit regardless of the match outcome? This is called ‘surebet’ and isn’t new in the gambling world.
Surebet is a situation when a bettor can make a profit regardless of the outcome by placing one bet per each outcome with different bookmakers. This happens when different bookmakers have different odds for the same game due to either bookmakers’ differing opinions (statistics) on event outcomes or errors. We can find those errors by scraping different bookmakers.
Avoid ‘account limitation’: Bookmakers, in general, dislike people who are good at gambling (no matter how they win); that’s why some people who earn money in betting sites get limitations. This means that you’d only bet a maximum amount of money per event set by the bookmaker — $5, $10, etc. If you start getting money with surebets, you may be seen as a ‘good bettor.’ To appear like an average person under bookmakers’ radars, experience bettors do this:Use many bookmakers: Create accounts in different bookmakers and spread your bets around them. It’ll be harder to identify you as a smart player in this way.
Round your stake: Although in the example I gave, I used decimal numbers; you shouldn’t do this just because most people don’t bet like that. Avoid decimal numbers at any cost and do your best to round your stake to the nearest number of five. If the formula gives you $47, then bet either $45 or $50 instead.
Lets say you want to find the price of an item on an eCommerce website. Normally, you will visit the website, search for the item and then scroll until you find the item.
But now let’s say you want to do this for thousands of items, perhaps across multiple websites. Maybe you are starting your own business and you want to keep track of the going prices for a variety of items. Manually checking prices on all of them is going to be very time consuming. To help you do this work faster, you can write a web scraper.
So how does this work?
When you visit a website with your browser, a server sends you some files, and the browser then renders them into pages that look nice and are easy for a human to use (hopefully). But you don’t need a browser to ask for those files. You can also write a computer program that requests those files. A web scraper (usually) will not render those files into pretty, usable pages, but instead load them into a format that makes them easy for a machine to read extremely quickly.
At that point, you can scan all of the files for all of the prices, and do whatever you like with them. You could average them and output a number. Or output the minimum and maximum prices. Or output the prices of the highest rated listings for whatever product you are curious about. Or feed the numbers to a graphing library that visualizes the data. Or put them into an Excel sheet. The possibilities are endless!
Some websites are hostile to this practice, however, and make you jump through hoops to prove that you are a real user and not a computer program. This makes sense, because too many webscrapers crawling all over your website can slow your site down or crash it. It’s also a way for competitors to get real time data about you, and you may want to make it more difficult for them to do so.
Stock markets tend to react very quickly to a variety of factors such as news, earnings reports, etc. While it may be prudent to develop trading strategies based on fundamental data, the rapid changes in the stock market are incredibly hard to predict and may not conform to the goals of more short term traders. This study aims to use data science as a means to both identify high potential stocks, as well as attempt to forecast future prices/price movement in an attempt to maximize an investor’s chances of success. Read more…
Lead Generation is crucial for any business, without new leads to fill your sales funnel it’s impossible to acquire your customers and grow your company. Some businesses garner a lot of inbound interest so PPC or social media ads may be enough to generate leads. But what if your product or service is something that most people don’t specifically search for? This might be a new technology, a niche product or B2B services where very few people might use a search engine to find you. Read more ….
The good thing about this code is that you do not need to log into any Instagram account. Anyone can access publicly available posts on Instagram using the hashtag. For example if you want to see the posts for the hashtag #newyork, you can do so by using the following URL:
So what should you do instead? Code your program to login and use the sessions to ensure your cookies get sent with every request!
s = requests.Session() s.post("https://fakewebsite.com/login", login_data)for url in url_list: response = s.get(url)
It takes just a little extra work but it will save you time from having to constantly update the code.
Don’t DOS Websites: Not that type of DOS. I mean Denial Of Service. If you don’t think you are doing this you should read this section because I’m about to blow your mind. Writing a for loop to access a website is a DOS.
Don’t Copy and Paste Reusable Code
Don’t Write Single Threaded Scrapers: Note that more threads doesn’t always mean better performance. This is because all these threads live on the same core. Confusing I know but this is something you will likely come across in testing.
Don’t Use the Same Pattern for Scraping: Many websites will ban you if you do the same thing over and over again. There are some strategies you can use to circumvent this.
Web scraping doesn’t have to be hard. The best thing you can do for yourself is build good tools that you can reuse and your web scraping life will be much easier. If you need assistance with a web scraping project feel free to reach out to me on twitter as I do consulting.
Wordometers is a website that provides data on live world statistics, and is the website we are going to scrape. Specifically we are going to scrape world population data that is in a table (seen below). Scraping data from a table is one of the most used forms of web scraping because most often then not the data we need in tables are not downloadable. So instead of getting the data manually we let a computer do it in mere seconds.
Beautiful Soup is one of the most powerful web scraping libraries and in my opinion the easiest to learn which is why were going to use it.
There are a few convenient and useful OCR tools in the Text Scanner such as below:
1. Images OCR
2. Screenshot OCR
3. Table OCR
4. Scanner/Digital Camera
All the OCR tools above can provide a different type of OCR conversions to help users from different file formats on different devices.
1. Extract Text from PDF.
2. Extract Text from Image.
3. Extract Text from Screenshot.
4. Extract Excel from Image.
5. Scan Text from Camera or Scanner.
What etiquette should web scrapers follow? – Web scraping code of conduct:
Scraping for your own personal use: no-one cares. Just make sure to throttle the process so you don’t hammer a website to the point it becomes a DDoS attack.
I’m not sure if there is any real law against scraping, but there are licensing issues regarding data published. If someone is paying for a data provider, and you scrape that data, that may not be legal for you to collect and redistribute.
Web Scraping with Python: from Fundamentals to Practice
How do deal with https-domains with SSL certificates in BeautifulSoup? And please don’t say use verify = False:
BeautifulSoup is a library for pulling data out of HTML and XML. You have to make a request using another library(e.g. requests) to get HTML content of the page and pass it to BeautifulSoup for extracting useful information.
I haven’t faced with any problems during scraping HTTPs sites using requests lib.
For anyone who goes with requests as your HTTP client, I would highly recommend adding requests-cache for a nice performance boost.
Why does Python not separate data into columns when exporting web scraping results to .csv?
Make sure to set the separator to , (I think the default is ;?).
Also, you should use BeautifulSoup(page.text) instead of BeautifulSoup(page.content). If you give it bytes rather than text, BeautifulSoup has to guess the text encoding, which is slow and can produce incorrect results.
And at the end, remember to call soup.decompose() to let python free up the memory.
How do I turn web scraping into a business?
Start by identifying the problem your service can solve. Eg, e-commerce companies wanting real time data on retail trends in their space, or financial firms wanting data on hiring trends gleaned from jobs postings, etc. If you can show how your tool addresses that problem better or cheaper than the current solution, and thus creates value and $ for your audience, you’ve got a business.
Is it possible to do web scraping without using any third-party modules?
Uh, of course you can. Here I wrote this just for you. I tried to make it slightly realistic so I gave it some error handling, a stopping point, absolute URL handling, and multithreading.
I think the first barrier you’ll run into with this is Python’s native HTML parser is very strict about what valid HTML is so it won’t interpret things the same way your web browser will. For that, I suggest using lxml as a parser (but that is a third-party module).
from collections import deque
from html.parser import HTMLParser
from threading import Lock
from urllib.error import HTTPError
from urllib.parse import urljoin
from urllib.request import urlopen
from concurrent.futures import ThreadPoolExecutor
NUMBER_OF_THREADS = 10
MAX_DEPTH = 3
TARGET_URL = r"https://www.reddit.com/r/Python/comments/v89fm9/is_it_possible_to_do_web_scraping_without_using/"
def __init__(self, url=None):
self.links = 
self.url = url
def handle_starttag(self, tag, attrs):
if tag == "a":
if "href" not in dict(attrs):
href = dict(attrs)["href"]
# Convert relative links to absolute links
href = urljoin(self.url, href)
Get the content of a URL.
except HTTPError as e:
def parse_html(html, url=None):
Parse the HTML of a web page.
parser = MyHTMLParser(url)
def handle(url, depth, callback, lock):
Handle a web page.
html = get_html(url)
links = parse_html(html, url).links
# Lock when printing to the terminal to avoid two threads printing at the same time
for link in links:
# Lock when adding to the queue to avoid two threads adding to the queue at the same time
callback((depth + 1, link))
def crawl(url, max_depth):
Crawl a web page.
seen = set()
crawling = deque([(0, url)])
lock = Lock()
with ThreadPoolExecutor(max_workers=NUMBER_OF_THREADS) as executor:
tasks = 
depth, url = crawling.popleft()
# If the depth is equal to the maximum depth, skip the URL (remember depth starts at 0)
if depth == max_depth:
# If the URL has already been seen, skip it
if url in seen:
# Submit the task and add the task to the list of tasks
tasks.append(executor.submit(handle, url, depth, crawling.append, lock))
# If the queue is empty and we still have tasks, wait for them one by one until we have something to do
while tasks and not crawling:
if __name__ == "__main__":
How to Make Money From Web Scraping – To conclude:
Web scraping can be a great way to make money online. There are a few different ways to go about it, but one of the most popular is to scrap web pages for sport arbitrage. This involves looking for discrepancies in odds between different bookmakers and then placing bets accordingly. Another way to make money from web scraping is to create a dataset with Beautiful Soup, a Python-based tool for extracting data from HTML and XML documents. This can be used to create a database of products for an ecommerce site, or to generate leads for a sales team. Finally, it’s also possible to scrape images from websites. This can be useful for creating memes or for other creative purposes. However, it’s important to follow the etiquette of web scraping and only scrape data that is publicly available. Otherwise, you could face legal action.
Web scraping can also be used to supplement your main income. In order to make money from web scraping, you will need to find a reliable source of data. One of the best places to find data for web scraping is Worldometers. This website provides a wealth of information on a variety of topics, and it is constantly updated with new data. Another great place to find data for web scraping is Beautiful Soup. Python is one of the best programming languages for web scraping, and it is relatively easy to learn. Once you have learned how to use Python for web scraping, you can start generating leads or collecting data for research purposes. Web scraping can be an extremely lucrative business, and it is a great way to make money online.
How to Make Money From Web Scraping? – Python Breaking News
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Below are the Top 10 legal side business that can make you $1000-$2000 a week. This list is based on my own experience and research. I have tried most of them and it takes dedication and passion to get there. Do your due diligence and make sure you have enough passion and patience to make it work.
Here are 10 legal side businesses that have the potential to make you $1000-$2000 a week in 2023:
Freelance writing or editing: If you have strong writing or editing skills, you could offer your services as a freelance writer or editor. You could write blog posts, articles, marketing materials, or other types of content for clients.
Graphic design: If you have experience with graphic design, you could offer your services to create logos, brochures, business cards, or other types of marketing materials for clients.
Virtual assistance: As a virtual assistant, you could provide administrative, technical, or creative support to clients remotely.
Social media management: If you have experience with social media marketing, you could offer your services to manage the social media accounts of businesses or individuals.
Website design or development: If you have experience with website design or development, you could offer your services to create or update websites for clients.
Pet sitting or dog walking: If you love animals, you could offer pet sitting or dog walking services to busy pet owners.
Personal training or coaching: If you have experience in fitness or coaching, you could offer personal training or coaching services to clients.
Event planning: If you enjoy planning events, you could offer your services to plan weddings, parties, or other types of events for clients.
Photography: If you have experience with photography, you could offer your services to take photos for events, businesses, or individuals.
Tutoring or teaching: If you have expertise in a particular subject, you could offer tutoring or teaching services to students.
These are just a few examples of legal side businesses that could potentially make you $1000-$2000 per week. The key to success in any side business is to offer high-quality services and to market your business effectively to reach potential clients.
Become an amateur team sports referee and officiate about 20 to 30 games per week. I did it myself and it works. You make extra cash and stay in shape and meet a lot of people( hot girls playing soccer or basketball included).
14- Organize sports tournaments (soccer, basketball, hockey): Rent good and inexpensive fields, convince friends to create teams, run a great campaign and organize amateur sport tournaments monthly and you can easily make $5000 after expenses if you do it right. This is not easy though, you must know local players and team captains and convince them to join.
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How to get to My saved Payment Buttons in Paypal?
As a small business owner who uses paypal for transactions, I always find it hard to get to my saved payment buttons in paypal to edit them.
I don’t know for the life of me why Paypal makes it so difficult and don’t document it. The only thing I can think of is that Paypal doesn’t want merchants to use those buttons anymore and want us to upgrade to business accounts where they can a monthly fee.
I have decided to blog about it to help my fellow small business owners who work hard and to also help myself, because whenever i find it, i always forget how i did it the next time.
As of October 2018, this is what i do to get to my saved payment buttons in paypal.
Log into your paypal account and go to Profile on the top left of the page.
Click on Profile and Settings
Click on “my Selling Tools” on the left menu
Click on Manage Selling on the right et voila….
They might change it again in the future to confuse us, but I will update it again if it happens.
Paypal already gets a fee on all transaction made on our site and they want to squeeze more by forcing us to upgrade. Don’t get fouled…..and stay woke.