- Top 3 Statistical Paradoxes in Data Science [Silver Blog]
Observation bias and sub-group differences generate statistical paradoxes.
- Awesome Tricks And Best Practices From Kaggle [Gold Blog]
Easily learn what is only learned by hours of search and exploration.
- What’s ETL? [Silver Blog]
Discover what ETL is, and see in what ways it’s critical for data science.
- Top 10 Python Libraries Data Scientists should know in 2021 [Platinum Blog]
So many Python libraries exist that offer powerful and efficient foundations for supporting your data science work and machine learning model development. While the list may seem overwhelming, there are certain libraries you should focus your time on, as they are some of the most commonly used today.
- How to Succeed in Becoming a Freelance Data Scientist [Platinum Blog]
With recent growth in data science, now is the best time to get into freelancing. The following steps will help you get started with finding clients or help you improve your current strategy.
- More Data Science Cheatsheets [Platinum Blog]
It's time again to look at some data science cheatsheets. Here you can find a short selection of such resources which can cater to different existing levels of knowledge and breadth of topics of interest.
- Must Know for Data Scientists and Data Analysts: Causal Design Patterns [Silver Blog]
Industry is a prime setting for observational causal inference, but many companies are blind to causal measurement beyond A/B tests. This formula-free primer illustrates analysis design patterns for measuring causal effects from observational data.
- A Machine Learning Model Monitoring Checklist: 7 Things to Track [Gold Blog]
Once you deploy a machine learning model in production, you need to make sure it performs. In the article, we suggest how to monitor your models and open-source tools to use.
- 3 Mathematical Laws Data Scientists Need To Know [Gold Blog]
Machine learning and data science are founded on important mathematics in statistics and probability. A few interesting mathematical laws you should understand will especially help you perform better as a Data Scientist, including Benford's Law, the Law of Large Numbers, and Zipf's Law.
- The Ultimate Guide to Acing Coding Interviews for Data Scientists [Silver Blog]
This article covers understanding the 4 types of coding interview questions and preparing for them effectively.
- Top YouTube Channels for Data Science [Platinum Blog]
Have a look at the top 15 YouTube channels for data science by number of subscribers, along with some additional data on the channels to help you decide if they may have some content useful for you.
- Data Science Learning Roadmap for 2021 [Gold Blog]
Venturing into the world of Data Science is an exciting, interesting, and rewarding path to consider. There is a great deal to master, and this self-learning recommendation plan will guide you toward establishing a solid understanding of all that is foundational to data science as well as a solid portfolio to showcase your developed expertise.
- How Reading Papers Helps You Be a More Effective Data Scientist [Silver Blog]
By reading papers, we were able to learn what others (e.g., LinkedIn) have found to work (and not work). We can then adapt their approach and not have to reinvent the rocket. This helps us deliver a working solution with lesser time and effort.
- Telling a Great Data Story: A Visualization Decision Tree [Platinum Blog]
Pick your visualizations strategically. They need to tell a story.
- Data Science vs Business Intelligence, Explained [Platinum Blog]
Knowing the differences between the business intelligence and data science is more than just a matter of semantics.
- How to Get Data Science Interviews: Finding Jobs, Reaching Gatekeepers, and Getting Referrals [Silver Blog]
In this post, the author shares what to do to get job interviews efficiently. Find answers to these questions: Where should I look for data science jobs? How do I reach out to the gatekeeper? How do I get referrals? What makes a good data science resume?
- The Best Data Science Project to Have in Your Portfolio [Gold Blog]
If you are trying to find your first path into a Data Science career, then demonstrating the quality of your skills can be the greatest hurdle. While many standard projects exist for anyone to complete, creating an original data-driven project that attempts to solve some challenge is worth so much more. A good Data Scientist is one that can solve data-related questions, and a great Data Scientist poses original data-related questions and then solves.
- Essential Math for Data Science: Introduction to Matrices and the Matrix Product [Silver Blog]
As vectors, matrices are data structures allowing you to organize numbers. They are square or rectangular arrays containing values organized in two dimensions: as rows and columns. You can think of them as a spreadsheet. Learn more here.
- Build Your First Data Science Application [Silver Blog]
Check out these seven Python libraries to make your first data science MVP application.
- How to Get Your First Job in Data Science without Any Work Experience [Platinum Blog]
Creativity, grit, and perseverance will become the three words you live by.
- 3 Ways Understanding Bayes Theorem Will Improve Your Data Science [Silver Blog]
Mastery of the mathematics and applications of this intuitive statistical concept will advance your credibility as a decision maker.