- How I Doubled My Income with Data Science and Machine Learning [Gold Blog]
Many career opportunities exist in the ever-expanding domain of data. Finding your place -- and finding your salary -- is largely up to your dedication, focus, and drive to learn. If you are an aspiring Data Scientist or have already started your professional journey, there are multiple strategies for maximizing your earning potential.
- A checklist to track your Data Science progress [Silver Blog]
Whether you are just starting out in data science or already a gainfully-employed professional, always learning more to advance through state-of-the-art techniques is part of the adventure. But, it can be challenging to track of your progress and keep an eye on what's next. Follow this checklist to help you scale your expertise from entry-level to advanced.
- Data Scientist, Data Engineer & Other Data Careers, Explained [Platinum Blog]
In this article, we will have a look at five distinct data careers, and hopefully provide some advice on how to get one's feet wet in this convoluted field.
- Data Preparation in SQL, with Cheat Sheet! [Gold Blog]
If your raw data is in a SQL-based data lake, why spend the time and money to export the data into a new platform for data prep?
- Rebuilding My 7 Python Projects [Silver Blog]
This is how I rebuilt My Python Projects: Data Science, Web Development & Android Apps.
- Why You Should Consider Being a Data Engineer Instead of a Data Scientist [Silver Blog]
A new king of the jungle has emerged.
- Data Science Books You Should Start Reading in 2021 [Gold Blog]
Check out this curated list of the best data science books for any level.
- How to ace A/B Testing Data Science Interviews [Silver Blog]
Understanding the process of A/B testing and knowing how to discuss this approach during data science job interviews can give you a leg up over other candidates. This mock interview provides a step-by-step guide through how to demonstrate your mastery of the key concepts and logical considerations.
- How to organize your data science project in 2021 [Gold Blog]
Maintaining proper organization of all your data science projects will increase your productivity, minimize errors, and increase your development efficiency. This tutorial will guide you through a framework on how to keep everything in order on your local machine and in the cloud.
- 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.