Continuously learning is one of the most important things a Data Scientist should do.
But, finding top-quality resources is difficult.
In this post, I will show you the top 6 resources you should look into.
1. SQL Tutorial for Data Scientists & Data Analysts
This small and free SQL tutorial goes through the most important SQL commands and concepts. The perfect resource when you want to brush up your SQL skills.
2. Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
One of the best books when it comes to learning fundamental statistic concepts and applying them to Python and R.
3. Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications
This book teaches you concepts on how to build production-grade Machine Learning systems. This involves the whole process from your data to deploying and operating a model.
4. Storytelling with Data
The best result is worth nothing when presented poorly. This book shows you how to choose an effective visual, eliminate clutter, and take your data storytelling skills to the next level.
5. Clean Code in Python
When starting with learning Data Science, some will only learn the basics of Python and jump straight to the specific tools. However, it is also important to have a deeper understanding of Python, but also about Design Patterns and Clean Code. This book will help you to become a better Python programmer by covering topics like Design Patterns, Software Architecture, Decorators, and more
6. Data Analysis with Polars
If you are a data practitioner, please remove Pandas from your toolkit and replace it with Polars. It is much faster than Pandas and can handle data of larger volumes. I love Polars, but one downside is that there aren’t many resources for it (unlike for Pandas). This course gives you the perfect introduction with many tricks and tips to get started with Polars.
Conclusion
I hope you will find the time to go through those resources, they are true gems.
Happy learning!