Must-Read Data Science Books Part 2 📚 | Top Picks for Beginners & Experts

Books for Data Science Part - 2: 5. Data Science from Scratch by Joel Grus published by O’Reilly. The second edition of this book is already released and it has been a popular book due to the fact that it encounters various fundamentals altogether in this single book. Starting from Crash course on Python, Visualizing Data, Linear Algebra and Statistics, Probability, Hypothesis and Inference, Getting and working with data and many more topics relating to data along with Machine learning, Neural Nets, Recommender systems, Network Analysis and whatnot involved as well. It’s a full package deal and you should definitely consider giving it a read. 6. R for Data Science by Hadley Wickham & Garrett Grolemund published by O’ Reilly. Okay, so I admit that I love working with python and have only mentioned the books based on Python for Data Science. But this book is for people who like or what you give ‘R’ programming language a shot. I have tried this language and it’s nice but most of the work is related to python so I never really considered shifting my programming language to R. This book breaks that bias, I really enjoyed reading and implementing this book while I was learning ‘R’. You should definitely read this book if you are thinking of doing something fun or new in data science like learning a new language for Data science tasks. The books will tell you all about it. Definitely worth checking out. 7. Think Stats by Allen B. Downey published by O’ Reilley. Think Stats is a prologue to Probability and Statistics for Python software engineers and Data Scientists (if you already are not familiar with these topics in detail). Think Stats underlines straightforward methods that you can use to explore real data sets and answer intriguing problems. The book presents a contextual analysis utilizing data from the National Institutes of Health. If you have essential skills in Python, you can utilize them to learn ideas in probability and statistics. Think Stats depends on a Python library for probability distributions. Many exercises included uses short programs to run various experiments and help readers develop a strong understanding. Most of the books don’t cover Bayesian statistics yet Think Stats depends on the possibility that Bayesian techniques are too critical to even consider delaying. By exploiting the PMF and CDF libraries (used for probability distribution), it is feasible for amateurs to gain proficiency with the ideas and take care of testing issues. 8. Build a Career in Data Science by Emily Robinson and Jacqueline Nolis This one’s quite different from the other two books we talked about earlier, mostly focusing on the technical stuff related to learning data science. Published in 2020, this one teaches you how to the data science industry works. And that’s why it’s a must-read. Emily and Jacqueline take us through a ride, starting from deciding if a data science career is for us to different types of data science jobs in the market and the end-to-end process of getting a job. For example, the 60% rule can help you decide which jobs you qualify and should apply to. That’s right — the book covers ideally everything on what you’ll need to become a data scientist. What I loved the most was the fact that it doesn’t stop there. There’s a lot of advice for people like me who already have the job to settle and grow in our data science role. By now, I have reread this book multiple times based on what level I’m on my data science journey. I can tell the authors had done extensive research to write this book with experts on multiple domains and companies. Keep this as your career guide. Link to Download Books: https://linktr.ee/python_soldiers