Data science can be an overwhelming field. Many people will tell you that you can’t become a data scientist until you master the following: statistics, linear algebra, calculus, programming, databases, distributed computing, machine learning, visualization, experimental design, clustering, deep learning, natural language processing, and more. That’s simply not true.
So, what exactly is data science? It’s the process of asking interesting questions and then answering those questions using data. Generally speaking, the data science workflow looks like this:
- Ask a question
- Gather data that might help you to answer that question
- Clean the data
- Explore, analyze, and visualize the data
- Build and evaluate a machine learning model
- Communicate results
This workflow doesn’t necessarily require advanced mathematics, a mastery of deep learning, or many of the other skills listed above. But it does require knowledge of a programming language and the ability to work with data in that language. And although you need mathematical fluency to become good at data science, you only need a basic understanding of mathematics to get started.
The other specialized skills listed above may indeed one day help you to solve data science problems. However, you don’t need to master all of those skills to begin your career in data science. You can begin today, and I’m here to help you!
Let’s start with Free Books.
1- Data Science at the Command Line by Jeroen Janssens: https://datascienceatthecommandline.com/2e/index.html
2- Deep Learning on Graphs by Yao Ma and Jiliang Tang: https://web.njit.edu/~ym329/dlg_book/dlg_book.pdf
3- Hands-on Machine Learning with Scikit-learn, Keras and Tensorflow by Aurelien Geron: https://tinyurl.com/2ydcjc9p
4- Practical Statistics for Data Science by Peter Bruce & Andrew Bruce
6-Learning Deep Architectures for AI by Yoshua Bengio: https://lnkd.in/gHNKMzE2
7- Python for Data Science Handbook by Jake VanderPlas: https://lnkd.in/bxTAdNY
8- The Hundred-Page Machine Learning Book by Andriy Burkov:https://lnkd.in/gdbbUuPH
9- A Course in Machine Learning by Hal Daumé III: https://lnkd.in/gDr2C7qi
10- Intuitive ML and Big Data in C++, Scala, Java, and Python by Kareem Alkaseer: https://lnkd.in/eVanhXm
11- Python Notes for Professionals book: https://lnkd.in/g2cNnFjJ
12- Learning Pandas https://lnkd.in/gM9C2BvN
13- Machine Learning — A First Course for Engineers and Scientists by Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten, and Thomas B. Schön: https://lnkd.in/gzuNxKi3
14- Dive into Deep Learning by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola: https://d2l.ai/d2l-en.pdf
15- A Comprehensive Guide to Machine Learning Soroush Nasiriany, Garrett Thomas, William Wang, Alex Yang, Jennifer Listgarten, Anant Sahai: https://lnkd.in/gp3AKgMY
16- SQL Notes for Professionals book: https://lnkd.in/g5dNZCuD
17-Algorithms Notes for Professionals book: https://lnkd.in/eX6YkWv
18- Deep Learning Interviews: Hundreds of fully solved job interview questions from a wide range of key topics in AI by Shlomo Kashani, Amir Ivry: https://lnkd.in/gMFVTbrn
19- Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David : https://lnkd.in/gEJGTfB7
Most of them are wikis, please consider buying the book or donating to support the authors if you like them