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A well-written and thorough book can be an amazing path to build deeper understanding of Data Science and also act as a handbook as you discover the internet's vast resources.
These are our and our experts top picks to get started building career-relevant skills.
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.
ISL is a fundamental book and popular amongst undergrad and grad students for its clarity and simplicity with explaining concepts. The math required to understand the book is kept to a minimum, making it unique in its format.
Whether you're a student, a professional, or just curious about statistical analysis, Head First's brain-friendly formula helps you get a firm grasp of statistics so you can understand key points and actually use them. Learn to present data visually with charts and plots; discover the difference between taking the average with mean, median, and mode, and why it's important; learn how to calculate probability and expectation; and much more.
This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science.
Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides.
Statistical methods are a key part of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.
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These days, courses are no longer a sequence of videos. They are usually accompanied by projects and a learning community, keeping you accountable and on the path.
Our experts recommend these courses, from free selections to paid programs.
With the motto "making neural nets uncool again", fast.ai is a straight-to-the-point practical (and free!) course that is valued by Machine Learning enthusiasts and engineers worldwide. Fast.ai comes with a community, many practical projects and great content.
Math is the foundation of Machine Learning and much needed if you need to work on the inner logic of its systems. Senior engineers are encouraged to propose and submit their own papers – and getting your LinAlg back in order is a must for that.
Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more!
Successfully perform all steps in a complex Data Science project, read statistical software output for created models and receive professional step-by-step coaching in the space of Data Science
To become an expert data scientist you need practice and experience. By completing this course you will get an opportunity to apply and gain knowledge in R data analysis. This final project will test your skills in data visualization, probability, inference and modeling, data wrangling, data organization, regression, and machine learning.
Kirill Eremenko's course on Udemy is a classic with almost a million (!) students worldwide. A-Z takes you from a bit of coding knowledge to making your own predictions and building ML models pretty swiftly. At prices between $10 - $20 it's also cheaper than many alternatives.
Lex Fridman is the instructor of an immensely popular and fundamental Deep Learning course at MIT. Together with the other MIT AI courses, this can help polish your skills and get the foundations right.
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