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No Bullshit Guide to Math and Physics


From Amazon: “Often calculus and mechanics are taught as separate subjects. It shouldn’t be like that. Learning calculus without mechanics is incredibly boring. Learning mechanics without calculus is missing the point. This textbook integrates both subjects and highlights the profound connections between them. This is the deal. Give me 350 pages of your attention, and I’ll teach you everything you need to know about functions, limits, derivatives, integrals, vectors, forces, … Continue Reading

Mathematics: A Very Short Introduction


From Amazon: “The aim of this book is to explain, carefully but not technically, the differences between advanced, research-level mathematics, and the sort of mathematics we learn at school. The most fundamental differences are philosophical, and readers of this book will emerge with a clearer understanding of paradoxical-sounding concepts such as infinity, curved space, and imaginary numbers. The first few chapters are about general aspects of mathematical thought. These are … Continue Reading

Statistics Done Wrong: The Woefully Complete Guide


From Amazon: “Scientific progress depends on good research, and good research needs good statistics. But statistical analysis is tricky to get right, even for the best and brightest of us. You’d be surprised how many scientists are doing it wrong. Statistics Done Wrong is a pithy, essential guide to statistical blunders in modern science that will show you how to keep your research blunder-free. You’ll examine embarrassing errors and omissions in … Continue Reading

Tales of Science and Data


This is an ebook written as a series of Jupyter notebooks, “intended as a collection of personally elaborated materials on Data Science. Topics span a quite large spectrum in the Data Science field: nothing will ever be fully comprehensive, but the purpose is keeping this continuously updated. Learning never ends!” Content includes: Probability & Statistics Machine Learning: Supervised Learning Unsupervised Learning Artificial Neural Networks Model Assessment Natural Language Processing Computer … Continue Reading

Python for Informatics: Exploring Information

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From Amazon: “This book is designed to introduce students to programming and computational thinking through the lens of exploring data. You can think of Python as your tool to solve problems that are far beyond the capability of a spreadsheet. It is an easy-to-use and easy-to learn programming language that is freely available on Windows, Macintosh, and Linux computers. There are free downloadable copies of this book in various electronic … Continue Reading

Data Wrangling with Python


From O’Reilly: “How do you take your data analysis skills beyond Excel to the next level? By learning just enough Python to get stuff done. This hands-on guide shows non-programmers like you how to process information that’s initially too messy or difficult to access. Through various step-by-step exercises, you’ll learn how to acquire, clean, analyze, and present data efficiently. You’ll also discover how to automate your data process, schedule file- … Continue Reading

Text Mining with R: A Tidy Approach


From Amazon: “Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext and other tidy tools in R can make text analysis easier and … Continue Reading

R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics


From Amazon: “With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression. Each recipe addresses … Continue Reading

Python Data Science Handbook: Essential Tools for Working with Data


From Amazon: “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. Working scientists and data crunchers familiar with reading and writing Python code will find this … Continue Reading

Data Science from Scratch: First Principles with Python


From Amazon: “Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get … Continue Reading