Skip to main content
BookintermediatePaid

The Data Science Handbook

by Field Cady · Field Cady

Field Cady's survey of the working data scientist's toolkit, spanning Python and R programming, statistics, machine learning algorithms, big-data systems and the softer craft of framing problems and communicating results to non-technical stakeholders.

Visit resource

This link may earn us a small commission at no extra cost to you. Affiliate disclosure

More resources on Data Science

CourseFree

Data Analysis with Python

Learn data analysis with Python! This free course covers NumPy, Pandas, data cleaning, and visualization. Start your data science journey today!

CourseFree

Practical Deep Learning for Coders

fast.ai's free course teaching deep learning top-down: you train working image, text, and tabular models in the first lessons, then work back to the underlying mechanics. Assumes about a year of coding experience, uses PyTorch and the fastai library.

VideoFree

StatQuest with Josh Starmer - Neural Networks videos

Josh Starmer's StatQuest provides clear, concise, and often humorous explanations of statistical and machine learning concepts. His videos on neural networks break down complex ideas into easily digestible 'quests.'

CourseFree

Kaggle Learn

Free interactive micro-courses from Kaggle, each a few hours of short lessons with in-browser coding exercises. Topics include Python, pandas, data visualization, SQL, feature engineering and introductory machine learning, giving learners working code skills for basic data analysis and modelling.

CourseFree

Harvard University's CS109 Data Science

Harvard's two-semester data science course with public lectures, labs and homework. CS109A covers data collection, cleaning, visualization and regression and classification models; CS109B adds deep learning and probabilistic methods. Students learn to run a full analysis in Python.

BookPaid

Practical Statistics for Data Scientists

A concise statistics book written for programmers that covers sampling, experimental design, A/B testing, regression, classification, and resampling, with R code throughout. Readers learn which statistical ideas matter for data science and how sampling and study design shape the conclusions data can support.

See all Data Science resources →