Data Analysis with Python
freeCodeCamp.org
Learn data analysis with Python! This free course covers NumPy, Pandas, data cleaning, and visualization. Start your data science journey today!
More resources on Data Science
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.
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.'
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.
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.
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.
Vectors | Chapter 1, Essence of Linear Algebra
Opening chapter of Grant Sanderson's animated linear algebra series, contrasting the physics, computer science and mathematics views of vectors. Shows vectors as arrows and coordinate lists, and how addition and scalar multiplication work geometrically, laying intuition for linear combinations and transformations.