Kaggle Learn
Kaggle
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.
More resources on Python for Data Science
numpy-financial – Financial Functions for Python
Official documentation for numpy-financial, the NumPy project's package of elementary financial functions split out of NumPy itself. It covers net present value, internal rate of return, payments, interest rates and future value, so you can compute loan and investment cash-flow figures in Python.
Scikit-learn Official Documentation
Official user guide, API reference and worked examples for scikit-learn, the standard Python machine learning library. Covers classification, regression, clustering, dimensionality reduction, pipelines, preprocessing and model selection, so readers can build, tune and evaluate models with cross-validation.
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.
Effective Pandas 2: Opinionated Patterns for Data Manipulation
Opinionated guide to idiomatic pandas 2: chaining operations instead of intermediate variables, choosing correct dtypes, avoiding SettingWithCopy problems, and writing pipelines that stay readable and testable. Aimed at people who already use pandas but produce tangled code.
Pandas Tutorials (Corey Schafer)
Video series building from installation and loading CSV files through selecting rows and columns, filtering, updating data, sorting, grouping and aggregating, and handling missing values, each demonstrated live in a notebook. Suits people who prefer coding along to reading documentation.
Data 100 Course Notes: Principles and Techniques of Data Science (UC Berkeley)
Berkeley's upper-division course notes: pandas, regex and data cleaning, EDA, visualization, sampling, SQL, linear and logistic regression, gradient descent, cross-validation, regularization, and PCA. Supplies the statistical reasoning behind the Python code that tutorials usually skip.