Linear algebra has applications in many fields, including data science. Get started with these Khan Academy modules.
- Vectors: An introduction to vectors.
- Matrices: An introduction to matrices.
- Linear algebra: Vectors, subspaces, matrix transformations, orthogonality, and eigenvectors.
Boost your data analysis skills by developing a strong foundation in calculus.
- Limits and continuity: Definition and properties of limits.
- Derivatives: Introduction.
- Derivatives: Chain rule and other advanced topics.
- Applications of derivatives: Approximating function values and finding limits.
- Analyzing functions: Critical points, local/global extrema, optimization.
Learn how to analyze relationships between variables using regression.
- Linear regression: Inferring about slope in linear regression and transforming nonlinear data.
- ANOVA: Analysis of variance.
- Multivariate Gaussian distributions for machine learning and data mining: By Alexander Ihler, Professor at University of California, Irvine.
Visual Analytics and Machine Learning
Follow these IBM OpenDS4All modules on more advanced data science topics. Each module contains lecture slides, sample code in Jupyter Notebook, and homework problems.
- Visual Analytics: Slides and Jupyter Notebook
- Supervised Machine Learning—Linear and Logistic Regression: Slides and Jupyter Notebook
- Supervised Machine Learning—Artificial Neural Networks: Slides and Jupyter Notebook
- Training Robust Models: Slides and Jupyter Notebook
Also watch this video series on supervised machine learning by Columbia University M.S. in Data Science student Tomislav Galjanic.
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