What you will learn
- Master matrix operations and their computational efficiency in data tasks
- Understand vector spaces and transformations for dimensionality reduction
- Apply eigenvalues/eigenvectors to principal component analysis (PCA)
- Learn singular value decomposition (SVD) for recommendation systems
- Implement linear algebra concepts in Python using NumPy
- Solve real-world data problems like image compression and NLP embeddings
Program Overview
Foundations of Linear Algebra
⏱️ 3-4 weeks
- Vectors, matrices, and tensor fundamentals
- Matrix multiplication and inversion
- Solving systems of linear equations
- Computational complexity considerations
Matrix Decompositions
⏱️ 4-5 weeks
- LU and QR decompositions
- Eigendecomposition theory and applications
- Singular Value Decomposition (SVD) deep dive
- Practical implementations in Python
Applications in Data Science
⏱️ 4-6 weeks
- PCA for dimensionality reduction
- Linear regression through matrix formulations
- Word embeddings and latent semantic analysis
- Image processing with matrix transformations
Advanced Topics
⏱️ 2-3 weeks
- Tensors for deep learning
- Graph theory adjacency matrices
- Sparse matrix optimizations
Get certificate
Job Outlook
- Critical for:
- Machine Learning Engineers (85% use daily)
- Computer Vision Specialists
- NLP Engineers
- Quantitative Researchers
- Salary Impact:
- Professionals with demonstrated linear algebra skills earn 15-20% more (2023 Data)
- Industry Demand:
- Listed as required skill in 92% of senior data scientist positions
Specification: Essential Linear Algebra for Data Science
|
