What you will learn
- Linear algebra essentials (vectors, matrices, linear transformations)
- Differential calculus concepts critical for machine learning
- Gradient descent and optimization techniques
- Mathematical foundations of neural networks
- Practical applications in data modeling and analysis
- Python implementations of mathematical concepts
Program Overview
Linear Algebra for Data Science
⏱️ 3-4 weeks
- Vector/matrix operations and properties
- Eigenvalues/eigenvectors and their applications
- Singular Value Decomposition (SVD) for dimensionality reduction
Differential Calculus Foundations
⏱️ 4-5 weeks
- Multivariable functions and partial derivatives
- Chain rule in multiple dimensions
- Gradient vectors and directional derivatives
Optimization for Machine Learning
⏱️ 4-5 weeks
- Gradient descent algorithms (batch, stochastic)
- Convexity and loss functions
- Backpropagation mathematics
Applied Projects
⏱️ 3-4 weeks
- Implementing linear regression from scratch
- Building a basic neural network layer
- Optimization case studies
Get certificate
Job Outlook
- Essential for:
- Machine Learning Engineers (110K180K)
- Data Scientists (90K160K)
- Quantitative Analysts (100K200K+)
- Skills impact:
- 73% of ML roles require advanced calculus/linear algebra (LinkedIn 2023)
- 2.3× more interview callbacks for candidates demonstrating math fluency
Specification: Algebra and Differential Calculus for Data Science
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