A concise, structured collection of the key mathematical concepts I encountered during my Master’s in Data Science at IIT Chicago.
Many of you may be asking:
- “Is the cost worth it—especially if I study abroad?”
- “What new and unique material will I actually learn?”
Here’s my take:
While the tuition investment can be steep, a Data Science program pushes your mathematical understanding to new heights. Courses in regression, machine learning, and data preprocessing introduce you to eye-opening concepts you might never explore in depth—unless a deadline forces you to!
- Deepens Conceptual Clarity
- Understanding the “why” behind algorithms helps you choose the right tool for each problem.
- Broadens Your Perspective
- Real-world assignments reveal nuances you won’t encounter in standard math classes.
- Builds Practical Skills
- From deriving cost functions to implementing gradient descent, you learn to connect theory with code.
In this repository, you’ll find organized notes and code examples on topics such as:
- Linear & Logistic Regression
- Regularization (Lasso, Ridge)
- Dimensionality Reduction (PCA, SVD)
- Clustering & Mixture Models
- Optimization Techniques
- Probability & Information Theory
- Matrix Decompositions & Eigenanalysis
Feel free to browse the folders, clone the repo, and dive into the notebooks!
I’m happy to help—just send me a message:
- Email: voonadhanvanth183@gmail.com
- LinkedIn: dhanvanth-voona
“Learning never stops—let’s explore the math together!”