This repository contains projects and assignments for my Applied Data Science course taught by Dr. Amir Hesam Salavati.
In this course, we cover the end-to-end data analysis workflow - from data gathering and cleaning to visualization and reporting.
The projects apply data science techniques to solve real-world problems using Python. We explore supervised learning algorithms like decision trees and neural networks as well as unsupervised learning methods.
The assignments give hands-on practice going through the stages of data preparation, exploratory analysis, model building, evaluation, and communication of results. Topics touched on include:
- Classification
- Regression
- Clustering
- Dimension reduction
- Deep neural networks
The goal is to gain broad experience with the data science pipeline rather than specialize in one area.
This repository documents my work through course lab sessions and homeworks. Each contains the related problem description, Jupyter notebooks, visualizations, and writeup.
Together they showcase my progress in acquiring practical data analysis skills. I’m very thankful to Professor Salavati for teaching this applied introduction to data science.