Title

Topics

Introduction to Data Science

Course Overview and Introduction

Environment Setup and Tools

Introduction to Python for Data Science

Data Fundamentals

Data Manipulation with Pandas Data Collection and Web Scraping Data Quality and Preparation

Data Processing and Text Analysis

Advanced Data Collection

Text Data Processing

Data Preprocessing Techniques

Data Visualization and Analysis

Data Types and Visualization Techniques

Exploratory Data Analysis

Basic Statistical Analysis

Mathematical Foundations

Visualization with Kernel Density Estimators

Linear Algebra for Data Science

Statistical Foundations

Dimensionality Reduction

Matrix Decompositions

SVD and PCA

Feature Engineering Basics

Probability and Statistics

Probability Fundamentals Distributions and MLE 

Naïve Bayes Classification

Review and Assessment

Course Review 

Midterm Examination

Midterm Exam 

Review of key concepts 

Recitation: Exam preparation

Regression Analysis

Linear Regression 

Gradient Descent 

Feature Engineering

Lab 3 Due 

Mid-Project Released 

Quiz 4 

Recitation: Regression practice

Classification Techniques

Classification Fundamentals Logistic Regression Advanced Classification Methods

Support Vector Machines 

Neural Networks introduction Recitation: Classification practice

Advanced Classification

Multi-class Classification Ensemble Methods Class Imbalance Problems

Unsupervised Learning

Clustering Analysis

Dimensionality Reduction

Anomaly Detection

Clustering algorithms

Outlier detection methods

Pattern recognition

Recommender Systems

Recommendation Algorithms

Collaborative Filtering 

Content-Based Filtering

Deep Learning and LLMs

Introduction to Deep Learning Neural Networks 

Large Language Models in Data Science

Applications in data labeling 

Data generation techniques 

Final project discussions