Ruixiang Tang
Spring 2025 Introduction to Data Science
- Lectures: See details on Canvas.
- Instructor: Ruixiang Tang
- Recitation:
- See details on Canvas.
- Office Hour:
- Office Hours: Section 05 Tuesday 9am-10am (see zoom link on canvas)
- Office Hours: Section 06 Tuesday 10am-11am (see zoom link on canvas)
- Office Hours: Section 07 Wednesday 10am-11am (see zoom link on canvas)
- Office Hours: Section 08 Tuesday 10am-11am (see zoom link on canvas)
Course Overview
CS 439 Spring 2025 introduces foundational concepts and practical techniques in data science, equipping students with skills in data manipulation, visualization, and advanced machine learning methods. The course covers key topics such as data preprocessing, statistical analysis, regression, classification, clustering, recommender systems, and an introduction to deep learning and large language models (LLMs). Students will gain hands-on experience with tools like Python, Pandas, Seaborn, and TensorFlow through five labs, five quizzes, and two major projects. With a focus on real-world applications, this course emphasizes understanding mathematical foundations, dimensionality reduction, anomaly detection, and modern AI techniques, preparing students for practical and research-driven careers in data science.
Grading
5 Quizzes
5 Labs
3 Research Papers (also research paper-based quizzes)
Midterm Exam
Final Semester Project
Learning Resources
Course Videos on CUbits: https://www.cubits.ai/collections/48/
Course Schedule (tentative)
| Week# | Title | Topics | Notes |
|---|---|---|---|
Week 1 | Introduction to Data Science | Course Overview and Introduction Environment Setup and Tools Introduction to Python for Data Science | Tools: Pandas, NumPy Lab: Environment setup and basic Python Resources: Pandas cheat sheet, Plot tutorial |
Week 2 | Data Fundamentals | Data Manipulation with Pandas Data Collection and Web Scraping Data Quality and Preparation | Tools: BeautifulSoup Lab 1 Released Quiz 1 Recitation: Python and Pandas fundamentals |
Week 3 | Data Processing and Text Analysis | Advanced Data Collection Text Data Processing Data Preprocessing Techniques | Lab 1 Due Text analysis techniques Data transformation methods Recitation: Data manipulation practice |
Week 4 | Data Visualization and Analysis | Data Types and Visualization Techniques Exploratory Data Analysis Basic Statistical Analysis | Lab 2 Released Quiz 2 Tools: Seaborn, Matplotlib Recitation: Visualization techniques |
Week 5 | Mathematical Foundations | Visualization with Kernel Density Estimators Linear Algebra for Data Science Statistical Foundations | Lab 2 Due Mathematical concepts for ML Recitation: Linear algebra applications |
Week 6 | Dimensionality Reduction | Matrix Decompositions SVD and PCA Feature Engineering Basics | Lab 3 Released Advanced mathematical concepts Recitation: SVD and PCA practice |
Week 7 | Probability and Statistics | Probability Fundamentals Distributions and MLE Naïve Bayes Classification | Quiz 3 Statistical modeling concepts Recitation: Probability and statistics practice |
Week 8 | Review and Assessment | Course Review Midterm Examination | Midterm Exam Review of key concepts Recitation: Exam preparation |
Week 9 | Regression Analysis | Linear Regression Gradient Descent Feature Engineering | Lab 3 Due Mid-Project Released Quiz 4 Recitation: Regression practice |
Week 10 | Classification Techniques | Classification Fundamentals Logistic Regression Advanced Classification Methods | Support Vector Machines Neural Networks introduction Recitation: Classification practice |
Week 11 | Advanced Classification | Multi-class Classification Ensemble Methods Class Imbalance Problems | Mid-Project Due Lab 4 Released Advanced classification techniques Recitation: Advanced classification practice |
Week 12 | Unsupervised Learning | Clustering Analysis Dimensionality Reduction Anomaly Detection | Clustering algorithms Outlier detection methods Pattern recognition |
Week 13 | Recommender Systems | Recommendation Algorithms Collaborative Filtering Content-Based Filtering | Lab 4 Due Lab 5 Released Quiz 5 Recitation: Recommender systems practice |
Week 14 | 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 |