CSC 405/605/705 - Fall 2026 – Schedule
The following gives a day-by-day breakdown of topics covered, readings assigned, and assignment handouts/due dates.
The schedule in this class is flexible, and past dates will be updated to reflect what was actually covered. Future dates are always tentative and subject to change. Slides, assignments, and the associated reading materials will be posted as the course progresses.
Day 1: Wednesday, August 19
Topics 1: Course Information Slides
Day 2: Monday, August 24
Preparation: Students must have a Python environment installed before class, including pip. Installing Jupyter Notebook in advance is also recommended.
Reading: How to Use Jupyter Notebook: A Beginner’s Tutorial
Topics 1: Introduction of Data Science Slides
Topics 2: Introduction of Jupyter Notebook Slides Demo
Day 3: Wednesday, August 26
Preparation: Students should install Git and register a GitHub account before class.
Reading: GitHub Introduction
Topics 1: Complete the Jupyter Notebook demo from the previous class.
Topics 2: Introduction of Git & GitHub Slides
Day 4: Monday, August 31
Topics: Re/Introduction to Python: Basic syntax, keywords, variables, and data types, Numerical operations, conditional statements, loop control Slides Demo 1 Demo 2
Day 5: Wednesday, September 2
Assignment: You can start Assignment 1
Topics: Re/Introduction to Python: Using Python to read from and write to files (TXT and CSV files) and perform basic operations Slides Dataset for in-class exercise Demo In-class Exercise
No class on Monday, September 7 – Labor Day holiday
Day 6: Wednesday, September, 9
Preparation: Students must have Pandas library installed before class.
Topics: Introduction to Pandas: Series and DataFrame Slides Demo
Day 7: Monday, September 14
Topics: Introduction to Pandas: Read from and write to files (CSV and Excel files) Slides Dataset for in-class exercise Demo
Day 8: Wednesday, September 16
Remind: Assignment 1 Due
Topics: Introduction to Pandas: Data cleaning, including removing missing values, outliers, duplicates, and inconsistent data formats. (Slides continue from last class) Demo
Day 9: Monday, September 21
Preparation: Students must have NumPy library installed before class.
Topics: Introduction to NumPy: Understanding and creating ndarray, Basic functions such as slicing, indexing, reshape, split, and resize, as well as basic math and arithmetic functions. Slides Demo
Day 10: Wednesday, September 23
Midterm Grades Due on Sep 25
Quiz 1
Day 11: Monday, September 28
Topics: Introduction to Statistics in Data Science: Basic Terminology, different types of Statistical Methods, Statistical Measures for Descriptive Statistics.
Day 12: Wednesday, September 30
Topics: Introduction to Statistics in Data Science: Probability foundation
Day 13: Monday, October 5
Topics: Introduction to Statistics in Data Science: Distributions for continuous variables and discrete variables.
Day 14: Wednesday, October 7
Assignment: You can start Assignment 2
Topics: Introduction to Statistics in Data Science: Central Limit Theorem, Probability Density Estimation.
No class on October 9 – October 13 (Fall break)
Day 15: Wednesday, October 14
Topics: Introduction to Statistics in Data Science: Correlation Measures.
Day 17: Monday, October 19
Topics: Introduction to Statistics in Data Science: Hypothesis Testing.
Day 18: Wednesday,October 21
Remind: Assignment 2 Due
Topics: Introduction to Visualization: Different Types of Basic Charts.
Day 19: Monday, October 26
Quiz 2
Day 20: Wednesday, October 28
Remind: Project Proposal Submission Due on Oct 30
Topics: Introduction to Visualization: Advanced Types of Charts.
Day 21: Monday, November 2
Preparation: Students must have Matplotlib and Seaborn libraries installed before class.
Topics: Introduction to Visualization: code implementation and in-class exercises.
Day 22: Wednesday, November 4
Preparation: Students must have streamlit library installed before class.
Reading: https://docs.streamlit.io/get-started/installation
Topics: Introduction to AI-assistant Data Analysis: Introduction to Streamlit.
Day 23: Monday, November 9
Preparation: Students must have langchain library installed before class.
Reading: https://docs.langchain.com/oss/python/langchain/install
Topics: Introduction to AI-assistant Data Analysis: Introduction to LangChain.
Day 24: Wednesday, November 11
Topics: Introduction to AI-assistant Data Analysis: Build project with LLMs.
Day 25: Monday, November 16
Topics: Introduction to Data Modeling.
Day 26: Wednesday, November 18
Topics: Introduction to Data Modeling.
Day 27: Monday, November 23
Quiz 3
*No class on November 25 – November 29 - Thanksgiving holiday
Day 28: Monday, November 30
Remind: Final Project Report Submission Due
Topics: Students Give Presentation
Day 29: Wednesday, December 2
Remind: Research Report (Graduate Students Only) submission Due
Topics: Students Give Presentation