Calendar

MATH 2530 Introductory Data Science, Ohio University, Spring 2025-26

Homework and Lab files will be loaded into the Ohio Supercomputer Center. If you do not have access to that, then you can see them in the class public repository.

Many things will fill in or be modified as we go along, so this calendar should always be considered a draft.

Week 1

Jan 12: Chapter 1. Data Curation; 1.1. Types and Sources of Data
Jan 14: 1.2. Collecting Data
Jan 15: Lab Lab 1: Expressions
Jan 16: 1.3. Organizing and Standardizing Data; 1.3.1. Introduction to Python; Homework Homework 1: Types and Sources of Data and Python Expressions

Week 2

Jan 19: No Class
Jan 21: 1.3.2. Organizing in Tables
Jan 22: Lab Lab 2: Strings, Arrays, Tables
Jan 23: Homework Homework 2: Collecting Data; Arrays and Tables

Week 3

Jan 26: 1.3.3. Scaling and Standardizing data
Jan 28: 1.4. Cleaning data; 1.4.1. More Python
Jan 29: Lab Lab 3: More on Tables
Jan 30: Homework Homework 3: Table Manipulation; 1.4.2. Introduction to Pandas

Week 4

Feb 2: 1.4.3. Data Wrangling
Feb 4
Feb 5: Lab Lab 4: Functions, Sets, and Cleaning Data
Feb 6: Homework Homework 4: Wrangling the Vote

Week 5

Feb 9: Chapter 2. Enhanced data visualization; 2.1. Traditional Plots
Feb 11: 2.2. Single vs. Two or More Variables
Feb 12: Test on Chapter 1
Feb 13: Homework Homework 5: Visualization; 2.3. Dynamic Visualization

Week 6

Feb 16: 2.4. Distribution Maps
Feb 18: 2.5. Time Series Plots
Feb 19: Lab Part of Lab 6 and Homework 6: World Progress
Feb 20: Homework The rest of Lab 6 and Homework 6: World Progress; 2.6. Misleading Graphs

Week 7

Feb 23
Feb 25
Feb 26: Lab Lab 7: More Visualization, Transformations, and KDEs
Feb 27: Homework Homework 7: Bike Sharing

Week 8

Mar 2: Chapter 3. Statistical models, estimation, and prediction; 3.1. Statistical Model
Mar 4: 3.2. Estimator and Sampling Distribution
Mar 5: Test through Chapter 2
Mar 6: Homework Homework 8: Probability, Simulation, and Estimation; (March 09-13 is Spring Break.)

Week 9

Mar 16: 3.3. Simulation-Based Estimation and Prediction
Mar 18
Mar 19: Lab Lab 9: Simulations
Mar 20: Homework Homework 9: Assessing Models, Binary Distributions, Parameters and Statistics

Week 10

Mar 23
Mar 25: Chapter 4. Machine learning and statistical learning; 4.1. Machine Learning; 4.2. Supervised Learning
Mar 26: Lab Lab 10: Normal Distribution and Variance of Sample Means
Mar 27: Homework Homework 10: Linear Regression; (Withdraw deadline)

Week 11

Mar 30
Apr 1
Apr 2: Test through Chapter 3
Apr 3: Homework Homework 11: Classification

Week 12

Apr 6
Apr 8
Apr 9: Lab Part of Lab 12 and Homework 12: Movie Classification
Apr 10: Homework The rest of Lab 12 and Homework 12: Movie Classification

Week 13

Apr 13: 4.3. Unsupervised Learning
Apr 15: Chapter 5. Data ethics; 5.1. Laws and Regulations; 5.2. Unfair Discrimination and Social Bias
Apr 16: Lab Lab 13: k-Means Clustering
Apr 17: Homework Homework 13: Data Ethics: Laws and Discrimination

Week 14

Apr 20: 5.3. Transparency of Methods and Analysis; 5.4. Sampling Bias
Apr 22
Apr 23: Test through Chapter 4
Apr 24: Homework Homework 14: Data Ethics: Transparency and Sampling Bias

Week 15

Apr 29: Wednesday: Final exam, 80:00-10:00 am, in our regular classroom.