Calendar

MATH 2530 Introductory Data Science, Ohio University, Spring 2022-23

Homework and Lab links will launch directly into Ohio University’s JupyterHub. 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 17: Lab Lab 1: Expressions
Jan 18: Chapter 1. Data Curation; 1.1. Types and Sources of Data
Jan 20: 1.2. Collecting Data; Homework Homework 1: Types and Sources of Data and Python Expressions

Week 2

Jan 23: 1.3. Organizing and Standardizing Data; 1.3.1. Introduction to Python
Jan 24: Lab Lab 2: Strings, Arrays, Tables
Jan 25: 1.3.2. Organizing in Tables
Jan 27: 1.3.3. Scaling and Standardizing data; Homework Homework 2: Collecting Data; Arrays and Tables

Week 3

Jan 30
Jan 31: Lab Lab 3: More on Tables
Feb 1: 1.4. Cleaning data; 1.4.1. More Python
Feb 3: Homework Homework 3: Table Manipulation; 1.4.2. Introduction to Pandas

Week 4

Feb 6: 1.4.3. Data Wrangling
Feb 7: Lab Lab 4: Functions, Sets, and Cleaning Data
Feb 8
Feb 10: Homework Homework 4: Wrangling the Vote

Week 5

Feb 13: Chapter 2. Enhanced data visualization; 2.1. Traditional Plots
Feb 14: Test on Chapter 1
Feb 15: 2.2. Single vs. Two or More Variables
Feb 17: Homework Homework 5: Visualization; 2.3. Dynamic Visualization

Week 6

Feb 20: 2.4. Distribution Maps
Feb 21: Lab Part of Lab 6 and Homework 6: World Progress
Feb 22: 2.5. Time Series Plots
Feb 24: Homework The rest of Lab 6 and Homework 6: World Progress; 2.6. Misleading Graphs

Week 7

Feb 27
Feb 28: Lab Lab 7: More Visualization, Transformations, and KDEs
Mar 1
Mar 3: Homework Homework 7: Bike Sharing

Week 8

Mar 6: Chapter 3. Statistical models, estimation, and prediction; 3.1. Statistical Model
Mar 7: Test through Chapter 2
Mar 8: 3.2. Estimator and Sampling Distribution
Mar 10: Homework Homework 8: Probability, Simulation, and Estimation; (March 13-17 is Spring Break.)

Week 9

Mar 20: 3.3. Simulation-Based Estimation and Prediction
Mar 21: Lab Lab 9: Simulations
Mar 22
Mar 24: Homework Homework 9: Assessing Models, Binary Distributions, Parameters and Statistics

Week 10

Mar 27
Mar 28: Lab Lab 10: Normal Distribution and Variance of Sample Means
Mar 29: Chapter 4. Machine learning and statistical learning; 4.1. Machine Learning; 4.2. Supervised Learning
Mar 31: Homework Homework 10: Linear Regression; (Withdraw deadline)

Week 11

Apr 3
Apr 4: Test through Chapter 3
Apr 5
Apr 7: Homework Homework 11: Classification

Week 12

Apr 10
Apr 11: Lab Part of Lab 12 and Homework 12: Movie Classification
Apr 12
Apr 14: Homework The rest of Lab 12 and Homework 12: Movie Classification

Week 13

Apr 17: 4.3. Unsupervised Learning
Apr 18: Lab Lab 13: k-Means Clustering
Apr 19: Chapter 5. Data ethics; 5.1. Laws and Regulations; 5.2. Unfair Discrimination and Social Bias
Apr 21: Homework Homework 13: Data Ethics: Laws and Discrimination

Week 14

Apr 24: 5.3. Transparency of Methods and Analysis; 5.4. Sampling Bias
Apr 25: Test through Chapter 4
Apr 26
Apr 28: Homework Homework 14: Data Ethics: Transparency and Sampling Bias

Week 15

May 3: Wednesday: Final exam, 12:20-2:20pm, in our regular classroom.