Calendar

MATH 2530 Introductory Data Science, Ohio University, Fall 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

Aug 22: Introduction
Aug 23: Lab Lab 01: Expressions
Aug 24: Chapter 1. Data Curation; 1.1. Types and Sources of Data
Aug 26: 1.2. Collecting Data; Homework Homework 01: Types and Sources of Data and Python Expressions

Week 2

Aug 29: 1.3. Organizing and Standardizing Data; 1.3.1. Introduction to Python
Aug 30: Lab Lab 02: Strings, Arrays, Tables
Aug 31: 1.3.2. Organizing in Tables
Sep 2: 1.3.3. Scaling and Standardizing data; Homework Homework 02: Collecting Data; Arrays and Tables; Project Project 1: World Progress

Week 3

Sep 5

Labor day holiday, no class

Sep 6

Lab Lab 03: More on Tables

Sep 7

1.4. Cleaning data

1.4.1 Introduction to Pandas

Datascience to Pandas Conversion

Sep 9

1.4.2. Data Wrangling

Homework Homework 03: Table Manipulation

Week 4

Sep 12
Sep 13: Lab Lab 04: Pandas Overview
Sep 14: Chapter 2. Enhanced data visualization; 2.1. Traditional Plots
Sep 16

Week 5

Sep 19: 2.2. Single vs. Two or More Variables; Homework Homework 04: Cleaning and Exploring Data with Pandas
Sep 20: Lab Lab 05: Functions and Visualizations
Sep 21
Sep 23: Test on Chapter 1

Week 6

Sep 26: 2.3. Dynamic Visualization; Homework Homework 05: Visualization
Sep 27: Lab Lab 06: Visualization, Transformations, and KDEs
Sep 28: 2.4. Distribution Maps
Sep 30: Fall break, no class

Week 7

Oct 3: 2.5. Time Series Plots
Oct 4: Lab Lab 07: Simulations
Oct 5: 2.6. Misleading Graphs
Oct 7: Homework Homework 07: Bike Sharing: Exploratory Data Analysis (EDA) and Visualization

Week 8

Oct 10: Chapter 3. Statistical models, estimation, and prediction; 3.1. Statistical Model
Oct 11: Lab Lab 08: Examining the Therapeutic Touch
Oct 12: 3.2. Estimator and Sampling Distribution
Oct 14: Homework Hw 08: Probability, Simulation, and Estimation

Week 9

Oct 17: Test through Chapter 2
Oct 18: Lab Lab 09: Normal Distribution and Variance of Sample Means
Oct 19: 3.3. Simulation-Based Estimation and Prediction
Oct 21: Homework Homework 09: Assessing Models, Binary Distributions, Parameters and Statistics

Week 10

Oct 24: Chapter 4. Machine learning and statistical learning; 4.1. Machine Learning
Oct 25: Lab Lab 10: Conditional Probability
Oct 26: 4.2. Supervised Learning
Oct 28: Homework Homework 10: Linear Regression; (Withdraw deadline)

Week 11

Oct 31
Nov 1: Lab Lab 11: Regression
Nov 2
Nov 4: Homework Homework 11: Classification

Week 12

Nov 7: Test through Chapter 3
Nov 8: Lab Labs 12 and 13: Movie Classification
Nov 9: 4.3. Unsupervised Learning
Nov 11: Veterans day holiday, no class

Week 13

Nov 14
Nov 15: Lab Labs 12 and 13: Movie Classification
Nov 16
Nov 18: Chapter 5. Data ethics; 5.1. Laws and Regulations; 5.2. Unfair Discrimination and Social Bias; Homework Homework 13: k-Means Clustering

Week 14

Nov 21: 5.3. Transparency of Methods and Analysis; 5.4. Sampling Bias
Nov 22: Lab as Lab 14, finish Project 1: World Progress
Nov 23: Thanksgiving holiday, no class
Nov 25: Thanksgiving holiday, no class

Week 15

Nov 28: Test through Chapter 4
Nov 29: Lab Lab 15: Data Ethics Concepts
Nov 30
Dec 2: Homework Homework 15: Data Ethics Scenarios

Week 16

Dec 7: Final exam, 12:20-2:20pm, in our regular classroom.