This page contains an outline of the topics, content, and assignments for the semester. Note that this schedule will be updated as the semester progresses and the timeline of topics and assignments might be updated throughout the semester.
week
dow
date
what
topic
prepare
slides
ae
ae_sa
hw
hw_sa
lab
lab_sa
exam
project
notes
1
Mon
Aug 21
Lec 1
Welcome to INFO 5001
Wed
Aug 23
Lec 2
Meet the toolkit
Fri
Aug 25
Lab 0
Hello data science!
lab00
2
Mon
Aug 28
Lec 3
Grammar of graphics
Wed
Aug 30
Lec 4
Visualizing various types of data
Fri
Sep 1
Lab 1
Data visualization
lab01 + hw01
3
Mon
Sep 4
No class (Labor Day)
Wed
Sep 6
Lec 5
Grammar of data wrangling
Fri
Sep 8
Lab
Git workflows (basics + merge conflicts)
4
Mon
Sep 11
Lec 6
Working with multiple data frames
Wed
Sep 13
Lec 7
Tidying data
Fri
Sep 15
Lab 2
Data wrangling
lab02 + hw02
5
Mon
Sep 18
Lec 8
Data types and classes
Wed
Sep 20
No class (at a conference)
Fri
Sep 22
Lab 3
Data tidying
lab03
6
Mon
Sep 25
Lec 9
Importing and recoding data
Wed
Sep 27
Lec 10
Recoding data + rowwise/columnwise operations
Fri
Sep 29
Lab 4
Git workflows (branches + PRs)
lab04 + hw03
7
Mon
Oct 2
Lec 11
Getting data from the web: Scraping
Wed
Oct 4
Lec 12
Functions
Fri
Oct 6
Lab
Develop project proposals
proj-proposal
8
Mon
Oct 9
No class (Fall Break)
Wed
Oct 11
Lec 13
Iteration
Fri
Oct 13
Lab 5
Functions + iteration
lab05 + hw04
9
Mon
Oct 16
Lec 14
Getting data from the web: APIs
Wed
Oct 18
Lec 15
Rectangling data
Fri
Oct 20
Lab
Develop project exploration
proj-explore
10
Mon
Oct 23
Lec 16
Debugging tips + tools
Wed
Oct 25
Lec 17
Reproducible project-based workflows
Fri
Oct 27
No class (exam)
exam
11
Mon
Oct 30
Lec 18
Customizing Quarto reports and presentations
Wed
Nov 1
Lec 19
Introduction to machine learning
Fri
Nov 3
Lab
No class (community restorative day)
proj-draft
12
Mon
Nov 6
Lec 20
Build better training data
Wed
Nov 8
Lec 21
Tree-based inference and hyperparameter optimization
Fri
Nov 10
Lab 6
Implement machine learning workflows
lab06 + hw05
13
Mon
Nov 13
Lec 22
Text analysis: fundamentals and sentiment analysis