INFO 2950: Introduction to Data Science

Modified

February 22, 2024

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 Tu Jan 23 Lec 1 Welcome to INFO 2950 hw-00
Th Jan 25 Lec 2 Meet the toolkit
F Jan 26 Lab Hello data science! lab-00
2 Tu Jan 30 Lec 3 Grammar of graphics
Th Feb 1 Lec 4 Visualizing various types of data
F Feb 2 Lab Data visualization lab-01 + hw-01
3 Tu Feb 6 Lec 5 Grammar of data wrangling
Th Feb 8 Lec 6 Working with multiple data frames
F Feb 9 Lab Git workflows (basics + merge conflicts) lab-git + hw-02
4 Tu Feb 13 Lec 7 Tidying data
Th Feb 15 Lec 8 Data types and classes
F Feb 16 Lab Data tidying lab-02
5 Tu Feb 20 Lec 9 Importing and recoding data
Th Feb 22 Lec 10 Recoding data + rowwise/columnwise operations
F Feb 23 Lab Git workflows (branches + PRs) lab-03
6 Tu Feb 27
No class (February Break)
Th Feb 29 Lec 11 Getting data from the web: Scraping
F Mar 1 Lab Develop project proposals proj-proposal + hw-03
7 Tu Mar 5 Lec 12 Functions
Th Mar 7 Lec 13 Iteration
F Mar 8 Lab Functions + iteration lab-04 + hw-04
8 Tu Mar 12 Lec 14 Getting data from the web: APIs
Th Mar 14 Lec 15 Rectangling data
F Mar 15 Lab No class (Exam 1) exam-01
9 Tu Mar 19 Lec 16 Probability: Review
Th Mar 21 Lec 17 Language of models
F Mar 22 Lab Develop project exploration proj-explore + hw-05
10 Tu Mar 26 Lec 18 Linear regression with a single predictor
Th Mar 28 Lec 19 Linear regression with multiple predictors
F Mar 29
Modeling with a numeric outcome lab-05 OR hw-06
11 Tu Apr 2
No class (Spring Break)
Th Apr 4
No class (Spring Break)
F Apr 5
No class (Spring Break)
12 Tu Apr 9 Lec 20 Models for discrete outcomes
Th Apr 11 Lec 21 Quantifying uncertainty with the bootstrap
F Apr 12 Lab Logistic regression + bootstrap lab-06 + hw-06
13 Tu Apr 16 Lec 22

Th Apr 18 Lec 23 Introduction to machine learning
F Apr 19 Lab

14 Tu Apr 23 Lec 24 Build better training data
Th Apr 25 Lec 25 Tree-based inference and hyperparameter optimization
F Apr 26 Lab Project peer reviews proj-peer + hw-07
15 Tu Apr 30 Lec 26 Text analysis: fundamentals and sentiment analysis
Th May 2 Lec 27 Text analysis: supervised text classification
F May 3 Lab Project presentations proj-present
16 Tu May 7 Lec 28 Wrap-up: Where to go from here
NA


exam-02 + proj-final