INFO 2950: Introduction to Data Science

Modified

April 13, 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 Tue Jan 23 Lec 1 Welcome to INFO 2950 hw-00
Thu Jan 25 Lec 2 Meet the toolkit
Fri Jan 26 Lab Hello data science! lab-00
2 Tue Jan 30 Lec 3 Grammar of graphics
Thu Feb 1 Lec 4 Visualizing various types of data
Fri Feb 2 Lab Data visualization lab-01 + hw-01
3 Tue Feb 6 Lec 5 Grammar of data wrangling
Thu Feb 8 Lec 6 Working with multiple data frames
Fri Feb 9 Lab Git workflows (basics + merge conflicts) lab-git + hw-02
4 Tue Feb 13 Lec 7 Tidying data
Thu Feb 15 Lec 8 Data types and classes
Fri Feb 16 Lab Data tidying lab-02
5 Tue Feb 20 Lec 9 Importing and recoding data
Thu Feb 22 Lec 10 Recoding data + rowwise/columnwise operations
Fri Feb 23 Lab Git workflows (branches + PRs) lab-03
6 Tue Feb 27
No class (February Break)
Thu Feb 29 Lec 11 Getting data from the web: Scraping
Fri Mar 1 Lab Develop project proposals proj-proposal + hw-03
7 Tue Mar 5 Lec 12 Functions
Thu Mar 7 Lec 13 Iteration
Fri Mar 8 Lab Functions + iteration lab-04 + hw-04
8 Tue Mar 12 Lec 14 Getting data from the web: APIs
Thu Mar 14 Lec 15 Rectangling data
Fri Mar 15 Lab No class (Exam 1) exam-01
9 Tue Mar 19 Lec 16 Linear regression with a single predictor
Thu Mar 21 Lec 17 Linear regression with multiple predictors
Fri Mar 22 Lab Develop project exploration proj-explore + hw-05
10 Tue Mar 26 Lec 18 Models for discrete outcomes
Thu Mar 28 Lec 19 Customizing Quarto reports and presentations
Fri Mar 29
Mid-semester review
11 Tue Apr 2
No class (Spring Break)
Thu Apr 4
No class (Spring Break)
Fri Apr 5
No class (Spring Break)
12 Tue Apr 9 Lec 20 Hypothesis testing with randomization
Thu Apr 11 Lec 21 Quantifying uncertainty with the bootstrap
Fri Apr 12 Lab Statistical inference lab-05 + hw-06
13 Tue Apr 16 Lec 22 Introduction to machine learning
Thu Apr 18 Lec 23 Build better training data
Fri Apr 19 Lab Work on project drafts
14 Tue Apr 23 Lec 24 Tree-based inference and hyperparameter optimization
Thu Apr 25 Lec 25 Unsupervised machine learning
Fri Apr 26 Lab Project peer reviews proj-peer + hw-07
15 Tue Apr 30 Lec 26 Text analysis: fundamentals and sentiment analysis
Thu May 2 Lec 27 Text analysis: supervised text classification
Fri May 3 Lab Project presentations proj-present + hw-08
16 Tue May 7 Lec 28 Wrap-up: Where to go from here proj-final
Sat May 18
Exam 02 - 2-4:30pm exam-02