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
tod
topic
prepare
slides
ae
ae_sa
hw
hw_sa
lab
lab_sa
exam
1
Mon
Jun 3
Morning
Welcome to INFO 2950
Mon
Jun 3
Afternoon
Meet the toolkit
Mon
Jun 3
Lab
Hello data science!
Tue
Jun 4
Morning
Grammar of graphics
Tue
Jun 4
Afternoon
Visualizing various types of data
Tue
Jun 4
Lab
Data visualization
Wed
Jun 5
Morning
Grammar of data wrangling
Wed
Jun 5
Afternoon
Working with multiple data frames
Wed
Jun 5
Lab
Thu
Jun 6
Morning
Tidying data
Thu
Jun 6
Afternoon
Data types and classes
Thu
Jun 6
Lab
Data tidying
Fri
Jun 7
Morning
Importing and recoding data
Fri
Jun 7
Afternoon
Recoding data + rowwise/columnwise operations
Fri
Jun 7
Lab
2
Mon
Jun 10
Morning
Getting data from the web: Scraping
Mon
Jun 10
Afternoon
Functions
Mon
Jun 10
Lab
Tue
Jun 11
Morning
Iteration
Tue
Jun 11
Afternoon
Getting data from the web: APIs
Tue
Jun 11
Lab
Functions + iteration
Wed
Jun 12
Morning
Linear regression with a single predictor
Wed
Jun 12
Afternoon
Linear regression with multiple predictors
Wed
Jun 12
Lab
Thu
Jun 13
Morning
Models for discrete outcomes
Thu
Jun 13
Afternoon
Hypothesis testing with randomization
Thu
Jun 13
Lab
Fri
Jun 14
Morning
No class (exam 01)
Fri
Jun 14
Afternoon
No class (exam 01)
Fri
Jun 14
Lab
No class (exam 01)
3
Mon
Jun 17
Morning
Quantifying uncertainty with the bootstrap
Mon
Jun 17
Afternoon
Introduction to machine learning
Mon
Jun 17
Lab
Statistical inference
Tue
Jun 18
Morning
Build better training data
Tue
Jun 18
Afternoon
Tree-based inference and hyperparameter optimization
Tue
Jun 18
Lab
Wed
Jun 19
Morning
No class (Juneteenth)
Wed
Jun 19
Afternoon
No class (Juneteenth)
Wed
Jun 19
Lab
No class (Juneteenth)
Thu
Jun 20
Morning
Text analysis: fundamentals and sentiment analysis