STA 199: Introduction to Data Science

Section 2 - Dr. Elijah Meyer

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_sa hw hw_sa lab lab_sa exam project notes
1 M 28 August Lab 0 Hello R! Release Lab-0
Tu 29 August Lec-1 Welcome to STA 199
Th 31 August Lec-2 Meet the toolkit
Fri 1 September


2 M 4 September No Lab
Release HW-1
Tu 5 September Lec-3 Data visualization
Th 7 September Lec-4 Visualizing various types of data
Fri 8 September


3 M 11 September Lab 1 Data visualization Lab Release Lab-1
Tu 12 September Lec-5 Grammar of data wrangling Due HW-1 / Release HW-2
W 13 September

Due Lab-1
Th 14 September Lec-6 Grammar of data wrangling II
Fri 15 September


4 M 18 September Lab 2 Data wrangling Release Lab-2
Tu 19 September Lec-7 Working with multiple data frames Due HW-2 / Release HW-3
W 20 September

Due Lab-2
Th 21 September Lec-8 Tidying data
Fri 22 September


5 M 25 September Lab 3 Data tidying Release Lab-3
Tu 26 September Lec-9 Data types and classes Due HW-3; Due Lab-3 (5:00 PM)
W 27 September


Th 28 September Lec-10 Importing and recoding data + Exam 1 Review Release Exam 1
Fri 29 September


6 M 2 October No Lab Work on Exam 1 Due: Exam 1 11:59 PM
Tu 3 October Lec-11 Data science ethics 1
Th 5 October Lec-12 Data science ethics 2 Release HW-4
Fri 6 October


7 M 9 October Lab 4 Merge Conflicts + Ethics Release Lab-4
Tu 10 October Lec-13 The language of models
W 11 October

Due Lab-4
Th 12 October Lec-14 Models with a single predictor
Fri 13 October

Due HW-4
8 M 16 October No Lab No Lab (Fall Break)
Tu 17 October
No Class (Fall Break)
Th 19 October Lec-15 Finish models with a single predictor + additive MLR
Fri 20 October


9 M 23 October Lab Work on Project Proposal
Tu 24 October Lec-16 Finish additive MLR + interaction models Release HW-5
Th 26 October Lec-17 Model Selection
Fri 27 October


10 M 30 October Lab 5 Predicting a numerical outcome Release Lab-5; Due Project Proposal
Tu 31 October Lec-18 Logistic Regression I
W 1 November

Due Lab-5
Th 2 November Lec-19 Probability
Fri 3 November


11 M 6 November Lab 6 Logistic Regression Lab Release Lab-6; Due: HW-5
Tu 7 November Lec-20 Finish Logistic + Intro to Hypothesis Testing
W


Due Lab-6
Th 9 November Lec-21 Hypothesis testing via simulation
Fri 10 November


12 M 13 November Lab 7 Project work day
Tu 14 November Lec-22 Quantifying uncertainty
W 15 November

Due: Draft Report
Th 16 November Lec-23 Talk more about Confidence Intervals Release Exam 2
Fri 17 November


13 M 20 November No Lab Work on Exam 2 Due: Exam 2 11:59 PM
Tu 21 November Lec-24 Customizing Quarto reports and presentations
Th 23 November No Class

Fri 24 November


14 M 27 November Lab Peer Review Peer Review Due
Tu 28 November Lec-24 Communicating data science results effectively
W



Th 30 November Lec-25 Work on project feedback day
Fri 1 December


15 M 4 December Lab Project Presentations
Tu 5 December Lec-26 Open Office Hours (in-class)
Th 7 December Lec-27 Interactive web apps with R and Shiny with Dr. Colin Rundel
Fri 8 December

Due: Final Report; HW-6