| WEEK | DATE | READING | TOPIC | MATERIALS |
|---|---|---|---|---|
1 |
Mon, Apr 1 | Optional Reading: |
π» Lab00: Intro to R and Dataframes |
|
| Tue, Apr 2 | π§βπ« Lec01: Intro to Datascience |
π Lec00 Slides π Lec01 Slides | ||
| Thu, Apr 4 | π R4DS, Chapter 5: Data tidying |
π§βπ« Lec02: Tidy Data |
π Lec02 Slides | |
2 |
Mon, Apr 8 | π» Lab01: Tidy Data and Databases |
||
| Tue, Apr 9 | π R4DS, Chapter 1: Data visualization |
π§βπ« Lec03: Graphics, Part I |
π Lec03 Slides | |
| Wed, Apr 10 |
| |||
| Thu, Apr 11 | π R4DS, Chapter 11: Communication |
π§βπ« Lec04: Graphics, Part II |
π Lec04 Slides | |
| Sun, Apr 14 | Homework 01 Due | |||
3 |
Mon, Apr 15 | π» Lab02: Statistical Visualizations |
||
| Tue, Apr 16 | π§βπ« Lec05: Exploratory Data Analysis |
|||
| Wed, Apr 17 |
| |||
| Thu, Apr 18 | π§βπ« Lec06: Sampling Techniques and Study Design |
π Lec06 Slides | ||
| Sun, Apr 21 | Mini-Project 01 Due | |||
4 |
Mon, Apr 22 | π» Review for ICA01 (NOT TURNED IN) |
||
| Tue, Apr 23 | π§βπ« Lec07: |
|||
| Wed, Apr 24 | ||||
| Thu, Apr 25 | π§βπ« In-Class Assessment 01 |
|||
5 |
Mon, Apr 29 | π» Lab03: RegEx |
||
| Tue, Apr 30 | π§βπ« Lec09: Statistics, Part I |
π Lec09 Slides | ||
| Wed, May 1 | Lab03 Due | |||
| Thu, May 2 | π Selected Chapters from IMS: βStatistical Inferenceβ |
π§βπ« Lec10: Statistics, Part II |
π Lec10 Slides | |
| Sun, May 5 | Homework 02 Due | |||
6 |
Mon, May 6 | π» Lab04: Two- and Multi-sample Tests |
||
| Tue, May 7 | π Selected Chapters from IMS: βRegression Modelingβ |
π§βπ« Lec11: Introduction to Statistical Modeling and Regression |
π Lec11 Slides | |
| Wed, May 8 | Lab04 Due | |||
| Thu, May 9 | π Selected Chapters from IMS: βRegression Modelingβ |
π§βπ« Lec12: More Regression |
π Lec 12 Slides | |
| Sun, May 12 | Mini-Project 02 Due | |||
7 |
Mon, May 13 | π» Lab05: Regression |
||
| Tue, May 14 | π TBD |
π§βπ« Lec13: Regression, Part III |
π Lec 13 Slides | |
| Wed, May 15 | Lab05 Due | |||
| Thu, May 16 | π TBD |
π§βπ« Lec14: PCA |
π Lec 14 Slides | |
| Sun, May 19 | ||||
8 |
Mon, May 20 | π» Lab06: PCA/KDE, and Review for ICA02 |
||
| Tue, May 21 | π TBD |
π§βπ« Lec15: Finishing up PCA; Review for ICA02 |
π Lec 15 Slides | |
| Wed, May 22 | No Lab Due | |||
| Thu, May 23 | π§βπ« In-Class Assessment 02 |
|||
9 |
Mon, May 27 | HOLIDAY: No Lab or Sections |
||
| Tue, May 28 | π TBD |
π§βπ« Lec17: PCA, Part III |
π Lec 17 Slides | |
| Wed, May 29 | ||||
| Thu, May 30 | π TBD |
π§βπ« Lec18: Classification, and Nonparametrics |
π Lec 18 Slides | |
10 |
Mon, Jun 3 | π» Lab07: Open OH for MP03 |
||
| Tue, Jun 4 | π TBD |
π§βπ« Lec19: KDE, and Clustering |
**Mini-Project 03 Due**"> βπ Lec 19 Slides | |
| Wed, Jun 5 | NO Lab 07 due; everyone will just receive a 100% | |||
| Thu, Jun 6 | π R4DS, Chapter 18: Missing Values |
π§βπ« Lec20: Classification, Clustering, and Missingness |
βπ Lec 20 Slides | |
Finals |
Tue, Jun 11 | Final Project Due |
PSTAT 100: Data Science Concepts and Analysis
Course Schedule and Calendar
Course Schedule
Note
This page will be updated as we progress through the quarter; please check back regularly for updates!
Note
Please try to complete the readings before coming to the specified lecture/starting the specified lab.
Textbook Abbreviations and Emoji Meanings
- R4DS = R for Data Science
- I2R = An Introduction to R
- IMS = Introduction to Modern Statistics, 2nd Ed.
- π» = Lab
- π§βπ« = Lecture
- π = Textbook Reading
- π = Paper Reading