PSTAT 100: Data Science Concepts and Analysis

Course Material

Instructor
Quarter

Ethan Marzban

Spring 2024

Acknowledgements: Special thanks to previous instructors Dr. Trevor Ruiz, Dr. Alex Franks, and Dr. Laura Baracaldo for graciously providing material and guidance for this course.


Readings:

Optional Reading:

Lectures:

  • Tues. Lecture 1: Course Introduction; Intro to Data

  • Thurs. Lecture 2: Data, Part II

Lab:

There is no required lab for this week, as we will not be having any Sections on Monday. However, please keep in mind the following:

  • If you have never programmed in R before, please read Chapter 2: Some R basics and Chapter 3: Data in R, from “An Introduction to R”, and Chapters 2 and 27 from R4DS.

    • This Lab provides a summary of important information about the basics of programming in R.

  • If you have some exposure to programming in R (or have already read Chapters 2 and 3 from I2R), but would like more practice with dataframes, we encourage you to complete the following lab: [Click Here]

Please note that, by virtue of being prerequisite, these concepts and topics are potentially testable on In-Class Assignments (and are also crucial for success in our future PSTAT 100 endeavors!).


Textbook Abbreviations

  • R4DS: R for Data Science
  • LDS: Learning Data Science
  • I2R: An Introduction to R
Week Topic Lab Homework Project
1 Introduction to Data
2 Statistical Graphics L1 HW01
3 Exploring and Cleaning Data L2 MP01
4 Sampling Techniques L3
5 Missingness, and KDE L4 HW02
6 Principal Components Analysis, and an Intro to Statistics L5 MP02
7 Introduction to Statistical Modeling L6 HW03
8 Regression L7
9 More Regression MP03
10 Classification and Clustering L8
11 Finals Final Proj.
  • L: lab
  • HW: Homework
  • MP: Mini-Project