Instructor: Xiao Hui Tai (xtai [at] ucdavis [dot] edu)

TA: Oscar Rivera (ogrivera [at] ucdavis [dot] edu)

Class time:
Lectures are Mondays, Wednesdays and Fridays, 11-11:50 AM at Wellman Hall 226.
Discussions (labs) are run by Oscar Rivera on Thursdays 12:10-1 PM (Section A01), 1:10-2 PM (Section A02) at TLC 2212.

Office hours:
Oscar Rivera: Thursday 3-4 PM and Friday 1-2 PM at MSB 1117
Xiao Hui Tai: Wednesdays 1-2 PM at MSB 4242

Syllabus: here.

Piazza: here.

Textbooks: There are three required textbooks. They are all available online and are free.

  1. R for Data Science by Hadley Wickham, Mine Çetinkaya-Rundel and Garrett Grolemund. 2nd Edition, 2023. Available here.
  2. Introduction to Modern Statistics by Mine Çetinkaya-Rundel and Johanna Hardin. 2nd Edition, 2024. Available here.
  3. OpenIntro Statistics by David Diez, Mine Çetinkaya-Rundel and Christopher Barr. 4th Edition, 2019. Available here.

There will sometimes be additional (optional) reading from:

  1. Art of R Programming by Norman Matloff. 2011. (Look on Google)

Class Schedule

Week 1: Introduction, overview of data types

  • Wed Sept 25: Introduction and R Basics
    • Notes: Lecture 1
    • Reading:
      • R for Data Science Chapters 1, 28
  • Fri Sept 27: Overview of data types and data structures, vectors
    • Notes: Lecture 2
    • Reading:
      • R for Data Science Chapter 2
      • Additional reading: Matloff Chapter 2

Week 2: Overview of data types

  • Mon Sept 30: More on vectors, arrays
    • Notes: Lecture 3
    • Reading:
      • Additional reading: Matloff Chapter 3
  • Wed Oct 2: Arrays, matrices, lists
    • Notes: Lecture 4
    • Reading:
      • Additional reading: Matloff Chapters 3, 4
  • Fri Oct 4: Lists, data frames
    • Notes: Lecture 5
    • Reading:
      • Additional reading: Matloff Chapter 4, 5
    • Homework 1: Html qmd

Week 3: Data frames, data manipulation and data visualization tools

  • Mon Oct 7: Data frames, exploratory data analysis, data manipulation tools
    • Notes: Lecture 6
    • Reading:
      • R for Data Science Chapter 3
  • Wed Oct 9: Data manipulation tools
  • Fri Oct 11: Data visualization tools

Week 4: Data visualization tools, Descriptive statistics

  • Mon Oct 14: Data visualization tools, descriptive statistics

    • Notes: Lecture 9
    • Reading:
      • Open Intro Statistics Chapter 1

  • Wed Oct 16: Review

  • Fri Oct 18: Midterm 1

Week 5: Descriptive statistics

  • Mon Oct 21: Describing numerical distributions
    • Notes: Lecture 10
    • Reading:
      • Open Intro Statistics Chapter 2
  • Wed Oct 23: Numerical data
    • Notes: Lecture 11
    • Reading:
      • R for Data Science Chapter 10
  • Wed Oct 25: Categorical data

Week 6: Introduction to probability

  • Mon Oct 28: Introduction to probability
    • Notes: Lecture 13
    • Reading:
      • Open Intro Statistics Chapter 3.1
  • Wed Oct 30: Conditional, marginal and joint probability
    • Notes: Lecture 14
    • Reading:
      • Open Intro Statistics Chapter 3.2
  • Fri Nov 1: Bayes’ Theorem; Random variables

Week 7: Common distributions

  • Mon Nov 4: Continuous random variabnles, Bernoulli distribution
    • Notes: Lecture 16
    • Reading:
      • Open Intro Statistics Chapter 3.5, 4.3
  • Wed Nov 6: Binomial distribution
    • Notes: Lecture 17
    • Reading:
      • Open Intro Statistics Chapter 4.3
  • Fri Nov 8: Poisson distribution

Week 8

  • Mon Nov 11: Veterans Day

  • Wed Nov 13: Review

  • Fri Nov 15: Midterm 2

Week 9: Normal distribution, sampling distributions

  • Mon Nov 20: Normal distribution
    • Notes: Lecture 19
    • Reading:
      • Open Intro Statistics Chapter 4.1
  • Wed Nov 22: Sampling distributions
    • Notes: Lecture 20
    • Reading:
      • Open Intro Statistics Chapter 5.1
  • Fri Nov 24: Sample proportion, Confidence intervals
    • Notes: Lecture 21
    • Reading:
      • Open Intro Statistics Chapter 5.2