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

TA: Jedidiah Harwood (jedharwood [at] ucdavis [dot] edu)

Class time:
Lectures are Mondays, Wednesdays and Fridays, 11-11:50 AM at Wellman Hall 216.
Discussions (labs) are run by Jedidiah Harwood on Thursdays 12:10-1 PM (Section A01), 1:10-2 PM (Section A02) at TLC 2212.

Office hours:
Jedidiah Harwood: Tuesdays and Thursdays 10-11 AM at MSB 1117
Xiao Hui Tai: Mondays 2:30-3:30 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 and Garrett Grolemund. 1st Edition, 2017. Available here.
  2. Introduction to Modern Statistics by Mine Çetinkaya-Rundel and Johanna Hardin. 1st Edition, 2021. Available here.
  3. OpenIntro Statistics by David Diez, Mine Cetinkaya-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 27: Introduction and R Basics
    • Notes: Lecture 1
    • Reading:
      • R for Data Science Chapters 1, 2, 27
  • Fri Sept 29: Overview of data types and data structures, vectors
    • Notes: Lecture 2
    • Reading:
      • R for Data Science Chapter 4, 20
      • Additional reading: Matloff Chapter 2

Week 2: Overview of data types

  • Mon Oct 2: More on vectors, arrays
    • Notes: Lecture 3
    • Reading:
      • R for Data Science Chapter 20
      • Additional reading: Matloff Chapter 3
  • Wed Oct 4: Arrays, matrices, lists
    • Notes: Lecture 4
    • Reading:
      • R for Data Science Chapter 20
      • Additional reading: Matloff Chapters 3, 4
  • Fri Oct 6: Lists, data frames, exploratory data analysis
    • Notes: Lecture 5
    • Reading:
      • Additional reading: Matloff Chapter 5
    • Homework 1: Html Rmd

Week 3: Data manipulation and data visualization tools

  • Mon Oct 9: Exploratory data analysis, data manipulation tools
    • Notes: Lecture 6
    • Reading:
      • R for Data Science Chapter 5
  • Wed Oct 11: Data manipulation and visualization tools
    • Notes: Lecture 7
    • Reading:
      • R for Data Science Chapter 3
  • Fri Oct 13: Data visualization tools

Week 4: Data visualization tools, Descriptive statistics

  • Mon Oct 16: Data visualization tools, descriptive statistics

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

  • Wed Oct 18: Review

  • Fri Oct 20: Midterm 1

Week 5: Descriptive statistics

  • Mon Oct 23: Describing numerical distributions
    • Notes: Lecture 10
    • Reading:
      • Open Intro Statistics Chapter 2
  • Wed Oct 25: Numerical and categorical data
    • Notes: Lecture 11
    • Reading:
      • R for Data Science Chapter 7
  • Fri Oct 27: Numerical and categorical data

Week 6: Introduction to probability

  • Mon Oct 30: Introduction to probability
    • Notes: Lecture 13
    • Reading:
      • Open Intro Statistics Chapter 3.1
  • Wed Nov 1: Conditional, marginal and joint probability
    • Notes: Lecture 14
    • Reading:
      • Open Intro Statistics Chapter 3.2
  • Fri Nov 3: Bayes’ Theorem; Random variables
    • Notes: Lecture 15
    • Reading:
      • Open Intro Statistics Chapter 3.4, 3.5
    • Homework 4: Html Rmd

Week 7: Common distributions

  • Mon Nov 6: Bernoulli and binomial distribution
    • Notes: Lecture 16
    • Reading:
      • Open Intro Statistics Chapter 4.3
  • Wed Nov 8: Binomial distribution
    • Notes: Lecture 17
    • Reading:
      • Open Intro Statistics Chapter 4.5
  • Fri Nov 10: Veterans Day

Week 8: Poisson and normal distribution

  • Mon Nov 13: Poisson and Normal distribution

    • Notes: Lecture 18
    • Reading:
      • Open Intro Statistics Chapter 4.1

  • Wed Nov 15: Review

  • Fri Nov 17: Midterm 2

Week 9: Normal distribution

  • Mon Nov 20: Normal distribution
    • Notes: Lecture 19
    • Reading:
      • Open Intro Statistics Chapter 5.1
  • Wed Nov 22, Fri Nov 24: Thanksgiving

Week 10: Sampling distributions, confidence intervals

  • Mon Nov 27: Sampling distribution of sample mean
  • Wed Nov 29: Sample proportion, Confidence intervals
    • Notes: Lecture 21
    • Reading:
      • Open Intro Statistics Chapter 5.2
  • Fri Dec 1: Confidence intervals for population mean and proportion
    • Notes: Lecture 22
    • Reading:
      • Open Intro Statistics Chapter 7.1.1-7.1.4

Week 11: Hypothesis testing

  • Mon Dec 4: Hypothesis testing framework and errors
  • Wed Dec 6: Hypothesis testing for population mean and proportion
    • Notes: Lecture 24
    • Reading:
      • Open Intro Statistics Chapter 6.1
  • Fri Dec 8: Confidence intervals and one-sided tests