STA 032 - Gateway to Data Science: Spring 2023

Instructor: Jingwei Xiong: jwxxiong@ucdavis.edu

TA: Xiawei Wang: xxwwang@ucdavis.edu

Class time:
Lectures are Mondays, Wednesdays and Fridays, 9-9:50 AM at Wellman 6
Discussions (labs) are run by TA on Thursdays 10:00 - 10:50 AM (Section A01), 11:00 - 11:50 AM (Section A02) at TLC 2212.

Office hours:

TBD Jingwei Xiong: TBD

Canvas: here.

Syllabus: here.

Piazza: here.

Gradescope: here.

Class Schedule

Week 1: Introduction, R programming basics

• Homework 0: Check in

• Mon April 3: Introduction, R, Rstudio

  • Notes: Lecture 1
  • Reading:
    • Getting started with R and RStudio
    • R markdown
    • R markdown tutorial
    • R for Data Science Chapters 1, 2, 27

• Wed April 5: Overview of data types and data structures, vectors

  • Notes: Lecture 2
  • Reading:
    • R for Data Science Chapter 4, 20
    • Additional reading: Matloff Chapter 2
    • Chapter 2:R basics

• Fri April 7: More on Vectors, R markdown Homework example

  • Notes: Lecture 3
  • Reading:
    • R for Data Science Chapter 20, 27
    • Additional reading: Matloff Chapter 2
    • Chapter 2:R basics
    • R markdown tutorial
  • Homework 1: (Due April 17 midnight, cover lecture 1-4) Html Rmd

Week 2: Introduction to data manipulation

• Mon April 10: List and data frame
  • Notes: Lecture 4
  • Reading:
    • R for Data Science Chapter 4, 20
    • Chapter 2:R basics
    • Chapter 3
• Wed April 12: Data Manipulation 1
  • Notes: Lecture 5
  • Reading:
    • Chapter 4:The tidyverse
    • R for Data Science Chapter 5
• Fri April 14: Data Manipulation 2, intro to data visualization
  • Notes: Lecture 6
  • Reading:
    • Chapter 4:The tidyverse
    • Data Wrangling with Tidyverse
    • Chapter 7:Introduction to data visualization
  • Homework 2: (Due April 26 midnight, cover lecture 5-8) Html Rmd

Week 3: Introduction to data visualization, descriptive statistics

• Homework 1: (Due April 17 midnight)

• Mon April 17: Data visualization 1

  • Notes: Lecture 7
  • Reading:
    • Chapter 7:Introduction to data visualization
    • Chapter 8:ggplot2

• Wed April 19: Data visualization 2, descriptive statistics

  • Notes: Lecture 8
  • Reading:
    • Chapter 10:Data visualization in practice
    • Open Intro Statistics Chapter 1

• Fri April 21: Describing numerical distributions

  • Notes: Lecture 9
  • Reading:
    • Open Intro Statistics Chapter 1
  • Homework 3: (Due May 3 midnight, cover lecture 9-11) Html Rmd

Week 4: Descriptive Statistics: Numerical and Categorical Data

• Mon April 24: Describing Numerical and Categorical Data
  • Notes: Lecture 10
  • Extra R programming 1: Extra R programming 1
  • Reading:
    • R for Data Science Chapter 7
    • Chapter 9 Visualizing data distributions
• Wed April 26: Describing Numerical and Categorical Data, customizing plots
  • Notes: Lecture 11
  • Reading:
    • Chapter 10 Data visualization in practice
    • Chapter 11 Data visualization principles
  • Homework 2: (Due April 26 midnight)
• Fri April 28: Introduction to Probability
  • Notes: Lecture 12

Midterm 1: (Due April 29 midnight, cover lecture 1-12)

• Open book take home exam
• It’s highly recommended to finish the homework 1,2,3 before midterm 1.

Week 5: Probability and distributions

• Mon May 1: Introduction to Probability
  • Notes: Lecture 13
  • Homework 4: (Due May 12 midnight, cover lecture 12-15) Html Rmd
• Wed May 3: Random Variables and Distributions
  • Notes: Lecture 14
  • Homework 3: (Due May 3 midnight)
• Fri May 5: Binomial distribution, simulation, R function
  • Notes: Lecture 15

Week 6: More on distributions

• Mon May 8: Law of large numbers, Poisson distribution, Monte Carlo simulation
  • Notes: Lecture 16
  • Extra example on Sampling
  • Homework 5: (Due May 21 midnight, cover lecture 15-18) Html Rmd
• Wed May 10: Continuous RV, Normal (Gaussian) Distribution
  • Notes: Lecture 17
• Fri May 12: Distributions for sample mean
  • Notes: Lecture 18
  • Homework 4: (Due May 12 midnight)

Week 7: Statistical inference 1

• Mon May 15: Confidence Intervals
  • Notes: Lecture 19
• Wed May 17: Confidence Intervals
  • Notes: Lecture 20
  • Extra example on CI
  • Homework 6: (Due May 28 midnight, cover lecture 19-22) Html Rmd
• Fri May 19: Hypothesis Testing
  • Notes: Lecture 21

Week 8: Statistical inference 2

• Mon May 22: Hypothesis Testing for Population Mean and Proportion

  • Notes: Lecture 22

• Wed May 24: Midterm 2 (In person)

• Fri May 26: Regression 1

  • Notes: Lecture 23

Week 9: Application 1

• Mon May 29: Memorial Day, no class

• Wed May 31: Regression 1

  • Notes: Lecture 24

• Fri Jun 2: Homework 7: (Due Jun 12 midnight) Html Rmd

Week 10: Application 2

• Mon Jun 5: Web scraping 1
  • Notes: Lecture 25
    
  • Source code: Lecture 25
• Wed Jun 7: Web scraping 2
  • Notes: Lecture 26