Elements of Data Science
SDS 322E

H. Sherry Zhang
Department of Statistics and Data Sciences
The University of Texas at Austin

Fall 2026

SDS 322E: Elements of Data Science

  • Lecture: Mon / Wed / Fri 11am - 12 noon | UTC 3.1
  • Lab: Tue 3-4pm (62915)/ 4-5pm (62920) | FAC 101B
Mon Tue Wed Thur Fri
Lecture Lab Lecture Lecture
Lab due 11:59pm HW due 11:59pm

Lab starts from Week 3 (09/08)

Assessment

  • Labs (30%) - Coding exercises, done in 3-ppl group.
    • Best 10 out of 11, due Tue 11:59 pm week 3-13.
  • Homework (30%) - More complex coding exercises, individual.
    • Best 5 out of 6, due Thur 11:59 pm week 3-5, 10-12.
  • Projects (40%) - Full data analysis report, done in 3-ppl group (same group as the Labs).
    • 2 out of 2, due Thur 11:59 pm week 8 and 14.
  • Exams (0%) - There are no exams.

Assessment

Week Tuesday 11:59pm Thursday 11:59pm
3 Lab 1
4 Lab 2 HW 1
5 Lab 3 HW 2
6 Lab 4 HW 3
7 Lab 5
8 Lab 6
9 Lab 7 Project 1
10 Lab 8 HW 4
11 Lab 9 HW 5
12 Lab 10 HW 6
13 Lab 11 Project 2 prelim
15 Project 2

About me

  • I’m a Postdoc Fellow at UT Austin

  • I did my PhD at Monash University in Australia

  • I work on visualization and statistical computing.

  • I climb 🧗‍♂️ and swim 🏊

Meet our teaching assistants

  • Graduate TA

  • Undergraduate TA:

    • Duc Anh Dang
    • Constanza Jongkind

Communication

  • Announcements in Canvas

  • Ask questions after lecture

  • Office hours: Sherry: Mon 9-11am | Ale: Wed 3-5pm

  • Email:

    • Subject: include course code (SDS 322E) and the reason for emailing
    • Plan ahead: expect 1-2 business days
    • Don’t expect answers during the weekend or after 5pm

Questions?

Learning objectives

End goal: Develop confidence in using R for exploratory analyses (Weeks 1–8) and confirmatory analyses (Weeks 9–14), while preparing you for more advanced data analysis tasks.

Specifically, you will learn:

  • Program for data analysis in R using RStudio
  • Wrangle and visualize data with tidyverse
  • Apply prediction and classification techniques, among other machine learning algorithms

The big picture

mtcars
                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

Transform (wrangling) with dplyr

You shall be able to do this for later assessment!

library(tidyverse)
mtcars |> 
  # create a new variable 
  mutate(kpl = mpg * 0.425144) |> 
  
  # "select" rows: select the rows with V-shaped Engine
  # vs: Engine (0 = V-shaped, 1 = straight)
  filter(vs == 0) |>
  
  # do things by groups
  group_by(cyl) |>  
  
  # create a data summarization
  summarize(disp = mean(disp, na.rm = TRUE), 
            kpl = mean(kpl, na.rm = TRUE)) |> 
  
  # sort the data frame by a variable
  arrange(disp)

Visualization with ggplot2

mtcars2 <- mtcars |> 
  mutate(
    vs = ifelse(vs == 0, "V-shaped", "Straight")
    ) |>
  rename(Engine = vs) 

mtcars2 |> 
  ggplot(aes(x = mpg, y = disp, 
             color = as.factor(cyl))) + 
  geom_point() + 
  facet_wrap(vars(Engine), 
             labeller = label_both) + 
  scale_color_brewer(
    palette = "Dark2", 
    name = "Number of cylinders") +
  theme_bw() + 
  theme(legend.position = "bottom") + 
  xlab("Miles per gallon") + 
  ylab("Displacement (cu.in.)")

Larger engine displacement means a greater volume of air must be filled, requiring more fuel per cycle to maintain combustion and directly causing a lower miles-per-gallon (MPG) rating.

Visualization with ggplot2

Visual principles

In next few weeks…

Week Date Topic
1 08/24 Welcome
2 08/31 Data Wrangling I: dplyr
3 09/07 Data Visualization: ggplot2
4 09/14 Data Visualization II: ggplot2
5 09/21 Data Wrangling: dplyr + lubridate
6 09/28 Data Wrangling: tidyr + Case Study I
7 10/05 Web Scraping + Case Study II
8 10/12 Text Analysis + Case Study III

In next few weeks…

Week Date Topic
9 10/19 Modelling I: Cluster analysis
10 10/26 Modelling II: PCA + Linear Regression
11 11/02 Modelling III: Logistic Regression + KNN
12 11/09 Modelling IV: Tree Methods + tidymodel
13 11/16 Case Study IV
Fall Break
14 11/30 Animation and Interactive Graphics
15 12/07 Project 2 Working Day

In the case study, you will create plots like …

In the case study, you will create plots like …

Textbooks

We will not learn Python in this course.

Class materials - course website

https://huizezhang-sherry.github.io/SDS322E/

You can view the slides in HTML

Class materials - GitHub organization

https://github.com/SDS322E-26Fall

The repository contains all the class exercises and assessment materials (Lab, Homework, and Projects).

We will instruct you how to get those materials for the class on Wednesday.

Install R and RStudio

Even if you installed R/RStudio previously, it’s important to have the latest version

  • R version 4.6.1
    • Check your current version with R.version
  • RStudio version 2026.08

RStudio IDE

Change the appearance of your RStudio IDE

  • Go to Tools > Global Options (or Press Command + ,) to pull out the options panel
  • Select the Appearance tab and find your favorite Editor font and theme

Your time

  • Install R and RStudio

  • Change the appearance of your RStudio IDE:

    • Go to Tools > Global Options (or Press Command + ,)

    • Select the Pane Layout tab to move around the panes

    • Select the Appearance tab and find your favorite Editor font and theme