STAT 140-04: Introduction to the Ideas and Applications of Statistics

This course introduces students to the discipline of statistics as a science of understanding and analyzing data. Throughout the module, students will learn how to effectively make use of data in the face of uncertainty: how to collect data, how to analyze data, and how to use data to make inferences and conclusions about real world phenomena.


Course Information

Teaching team

ShanShan Headshot

Shan Shan (Instructor)
Ales Headshot

Alex Moreno
Sneha Headshot

Sneha Pradhan

Class Time and Location

Monday through Friday 12:45PM (EST) on zoom

Communication

All communication will take place via Piazza:

  1. All official announcements and communication will happen over Piazza.
  2. Any questions regarding course content and course organization should be posted on Piazza. You are strongly encouraged to answer other students' questions when you know the answer.
  3. If there are private matters specific to you (e.g special accommodations, requesting alternative arrangements etc.), please create a private post on Piazza.
  4. For longer discussions (e.g., help on R project), please come to office hours.
  5. I will respond to Piazza posts sent during the week within 24h. I will respond to Piazza posts sent during the weekend at my own discretion.

Course materials

Textbook

Finding a good book is always a problem—none of the available texts is fully satisfactory. I will mostly follow the chapters in

Introduction to Statistics and Data Science: A moderndive into R and the tidyverse

by Chester Ismay, Albert Y. Kim, Arend M. Kuyper, Elizabeth Tipton, and Kaitlyn G. Fitzgerald. But we shall also read widely from various sources. I will occasionally post additional reading materials. Please check the daily schedule for reading assignments each day.

Computer and zoom attendance

You are not required to purchase any books, hardware, or software for this course. While not required, having one’s own laptop (and webcam, if not built-in) is helpful, particularly for in-class activities and office hours. If you do not have a laptop, please reach out to me at the beginning of the module to discuss possibilities.

Students should ideally be attending the class via zoom with webcam enabled and microphone capability, unless simply not possible, to maximize opportunities for participation. If you do not have stable internet or webcam, please let me know at the beginning of the module.

R and Rstudio

We will be using the programming language R and Rstudio. R is one of the most commonly used programming languages in academic research and industry. Knowing R is a marketable skill. In this class, you will learn enough about R to conduct basic data analysis tasks. Please install R and Rstudio at your earliest convenience.

Datacamp

In the first two weeks, you will be asked to complete 4 DataCamp assignment. You will get an email by the first day of class inviting you to join our class organization with an assignment pointing you to the specific chapters to do. Available for free.

  • How is your grade calculated? Check out the Syllabus tab for course policies.
  • You can find all course materials (readings, slides, videos, assignments) about this course on the daily Schedule.
  • Got a question? Come to the teaching staff's Office Hours. We are ready to help!
  • All communcations about this course should take place on Piazza.
  • You will submit all your assignments on Gradescope.
  • Browse through the Tips tab for some quick answers to FAQs.

Course goals

Statistical Thinking

Data visualization and summary, statistical inference

Computational Skills

Working with data in R and Rstudio

Communication Skills

Writing and speaking about statistics

Typical workflow

Weekly
  • Monday

    Q&A (15 min) + Lecture (30min) + Tutorial exercise (30min)

  • Tuesday

    Lecture (30min) + Tutorial exercise (30min)

  • Wednesday: HW due

    Lecture (15min) + Tutorial exercise (1hr 30min)

  • Thursday

    Lecture (30min) + Tutorial exercise (30min)

  • Friday: Project due

    Lecture (15min) + Tutorial exercise (1hr 30min)

Daily
  • Before class (1hr)

    Finish reading/video assignments

  • During class (1.5hr)

    Some lecture and lots of learning by doing

  • After class (2hr)

    Complete a problem set and project assignment everyweek

In general, this course should take you about 20-22 hours per week.

Acknowledgement

Some of the slides, exercises and language of syllabus come from Mine Çetinkaya-Rundel, David Banks, Evan Ray, Kari Lock Morgan, Ben Baumer, Albert Kim, Drew Hilton, Teaching Statistics: A Bag of Tricks by Andrew Gelman and Deborah Nolan, and Asking good questions blog by Allan Rossman. Illustrations in the course goal sections come from the Openscapes blog Tidy Data for reproducibility, efficiency, and collaboration by Julia Lowndes and Allison Horst (second) and the Luminousmen blog luminousmen.com (first and third). Some of the website design come from Mine Çetinkaya-Rundel and Mine Dogucu.