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WSP 361 — Introduction to Statistics (ICME Workshop)

Quarter: Summer
Day(s): Monday
Course Format: Live Online (About Formats)
Duration: 1 day
Date(s): Aug 17
Time: 9:00 am—4:45 pm
Drop Deadline: Aug 7
Unit: 0
Grade Restriction: NGR only; no credit/letter grade
Tuition: $400
Limit: 30
Status: Open
Please Note: Registration closes on August 14 at 5 pm. The refund deadline for this workshop is August 7 at 5 pm.
Live Online
9:00 am—4:45 pm
Aug 17
1 day
Drop By
Aug 7
0 Unit
Grade Restriction
NGR only; no credit/letter grade
Please Note: Registration closes on August 14 at 5 pm. The refund deadline for this workshop is August 7 at 5 pm.
The Fundamentals of Data Science

Big data has changed the way we work, live, and play. Data science—developing and testing models and algorithms—helps us gain knowledge for ourselves and provide insights to others.

Introduction to Statistics is one of ten workshops included in Fundamentals of Data Science, a series of one-day workshops offered by the Stanford Institute for Computational and Mathematical Engineering (ICME), provides an introduction to multiple aspects of data science for those who are new to the field and those seeking to broaden their education and skills in data science. Students can sign up for one workshop, or several throughout the week. Students who complete four workshops will qualify for the Stanford ICME Summer Workshops Certificate of Completion.

These workshops are not eligible for tuition discounts through Stanford Continuing Studies, but ICME offers discounts for eligible affiliates and partners. See below for details:

Stanford staff and full-time Stanford students: You may be eligible for a tuition discount if you register directly through ICME. The tuition for Stanford staff is $100 and the tuition for full-time Stanford students is $75. For more information and to register with these discounts, visit

ICME partners and affiliates qualify for a discount. If you think you qualify and have not received a discount code separately, email ticketorders@stanford.edu. You will need to register through ICME's website.

Workshop: "Introduction to Statistics"
Instructor: Guenther Walther, Professor of Statistics, Stanford

Statistics is the science of learning from data. This workshop will help you to develop the skills you need to analyze data and to communicate your findings. There won't be many formulas in the workshop; rather, we will develop the key ideas of statistical thinking that are essential for learning from data.

We will discuss the main tools for descriptive statistics which are essential for exploring data, with an emphasis on visualizing information. We will explain the important ideas about sampling and conducting experiments. Then we will look over some important rules of probability and discuss normal approximation and the central limit theorem. We will show you the important concepts and pitfalls of regression and how to do inference with confidence intervals and tests of hypotheses. You will learn how to analyze categorical data and discuss one-way analysis of variance. Finally, we will look at reproducibility, data snooping and the multiple testing fallacy, and how to account for multiple comparisons. These issues have become particularly important in the era of big data.

Broadly, there are three main reasons why statistical literacy is essential in data science: First, it provides the skills to assess whether the data are sufficient to answer the questions at hand. Second, it establishes a rigorous framework for quantifying uncertainty. And finally, it provides techniques for effectively communicating the findings of your analyses. This workshop equips you with the important tools in all of these areas. It is the statistical foundation on which the recent exciting advances in machine learning are built.

Please note: Although the enrollment limit for this workshop is set to 30 Continuing Studies students, this course is designed for the entire Stanford community, and enrolled Continuing Studies students will be joined in the classroom by Stanford graduates and undergraduates. Students should expect a large class.