Statistics for Data Science

Statisticians who can code and understand data science have an advantage in today’s competitive, dynamic job market. Learn to solve complex challenges with data.

About the Course

Statistics for Data Science is a highly practical, eight-week course that will provide you with the foundational tools to solve data science problems and prepare you to take the next steps in the world of machine learning.

You Will Learn To:

Leverage a data set to produce a specified set of results
Evaluate results and understand the concept of hypothesis testing
Perform a Principal Components Analysis (PCA) to provide meaningful insights on the original data set
Learn the intricacies of logistic regression, evaluate its outputs, and comprehend how a link function works
Perform multiple pairwise comparisons and analyze models with multiple categorical predictors
Build a classification model and interpret results
Present a start-to-finish analysis with meaningful insights on a data set using exploratory analysis dimension reduction, linear models, and classification models

Methodologies and Techniques:

You Will Be Able To:

Solve data science problems and prepare to take the next steps in the world of machine learning
Understand RStudio and its application
Gain confidence handling and manipulating data
Interpret data and be able to communicate it effectively
Earn a certificate of completion from the University of Chicago and become part of the UChicago network

Request for Information

UCH-SDC-ENG

https://online.professional.uchicago.edu/info-typ/dtb-sds-infotyp/

Career Outlook

$

87

k

The average annual base pay for a statistician in the US

#

21

The ranking of statistician in US News and World Report’s 2021 Best Jobs

26.9

%

The expected CAGR of the global data science platform size from 2020 to 2027

Potential Job Titles for Data Science-Applied Statistics

Analytics Consultant
Data Insight Analyst
Data Scientist
Machine Learning Specialist
Statistician

Meet Your Instructors

Gregory Bernstein works as a data scientist for Kinexon Sports and Media, a small branch of a German based company working with teams in the NBA, NFL and other professional leagues to monitor athletes’ movement and exertion, provide consultation regarding load management, and optimize in-game performance. Bernstein enjoys working in the startup environment and engages in analytical modeling, reporting and visualization, and product development. He earned a BA from Lafayette College in creative writing and economics in 2012 and graduated from the University of Chicago Master of Science in Analytics program in 2019. In addition to sports, Bernstein has a passion for teaching and education and has previously worked with the Princeton Review and as a TA for both the MS Analytics program and the Booth School of Business.

Do you have questions?

The University of Chicago Approach to Online Learning

Our online learning programs are crafted with your specific needs in mind. Programs combine e-learning with live, interactive sessions to strengthen your skill set while maximizing your time. We couple academic theory and business knowledge with practical, real-world application. Through online learning sessions, you will have an opportunity to grow your professional network and interact with University of Chicago instructors and your classmates.