Statistics for Data Science
Learn to solve complex problems with data.
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At a Glance
- Enrollment:
- Open Enrollment
- Length:
- 8 weeks
- Format:
- Online
- Total CEUs:
- 6.8 CEUs
- Investment:
-
$2,800
Upcoming Dates
September Start
Students may register up to 7 days after the course start.
Unlock insights and make informed decisions through rigorous analysis and coding proficiency.
The University of Chicago’s eight-week Statistics for Data Science course will prepare you to leverage data and drive important decision-making processes. You will also learn to code at an introductory level, emerging prepared to enter the world of machine learning.
Designed For
Designed for aspiring data scientists who would like to learn to code, professionals with computer science backgrounds, and those looking to transition into data science.
Learning Objectives to Become an Expert in Data Science-Applied Statistics
Statistics—the art of finding structure in and gleaning deeper insights from data—is among the essential means of analyzing and quantifying uncertainty, and statistical methods are crucial to data science. This highly practical course will provide the foundational tools to solve data science problems and prepare you to embrace the possibilities and solutions in machine learning.
After completing the course, you will be able to:
- Understand R and RStudio and their applications.
- Analyze the concept of hypothesis testing, work with data sets, build classification models, and interpret results.
- Comprehend the intricacies of logistic regression, evaluate its outputs, and understand how a link function works.
- Perform a Principal Components Analysis (PCA) and multiple pairwise comparisons and analyze models with multiple categorical predictors.
- Present a start-to-finish analysis with meaningful insights on a data set using exploratory analysis, dimension reduction, linear models, and classification models.
- Earn a credential certifying completion from the University of Chicago and join the UChicago network.
Ready to Take Your Career to the Next Level?
Register today and unite your professional practice with our distinctive blend of academic rigor and real-world application.
Enroll NowStatistics for Data Science Curriculum
You will learn to:
- Solve problems using statistical methods.
- Manage and work confidently with data.
- Interpret and communicate data effectively.
Methodologies and Techniques:
R
RStudio
Online Format Features
- Self-paced interactive learning modules with a variety of engaging learning activities, assignments, and resources.
- Live sessions that bring you, your peers, and your instructor together to learn collaboratively about the current state of the field, engage with real-world problems, and explore authentic solutions.
- Continuous support from your instructional assistant, who will accompany you on your journey through the content, answer your questions, and provide feedback on your work.
Weekly Course Schedule
Familiarize yourself with, set up, and practice using RStudio.
Learn modeling basics, define an objective function for evaluating model performance, carry out unsupervised/supervised analysis, and explore the bias/variance tradeoff with an introduction to different model types.
Understand random variables and hypothesis testing. Gain exposure to different data distributions and methods for hypothesis testing.
Examine distance- and density-based clustering methods for exploratory analysis—k-means, hierarchical clustering, and DBSCAN—by selecting the appropriate clustering method to expand your knowledge of data sets.
Discover dimension reduction and learn how to apply principal components analysis (PCA) as a method, including the fundamentals of PCA, to comprehend how its results have significance in terms of the original data and the creation of meaningful features from exploratory analysis that will help you perform supervised modeling.
Examine the method of moments and learn to use it to determine linear model parameters, understand the assumptions and restrictions of a linear model, and evaluate the estimates and suitability in a linear model.
Learn to perform variable transformations and include interaction terms to improve model quality, discover and address issues with multicollinearity, and incorporate features from exploratory analysis when building a linear model.
Delve into the concept of a classification model while learning about the intricacies of logistic regression, outputs, and link functions. Understand the extension of binary logistic regression to multinomial logistic regression.
Examine the process of modeling categorical independent variables, evaluate ANOVA outputs from a traditional linear model, determine whether group means values are significantly different, perform multiple pairwise comparisons, and analyze models with multiple categorical predictors.
Earn a Credential in Statistics for Data Science
After successful completion of this course, participants will receive credentials certified by the University of Chicago including a digital badge to recognize their achievement.
Meet Your Instructor
Our highly trained instructors are courageous thinkers and passionate leaders who leverage years of industry expertise and up-to-date knowledge of terminology, tools, and trends to deliver an unparalleled learning experience. Through their rigorous discourse, cross-disciplinary collaboration, and field-shaping contributions, they create practical solutions and pioneering innovations that enrich our world.
Gregory Bernstein, MSc
Data Scientist, Kinexon Sports and Media
Gregory Bernstein works as a data scientist and product manager for Kinexon Sports and Media, a branch of a Germany-based company that works with teams in the NBA, NFL, and other professional leagues to monitor athletes' movement and exertion, provide consultation on load management, and optimize in...
Career Outlook
Data is a commodity, and statisticians who know how to code and understand data science are in high demand across industries. Statistics, the art of finding structure in and gleaning deeper insights from data, is among the most vital means of analyzing and quantifying uncertainty, and statistical methods are crucial to data science. The overall employment market for mathematicians and statisticians is expected to grow by 33% over the next decade.
The average annual pay for a statistician in the United States.
The ranking of statistician among the top 100 jobs.
The expected CAGR of the global data science platform size from 2020 to 2027.
Potential Job Titles for Professionals with Data Science-Applied Statistics Skills
- Analytics Consultant
- Data Insight Analyst
- Data Scientist
- Machine Learning Specialist
- Statistician
Of Interest
Python for Data Science
Learn to design and write high-performing Python code.