Graduate Certificate in Data Science
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Download the certificate guide for details on admission requirements, tuition, and courses.
The Time to Upskill is Now. 100% Online.
Data intelligence is the difference between a successful business and one that gets left behind. The 100 percent online University of Wisconsin Graduate Certificate in Data Science is designed for busy professionals ready to hone their skills in data science and build the foundation necessary to effectively work with and communicate about data.
Gain the relevant knowledge to help your career thrive:
- Strengthen data science skills to keep pace in an evolving field
- Improve data literacy to more effectively communicate your analysis
- Learn how to organize and interpret data using the latest tools and methodologies
As a healthcare professional, I needed to work with and understand data…It was during that time that I realized data science was something that would help me connect all the dots and was an area I didn’t want to miss out on exploring.” – Venmathi Shanmugan, UW Data Science graduate
Coursework completed in the certificate can apply toward the Master of Science in Data Science program should you choose to enroll as a degree-seeking student later. Learn more about the courses in the certificate program.
Who Should Apply?
Data Science Graduate Certificate students learn skills that help them advance in their current jobs, gain a competitive edge in the job market, or change careers entirely. You do not need a technical background. In fact, our students come from a wide variety of work experiences, including engineering, statistics, mathematics, business, marketing, healthcare and many other fields.
Busy adults will find the flexibility of the online format especially convenient. Learn more about online learning with UW.
A bachelor’s degree from an accredited institution is required for admission.
Universities of Wisconsin Collaboration
The Data Science Graduate Certificate is offered through the Master of Science in Data Science. This certificate program is a partnership of UW-Eau Claire, UW-Green Bay, UW-La Crosse, UW-Oshkosh, UW-Stevens Point, and UW-Superior. Learn more about our campus partners and choosing a home campus.
Accreditation
Whether online or on campus, University of Wisconsin programs have a reputation for delivering world-class education and student support. Accreditation is your assurance that you will graduate with skills that are relevant to your field and valued by employers. The Data Science Graduate Certificate is approved by the University of Wisconsin Board of Regents and is fully accredited by the Higher Learning Commission.
To be eligible for admission to the UW Data Science Certificate program, students must meet the following requirements:
- A bachelor’s degree from an accredited university with a cumulative GPA of 3.0 or higher. Students with a GPA of less than 3.0 may be considered for provisional admission based on a review of all application materials.
- Completed coursework in elementary statistics and introductory computer programming. Relevant work experience in these areas may be considered in lieu of prerequisite coursework. Please contact an enrollment adviser for details.
You may also need:
- Your resume.
- Two letters of recommendation (can be professional or academic).
- A personal statement of up to 1,000 words describing the reasons behind your decision to pursue this degree and what you believe you will bring to the data science field. Space for the personal statement is included in the online application.
Aptitude tests, such as the GMAT or GRE, are not required for admission.
If you are not sure whether you meet these requirements, or which courses you need to take to satisfy prerequisites, contact an enrollment adviser by phone, 608-800-6762, or email learn@uwex.wisconsin.edu.
Application Deadline
Your online application and all required materials must be submitted to your preferred home campus generally 2-4 weeks prior to the date classes start (this varies by campus) to be considered for admission.
Starting your application early will ensure you have plenty of time to gather required materials (such as transcripts) and complete the University of Wisconsin System Online Admission Application.
International Guidelines
This program welcomes online students from around the world. Online students do not qualify for an F-1 Student Visa to travel to the U.S., but instead can participate in our online courses remotely. If your native language is not English and/or you attended school outside of the U.S., you will likely need to provide proof of English language proficiency and an official translation or evaluation of academic transcripts. Requirements will vary based on a student’s academic history and home campus policies. For guidance about these requirements and how they apply to your specific situation, contact your preferred home campus admissions office.
If you would like to apply as an International Student for an on-campus program in the UW System please refer to these resources through Go Wisconsin.
How to Apply
While you are free to apply on your own, many prospective students find it helpful to speak with an enrollment adviser first.
Step 1. Select a home campus from our list of program partners: UW-Eau Claire, UW-Green Bay, UW-La Crosse, UW-Oshkosh, UW-Stevens Point, and UW-Superior. Learn more about our campus partners and choosing a home campus.
Step 2. Apply to your preferred home campus using the University of Wisconsin System Online Admission Application. Please review the application instructions for your preferred home campus below.
*For a limited time, UW is offering an application fee waiver to those who haven’t yet applied to the Summer or Fall 2025 semesters. To redeem, use coupon code APPLY25 on the UW Online Application payment page.
- Apply as a “Graduate Applicant”.
- When asked “Are you applying as a degree-seeking student?” select “Yes”, and that you would like to earn a “Graduate Degree”.
- Select “UW-Eau Claire” as the campus.
- Choose “Data Science-Collaborative” for program.
- State that you are interested in the Graduate Certificate in Data Science in the essay portion of the application.$56 application fee.*
*For a limited time, UW Extended Campus is offering an application fee waiver to those who haven’t yet applied to the Spring, Summer or Fall 2024 semesters. To redeem, use coupon code APPLY24 on the UW Online Application payment page.
Financial aid is available if you qualify.
If you are applying to UW-Eau Claire for Spring 2025 or later, you will use the UW-Eau Claire Graduate Admissions Application.
- Apply as a “Graduate Non-Degree Applicant”.
- When asked “Are you applying as a degree-seeking student?” select “No”, and that you plan to take “Graduate” classes.
- Select “UW-Green Bay” as the campus.
- Choose “MS Data Science Certificate” for program.
- No application fee. Skip the application fee section (not required for the certificate).
- Not eligible for financial aid.
- Apply as a “Graduate Applicant”.
- When asked “Are you applying as a degree-seeking student?” select “Yes”, and that you would like to earn a “Graduate Degree”.
- Select “UW-La Crosse” as the campus.
- Choose “Data Science-Collaborative” and then click on “Data Science Certificate-Collaborative” for program.
- $56 application fee.*
- *For a limited time, UW Extended Campus is offering an application fee waiver to those who haven’t yet applied to the Spring, Summer or Fall 2024 semesters. To redeem, use coupon code APPLY24 on the UW Online Application payment page.
- Financial aid is available if you qualify.
- Apply as a “Graduate Applicant”.
- When asked “Are you applying as a degree-seeking student?” select “Yes”, and that you would like to earn a “Graduate Degree”.
- Select “UW-Oshkosh” as the campus.
- Choose “Certificate in Data Science” for program.
- $56 application fee.*
- *For a limited time, UW Extended Campus is offering an application fee waiver to those who haven’t yet applied to the Spring, Summer or Fall 2024 semesters. To redeem, use coupon code APPLY24 on the UW Online Application payment page.
- Financial aid is available if you qualify.
- Apply as a “Graduate Non-Degree Applicant”.
- When asked “Are you applying as a degree-seeking student?” select “No”, and that you plan to take “Graduate” classes.
- Select “UW-Stevens Point” as the campus.
- Choose “Grad Non-Degree Seeking” for program.
- State that you are interested in the Graduate Certificate in Data Science in the essay portion of the application.
- No application fee. Skip the application fee section (not required for the certificate).
- Not eligible for financial aid.
- Apply as a “Graduate Non-Degree Applicant”.
- When asked “Are you applying as a degree-seeking student?” select “No”, and that you plan to take “Graduate” classes.
- Select “UW-Superior” as the campus.
- Choose “Data Science Certificate (non-degree seeking) Graduate” for program.
- No application fee. Skip the application fee section (not required for the certificate).
- Not eligible for financial aid.
Step 3. Send your resume, personal statement, and letters of recommendation; and arrange to have your official college transcripts* (from each institution you attended) sent to the graduate student admissions office of the home campus to which you applied.
*Please request electronic transcripts if this service is offered by your previous school(s). Have the e-transcript sent from your previous school directly to the admissions e-mail address of your chosen home campus. E-transcripts are usually delivered more quickly than physical copies sent by mail.
5 Courses. 15 Credits. 100% Online.
Data Science Graduate Certificate courses are available during the fall, spring, and summer semesters. All courses are 100 percent online and asynchronous. Students who choose to take two courses per semester can complete the certificate in one year.
Students planning to complete the certificate in Spring and Summer 2025 must complete DS 700, DS 710, DS 715, DS 735, and DS 740.
Students planning to complete the certificate in Fall 2025 and later are required to take three courses: DS 700 OR DS 701, DS 705, and DS 710. Students may choose two elective courses from the remaining curriculum (DS 785-Capstone is not available for certificate students).
Data science certificate courses are part of the online University of Wisconsin Master of Science in Data Science program. Some of your peers will be pursuing the certificate, while others will be degree-seeking students. Should you choose to continue your data science education upon completion of the certificate, you will have the option to apply to the MS in Data Science program.
Preview lectures, assignments, and discussions in this Course Inside Look: Foundations of Data Science.
| Course | Credits |
|---|---|
This course provides an introduction to data science and highlights its importance in business decision making. It provides an overview of commonly used data science tools along with spreadsheets, relational databases, statistics, and programming assignments to lay the foundation for data science applications. | 3 Credits |
This course introduces data science and highlights its importance in decision making. Students will learn how to analyze data using the R programming language. During the course, students will learn how to import data into R, tidy it, conduct exploratory data analysis, develop visualizations, and draw statistical inferences. The course aims to teach data wrangling, visualization and exploration with R. | 3 Credits |
This course will present statistical methods and inference procedures with an emphasis on applications, computer implementation, and interpretation of results. Familiarity with the R programming language is highly recommended. Topics include simple and multiple regression, model selection, correlation, moderation/interaction analysis, logistic regression, the chi-square test, the Kruskal-Wallis test, analysis of variance (ANOVA), multivariate analysis of variance (MANOVA), factor analysis, and canonical correlation analysis. Prerequisite: DS 700 or 701. | 3 Credits |
Computer programming is an essential part of data science. When working with large data sets, it’s especially important to be able to write effective, efficient code to help you organize and understand the data. In this course, we’ll introduce you to one of the most widely-used programming languages for data science: Python. You’ll gain experience working with real-world data, and leave the course with skills you can apply in other courses in the MS Data Science Program as well as on the job! | 3 Credits |
This course will introduce you to the concepts and techniques to work with and reason about subject-oriented, integrated, time-variant, and nonvolatile collections of data in support of management’s decision-making process. | 3 Credits |
This course explores the various approaches for data management used in data science. We present how data is collected, transformed, stored, and delivered for use in data science projects. | 3 Credits |
This course prepares you to process large data sets efficiently. You will be introduced to nonrelational databases and algorithms that allow for the distributed processing of large data sets across clusters. Prerequisite: DS 710 | 3 Credits |
This course will prepare you to master technical, informational, and persuasive communication to meet organizational goals. Technical communication topics include a study of the nature, structure, and interpretation of data. Informational communication topics include data visualization and design of data for understanding and action. Persuasive communication topics include the study of written, verbal, and nonverbal approaches to influencing decision makers. | 3 Credits |
Explore data mining methods and procedures for diagnostic and predictive analytics. Topics include association rules, clustering algorithms, tools for classification, and ensemble methods. Computer implementation and applications will be emphasized. Prerequisites: DS 700 or 701, DS 705 or DS 710 | 3 Credits |
This course covers two aspects of data analytics. First, it teaches techniques to generate visualizations appropriate to the audience type, task, and data. Second, it teaches methods and techniques for analyzing unstructured data – including text mining, web text mining and social network analysis. Prerequisite: DS 740 | 3 Credits |
Data storytelling involves using data to tell a compelling narrative that helps audiences understand, engage with, and act on the information. This course combines data analysis with communication techniques to present data in an informative and engaging way. This course is specifically designed as a graduate-level requirement for the MSDS degree, focusing on teaching students how to effectively communicate insights through data storytelling techniques. Participants will learn to craft engaging stories that resonate with various audiences and drive decision-making. Prerequisites: DS 700 or 701. DS 705 OR DS 740 suggested but not required. | 3 Credits |
This course examines how data science relates to developing strategies for organizations. The emphasis is on using an organization’s data assets to inform better decisions. The course investigates the use of data science findings to develop solutions to competitive organizational challenges. Special attention is given to critically examining decisions to ensure that they are ethical and avoid unfair bias. Professional codes of conduct as well as local and international regulations are also considered. Prerequisites: DS 740 suggested but not required. | 3 Credits |
Introduction to the theory and applications of deep learning. The course begins with the study of neural networks and how to train them. Various deep learning architectures are introduced including convolutional neural networks, recurrent neural networks, and transformers. Applications may include image classification, object detection, and natural language processing. Algorithms will be implemented in Python using a high-level framework such as Pytorch or TensorFlow. Prerequisites: DS 740.
| 3 Credits |
Explore the current and future applications of data science as a strategic decision-making tool to achieve a competitive advantage in business. With an emphasis on obtaining decision-making value from an organization’s data assets, this course will investigate the use of data science findings to develop solutions to competitive business challenges. Through case studies, you will examine how data science methods can support business decision making, and discover a range of methods the data scientist can use to get people within the organization on board with data science projects. | 3 Credits |
Spring 2025
Course Preview Week: January 21 - January 27, 2025
Semester Dates: January 28 - May 09, 2025
| Course | Credits |
|---|---|
This course provides an introduction to data science and highlights its importance in business decision making. It provides an overview of commonly used data science tools along with spreadsheets, relational databases, statistics, and programming assignments to lay the foundation for data science applications. | 3 Credits |
Computer programming is an essential part of data science. When working with large data sets, it’s especially important to be able to write effective, efficient code to help you organize and understand the data. In this course, we’ll introduce you to one of the most widely-used programming languages for data science: Python. You’ll gain experience working with real-world data, and leave the course with skills you can apply in other courses in the MS Data Science Program as well as on the job! | 3 Credits |
This course will introduce you to the concepts and techniques to work with and reason about subject-oriented, integrated, time-variant, and nonvolatile collections of data in support of management’s decision-making process. | 3 Credits |
This course prepares you to process large data sets efficiently. You will be introduced to nonrelational databases and algorithms that allow for the distributed processing of large data sets across clusters. Prerequisite: DS 710 | 3 Credits |
This course will prepare you to master technical, informational, and persuasive communication to meet organizational goals. Technical communication topics include a study of the nature, structure, and interpretation of data. Informational communication topics include data visualization and design of data for understanding and action. Persuasive communication topics include the study of written, verbal, and nonverbal approaches to influencing decision makers. | 3 Credits |
Explore data mining methods and procedures for diagnostic and predictive analytics. Topics include association rules, clustering algorithms, tools for classification, and ensemble methods. Computer implementation and applications will be emphasized. Prerequisites: DS 700 or 701, DS 705 or DS 710 | 3 Credits |
This course covers two aspects of data analytics. First, it teaches techniques to generate visualizations appropriate to the audience type, task, and data. Second, it teaches methods and techniques for analyzing unstructured data – including text mining, web text mining and social network analysis. Prerequisite: DS 740 | 3 Credits |
Introduction to the theory and applications of deep learning. The course begins with the study of neural networks and how to train them. Various deep learning architectures are introduced including convolutional neural networks, recurrent neural networks, and transformers. Applications may include image classification, object detection, and natural language processing. Algorithms will be implemented in Python using a high-level framework such as Pytorch or TensorFlow. Prerequisites: DS 740.
| 3 Credits |
Explore the current and future applications of data science as a strategic decision-making tool to achieve a competitive advantage in business. With an emphasis on obtaining decision-making value from an organization’s data assets, this course will investigate the use of data science findings to develop solutions to competitive business challenges. Through case studies, you will examine how data science methods can support business decision making, and discover a range of methods the data scientist can use to get people within the organization on board with data science projects. | 3 Credits |
Summer 2025
Registration Opens: March 10, 2025
Course Preview Week: May 20 - May 26, 2025
Semester Dates: May 27 - August 08, 2025
| Course | Credits |
|---|---|
This course will present statistical methods and inference procedures with an emphasis on applications, computer implementation, and interpretation of results. Familiarity with the R programming language is highly recommended. Topics include simple and multiple regression, model selection, correlation, moderation/interaction analysis, logistic regression, the chi-square test, the Kruskal-Wallis test, analysis of variance (ANOVA), multivariate analysis of variance (MANOVA), factor analysis, and canonical correlation analysis. Prerequisite: DS 700 or 701. | 3 Credits |
This course will prepare you to master technical, informational, and persuasive communication to meet organizational goals. Technical communication topics include a study of the nature, structure, and interpretation of data. Informational communication topics include data visualization and design of data for understanding and action. Persuasive communication topics include the study of written, verbal, and nonverbal approaches to influencing decision makers. | 3 Credits |
Explore data mining methods and procedures for diagnostic and predictive analytics. Topics include association rules, clustering algorithms, tools for classification, and ensemble methods. Computer implementation and applications will be emphasized. Prerequisites: DS 700 or 701, DS 705 or DS 710 | 3 Credits |
Explore the current and future applications of data science as a strategic decision-making tool to achieve a competitive advantage in business. With an emphasis on obtaining decision-making value from an organization’s data assets, this course will investigate the use of data science findings to develop solutions to competitive business challenges. Through case studies, you will examine how data science methods can support business decision making, and discover a range of methods the data scientist can use to get people within the organization on board with data science projects. | 3 Credits |
Fall 2025
Registration Opens: April 14, 2025
Course Preview Week: August 26 - September 01, 2025
Semester Dates: September 02 - December 12, 2025
| Course | Credits |
|---|---|
This course introduces data science and highlights its importance in decision making. Students will learn how to analyze data using the R programming language. During the course, students will learn how to import data into R, tidy it, conduct exploratory data analysis, develop visualizations, and draw statistical inferences. The course aims to teach data wrangling, visualization and exploration with R. | 3 Credits |
This course will present statistical methods and inference procedures with an emphasis on applications, computer implementation, and interpretation of results. Familiarity with the R programming language is highly recommended. Topics include simple and multiple regression, model selection, correlation, moderation/interaction analysis, logistic regression, the chi-square test, the Kruskal-Wallis test, analysis of variance (ANOVA), multivariate analysis of variance (MANOVA), factor analysis, and canonical correlation analysis. Prerequisite: DS 700 or 701. | 3 Credits |
Computer programming is an essential part of data science. When working with large data sets, it’s especially important to be able to write effective, efficient code to help you organize and understand the data. In this course, we’ll introduce you to one of the most widely-used programming languages for data science: Python. You’ll gain experience working with real-world data, and leave the course with skills you can apply in other courses in the MS Data Science Program as well as on the job! | 3 Credits |
This course explores the various approaches for data management used in data science. We present how data is collected, transformed, stored, and delivered for use in data science projects. | 3 Credits |
This course prepares you to process large data sets efficiently. You will be introduced to nonrelational databases and algorithms that allow for the distributed processing of large data sets across clusters. Prerequisite: DS 710 | 3 Credits |
Explore data mining methods and procedures for diagnostic and predictive analytics. Topics include association rules, clustering algorithms, tools for classification, and ensemble methods. Computer implementation and applications will be emphasized. Prerequisites: DS 700 or 701, DS 705 or DS 710 | 3 Credits |
Data storytelling involves using data to tell a compelling narrative that helps audiences understand, engage with, and act on the information. This course combines data analysis with communication techniques to present data in an informative and engaging way. This course is specifically designed as a graduate-level requirement for the MSDS degree, focusing on teaching students how to effectively communicate insights through data storytelling techniques. Participants will learn to craft engaging stories that resonate with various audiences and drive decision-making. Prerequisites: DS 700 or 701. DS 705 OR DS 740 suggested but not required. | 3 Credits |
This course examines how data science relates to developing strategies for organizations. The emphasis is on using an organization’s data assets to inform better decisions. The course investigates the use of data science findings to develop solutions to competitive organizational challenges. Special attention is given to critically examining decisions to ensure that they are ethical and avoid unfair bias. Professional codes of conduct as well as local and international regulations are also considered. Prerequisites: DS 740 suggested but not required. | 3 Credits |
Introduction to the theory and applications of deep learning. The course begins with the study of neural networks and how to train them. Various deep learning architectures are introduced including convolutional neural networks, recurrent neural networks, and transformers. Applications may include image classification, object detection, and natural language processing. Algorithms will be implemented in Python using a high-level framework such as Pytorch or TensorFlow. Prerequisites: DS 740.
| 3 Credits |
The University of Wisconsin online Master of Science in Data Science prepares you to be a data scientist through comprehensive and multidisciplinary coursework. Upon completion of your master’s degree, you will possess the following skills and abilities:
Manage and prepare data.
- Collect, prepare, store and manage data to devise solutions to data science tasks.
- Manage and use data in various forms, from traditional databases to big data.
Transform data into insights.
- Design and implement algorithms to translate data into actionable insights.
Communicate solutions.
- Create, write, and orally communicate technical materials for diverse audiences
- Help technical and non-technical professionals visualize, explore, interpret, and act on data science findings.
Tuition is a flat fee of $875 per credit whether you live in Wisconsin or out of state.
There are no additional course or program fees, however, textbooks are purchased separately and are not included in tuition. You will not pay segregated fees (fees in addition to tuition that cover the cost of student-organized activities, facility maintenance, and operations) and you will not be charged a technology fee. If software or special technology is required in one of your courses, it will be provided to you and is included in your tuition.
Students enrolling in certificate programs may be eligible for financial aid. Refer to your home campus for more information.
Experience UW Data Science
Learn about data science, meet the faculty, read student stories, and more. Read the blog.
