Program Overview

Data Science, Certificate

The Data Science certificate is designed to develop abilities such as data management, reproducibility, modeling strategies, and ethical considerations of data science. This certificate is a great fit for students who like programming, want to learn data analysis, and seek to be high-end users of data science tools in domain areas. Data science is one of the fastest-growing career sectors in Wisconsin and across the nation.

How to Get in

Students are eligible to declare the certificate at any point in their studies, however, they should declare it as early as possible to plan the required coursework. Students declared in the Data Science major or the Certificate in Engineering Data Analytics are not eligible to declare the Certificate in Data Science.

Requirements

The certificate requires a minimum of 16 credits.

• Foundation Courses: 10-12 credits
  • Complete two programming courses from:
    • COMP SCI 220: Data Science Programming I
    • COMP SCI 320: Data Science Programming II
    • STAT 240: Data Science Modeling I
    • E C E 204: Data Science & Engineering
  • Complete one ethics course from:
    • L I S 461: Data and Algorithms: Ethics and Policy
    • E C E/I SY E 570: Ethics of Data for Engineers
• Elective Courses: 6 credits
  • Complete a minimum of 6 credits of electives, including at least 3 credits from the Fundamental Electives list.
  • Fundamental Electives:
    • ACT SCI 654: Regression and Time Series for Actuaries
    • ACT SCI 655: Health Analytics
    • ACT SCI 657: Risk Analytics
    • BIOCORE 382: Evolution, Ecology, and Genetics Laboratory
    • BIOCORE 384: Cellular Biology Laboratory
    • BIOCORE 486: Principles of Physiology Laboratory
    • BSE 405: Artificial Intelligence in Agriculture
    • CHEM 361: Machine Learning in Chemistry
    • CIV ENGR 516: Hydrologic Data Analysis
    • COMP SCI 320: Data Science Programming II
    • COMP SCI/E C E/M E 532: Matrix Methods in Machine Learning
    • COMP SCI 544: Introduction to Big Data Systems
    • COMP SCI 565: Introduction to Data Visualization
    • COMP SCI/B M I 576: Introduction to Bioinformatics
    • ECON 315: Data Visualization for Economists
    • ECON 400: Introduction to Applied Econometrics
    • ECON 410: Introductory Econometrics
    • ECON 460: Economic Forecasting
    • ECON 570: Fundamentals of Data Analytics for Economists
    • ECON 695: Topics in Economic Data Analysis
    • ED PSYCH 551: Quantitative Ethnography
    • FINANCE 310: Data Analytics for Finance
    • F&W ECOL 395: Data and GIS Tools for Ecology
    • F&W ECOL 458: Environmental Data Science
    • GEOG 378: Introduction to Geocomputing
    • GEOG 560: Advanced Quantitative Methods
    • GEOG 573: Advanced Geocomputing and Geospatial Big Data Analytics
    • GEOG 574: Geospatial Database Design and Development
    • GEOG 579: GIS and Spatial Analysis
    • I SY E 412: Fundamentals of Industrial Data Analytics
    • I SY E 521: Machine Learning in Action for Industrial Engineers
    • INFO SYS 423: Digital Platform Analytics
    • MATH 444: Graphs and Networks in Data Science
    • MATH 535: Mathematical Methods in Data Science
    • MATH 616: Data-Driven Dynamical Systems, Stochastic Modeling and Prediction
    • PHYSICS 361: Machine Learning in Physics
    • SOC 362: Statistics for Sociologists III
    • SOIL SCI 585: Using R for Soil and Environmental Sciences
    • STAT 340: Data Science Modeling II
    • STAT 405: Data Science Computing Project
    • STAT 436: Statistical Data Visualization
    • STAT/COMP SCI 471: Introduction to Computational Statistics
  • Domain Electives: 0-3 credits
    • A A E/ECON 421: Economic Decision Analysis
    • COMP SCI/E C E/I SY E 524: Introduction to Optimization
    • COMP SCI 541: Theory & Algorithms for Data Science
    • GEN BUS 307: Business Analytics II
    • INFO SYS 322: Introduction to Databases
    • L I S 407: Data Storytelling with Visualization
    • L I S 440: Navigating the Data Revolution: Concepts of Data & Information Science
    • LSC 460: Social Media Analytics
    • LSC 660: Data Analysis in Communications Research
    • SOC 351: Introduction to Survey Methods for Social Research
    • SOC/C&E SOC 365: Data Management for Social Science Research
    • SOC/C&E SOC 618: Social Network Analysis

Residence and Quality of Work

• Minimum 2.000 GPA on all certificate courses
• At least 9 credits must be taken in residence at UW-Madison

Certificate Completion Requirement

This undergraduate certificate must be completed concurrently with the students undergraduate degree. Students cannot delay degree completion to complete the certificate.

Learning Outcomes

1. Apply tools and processes necessary for data management and reproducibility.
2. Produce meaning from data employing modeling strategies.
3. Learn best practices related to data science concepts and methods.
4. Articulate policy, privacy, security, and ethical considerations in data science projects.

Advising and Careers

Data scientists are trained to manage, process, model, gain meaning and knowledge, and present data. These skills can be employed in a wide variety of different sectors of employment. Examples of interests of our students include finance, banking, sports analytics, marketing, retail, humanities, psychology, biosciences, healthcare, and consulting, just to name a few. Students are encouraged to combine Data Science with majors, certificates, and courses from differing areas to best be able to apply their data science in the area of their choosing. Data science is one of the fastest-growing areas of jobs in the United States and in Wisconsin. The Occupational Outlook Handbook (OOH) from the Bureau of Labor Statistics shows the job growth outlook for Data Scientists to be 36% (much faster than average).