Data Science BS
Students who love math and computer science don’t have to choose between the two. The Data Science major allows a student to build towards a career in big data through statistics, math, business, and computer science coursework. The goal of a data scientist is to assess, analyze, and interpret large volumes of information and create new information from existing data. The skilled data scientist then communicates their findings so that the information can be applied in all types of decision-making scenarios whether in business, government or any other industry.
Data scientists will use the scientific method to test and validate various hypotheses applicable to the data set and will create algorithms and computer processes. Their jobs are related to, and work closely with, data analysts, but are more math and computer programming intensive than an analyst. Similarly, data scientists also work with data engineers; while engineers focus on the computer infrastructure and architecture, a data scientist brings in the knowledge of how to apply the output to various decision-making scenarios.
For example, a data scientist may work within a government agency to predict how an economic policy initiative will impact the unemployment rate in various regions. A data scientist may work with Amazon to detect buying patterns among certain demographic groups and make recommendations for marketing promotions. Or, a data scientist may work in a healthcare organization helping to create an app that uses artificial intelligence to assess self-reported medical symptoms.
Program Type
Major
Program Format
On Campus
Why UWM?
Milwaukee is an excellent place to study data science because many local businesses — including well-known names such as Harley-Davidson, Molson Coors, Rockwell Automation, Johnson Controls, FIS, and Northwestern Mutual — regularly hire data scientists. Several of these businesses run internship programs for undergraduates. This proximity to data science employers gives UWM students an edge in making the connections that will launch a successful career in data science.
With every industry turning to data to improve decision-making and performance, careers related to big data, machine learning and artificial intelligence are among the fastest growing professions today and there is a shortage of trained workers.
A student with combined skills in statistical analysis and computer programming will be in high demand and the profession as a whole is expected to grow by 15% between 2019 and 2029 by the U.S. Bureau of Labor Statistics. Career options with this degree can include data scientist, data analyst, machine learning technician, artificial intelligence programmer, and more.
Entry level jobs can be found with a bachelor’s degree, though many will seek a master’s degree to advance their career and salary potential.
Salaries can vary greatly by region, educational level, and industry, but the national median salary was $122,000 in 2019.
Data Science or Data Analytics?
Data analysts and data scientists fill complementary positions in a company. A data scientist has more training in math and computer science and their duties are more related to the computer programming required to build a good database and extract the right data. The scientist then collaborates with an analyst to understand what data is needed for the problem at-hand and what type of data needs to be generated so that they can write their programming code accordingly. Data scientists are also the people who usually create the algorithms needed to analyze data. If you are more interested in interpreting and applying the results of data collection in a business or industry setting, you may wish to explore UWM’s bachelor’s degree in data analytics.
Overview of the Program
The BSDS requires a minimum of 120 credits which includes 60 credits specific to the major plus additional general education requirements. Students are required to complete either a capstone or an internship.
Through coursework and practical experience, we expect our students will graduate and:
- Be able to integrate methods and concepts from mathematics, statistics and computer science to solve data science problems, including data management and extraction of meaning from data.
- Demonstrate critical thinking related to data science problems and concepts.
- Demonstrate oral and written communication skills related to data science.
- Demonstrate awareness of the ethical aspects of data science
Program Requirements and Curriculum
For admission to the B.S. in Data Science program, students must meet the general requirements of admission to UW-Milwaukee, including high school graduation with 4 units in English, 3 units in Mathematics, 3 units in Natural Science, 3 units in Social Science, and 4 units in academic electives. Performance on the ACT/SAT may also be considered if desired by the applicant, as is an application essay.
However, students who intend to complete the BS in Data Science (BSDS) program in four years will need to enter college prepared to start math at the precalculus level or higher.
The Table below illustrates the curriculum for the proposed program.
General education and breadth courses (36-38 credits)
| Subject | Credits |
|---|---|
| Humanities – English 310 and either Philos 237 or another approved course | 6 |
| Social Science | 6 |
| Natural Science – including at least one lab (if Math 231 is taken, it will count as four credits of natural science) | 6 |
| Cultural Diversity | 3 |
| Arts | 3 |
| Language – 2 units of world language other than English either taken as two years of high school language or two semesters of college language | 6-8 |
Preparatory Courses – Total credits: 23-28
Mathematics
One of the following calculus sequences (or an equivalent) – 8-12 credits*
| Course Code | Course Name |
|---|---|
| MATH 231 & MATH 232 & MATH 233 | Calculus and Analytic Geometry I and Calculus and Analytic Geometry II and Calculus and Analytic Geometry III |
| MATH 211 & MATH 212 | Survey in Calculus and Analytic Geometry I and Survey in Calculus and Analytic Geometry II |
| MATH 234 or MATH 240 | Linear Algebra and Differential Equations Matrices and Applications |
Computer Science
| Course Code | Course Name | Credits |
|---|---|---|
| COMPSCI 250 | Introductory Computer Programming | 3 |
| COMPSCI 251 | Intermediate Computer Programming | 3 |
Statistics
| Course Code | Course Name | Credits |
|---|---|---|
| MTHSTAT 215 or IND ENG 367 | Elementary Statistical Analysis Introductory Statistics for Physical Sciences and Engineering Students | 3 |
| MTHSTAT 216 | Introduction to Statistical Computing and Data Science | 3 |
Core Courses – Total credits: 36
Statistics
| Course Code | Course Name | Credits |
|---|---|---|
| MTHSTAT 361 | Introduction to Mathematical Statistics I | 3 |
| MTHSTAT 362 | Introduction to Mathematical Statistics II | 3 |
| MTHSTAT 563 | Regression Analysis | 3 |
| MTHSTAT 566 | Computational Statistics | 3 |
| MTHSTAT 568 | Multivariate Statistical Analysis | 3 |
Computer Science
| Course Code | Course Name | Credits |
|---|---|---|
| COMPSCI 317 or MATH 341 | Discrete Information Structures Seminar: Introduction to the Language and Practice of Mathematics | 3 |
| COMPSCI 351 | Data Structures and Algorithms | 3 |
| COMPSCI 395 or PHILOS 237 | Social, Professional, and Ethical Issues Technology, Values, and Society | 3 |
| COMPSCI 422 | Introduction to Artificial Intelligence | 3 |
| COMPSCI 411 or COMPSCI 425 | Machine Learning and Applications Introduction to Data Mining | 3 |
| COMPSCI 557 | Introduction to Database Systems | 3 |
Communication and Ethics
| Course Code | Course Name | Credits |
|---|---|---|
| ENGLISH 310 | Writing, Speaking, and Technoscience in the 21st Century | 3 |
Capstone Experience (select one of the options below)
| Course Code | Course Name | Credits |
|---|---|---|
| MTHSTAT 489 | Internship in Mathematical Statistics, Upper Division | 1-6 |
| MATH 599 | Capstone Experience | 1 |
| COMPSCI 595 | Capstone Project | 3 |
| COMPSCI 599 | Senior Thesis | 3 |
Electives (to reach 120 total credits)
Suggested are courses with substantial data analysis, data processing or computational content, such as:
| Course Code | Course Name | Credits |
|---|---|---|
| COMPSCI 315 | Introduction to Computer Organization and Assembly Language Programming | 3 |
| COMPSCI 411 | Machine Learning and Applications | 3 |
| COMPSCI 423 | Introduction to Natural Language Processing | 3 |
| COMPSCI 425 | Introduction to Data Mining | 3 |
| COMPSCI 444 | Introduction to Text Retrieval and Its Applications in Biomedicine | 3 |
| COMPSCI 459 | Fundamentals of Computer Graphics | 3 |
| COMPSCI 469 | Introduction to Computer Security | 3 |
| COMPSCI 535 | Algorithm Design and Analysis | 3 |
| MTHSTAT 562 | Design of Experiments | 3 |
| MTHSTAT 564 | Time Series Analysis | 3 |
| MTHSTAT 565 | Nonparametric Statistics | 3 |
| MATH 315 | Mathematical Programming and Optimization | 3 |
| MATH 318 | Topics in Discrete Mathematics | 3 |
| MATH 583 | Introduction to Probability Models | 3 |
| INFOST 120 | Information Technology Ethics | 3 |
| INFOST 315 | Knowledge Organization for Information Science and Technology | 3 |
| INFOST 465 | Legal Aspects of Information Products and Services | 3 |
| INFOST 660 | Information Policy | 3 |
| INFOST 661 | Information Ethics | 3 |
Are you interested in learning more about the Data Science program and have already started your classes at UWM? You can contact the faculty advisor directly: Professor Daniel Gervini at gervini@uwm.edu.
Not yet enrolled at UWM, contact an admissions counselor at datascience-degrees@uwm.edu.
During your time at UWM, you may have multiple members of your success team, including advisors, peer mentors, and success coaches. Your college advisor will help you:
- Define your academic and life goals
- Create an educational plan that is consistent with those goals;
- Understand curriculum, major and degree requirements for graduation, as well as university policies and procedures; and
- Find campus and community resources and refer you to those resources as appropriate.
The scholarship process for all students and all scholarships (major specific ones and all general scholarships) begins with UWM’s Panther Scholarship Portal.
The first time you log in, you will need to create your general application; this will stay on file for four years but you should update it regularly and must, at a minimum, sign it once per year to re-activate it. You will also need to upload a copy of your transcript twice a year. Log into the Panther Portal twice a year to update your application and upload a newer transcript – we recommend doing this in November/December and in February/March every year.
You may see scholarships on your Panther Scholarship Portal dashboard that you can “Apply To.” These are scholarships which require additional information beyond the general application and transcript. If you do not see “Apply To” you are still being considered as long as your general application and transcript are up-to-date.