Almost all human activity in the world today is measured and recorded as data. Whether we are listening to music, streaming shows, making a doctor’s appointment or booking a flight – we are generating data.
Analyzing that data can vastly improve human lives and business performance. So, it’s not surprising that data analytics is now used routinely even in fields not traditionally associated with data – including the arts, music and creative writing. Data analysts are also highly valued and sought after in business, government, health care, education, social welfare, engineering and manufacturing.
Wherever you want to make your mark on the world, a career in data analytics can help you do it.
With a BS in Data Analytics from UWM, you will train to practice in the field that you’re most passionate about. At UWM, students can tailor their program around six areas to prepare for a data career in that industry: health care, business, social sciences, natural sciences, information science, or geography. With a BS in Data Analytics, you will learn:
- The foundations of analysis – this involves calculus, statistics, data mining and computer programming.
- How to apply data to solving real-world problems.
- The communications skills critical for presenting data in a professional setting.
A well-trained data analyst serves as a bridge between the data and the users of the data, putting meaning and context to the data while also making sure data is used in an ethical manner.
It’s not just tech companies like Google or Facebook that need skilled employees who can work with data. All industries rely on and use data to make important decisions. Manufacturers need to understand the demand for their product and the shopping habits of their customers. The service sector needs to understand product variations and pricing – for example, airlines that need to schedule different routes and different pricing option for a single flight. Retailers need to understand traffic patterns of in-store shoppers to site new locations. Consulting firms need to crunch data related to the behavior of voters, future college students, and consumers of news so they can best advise their clients. And tech companies need to understand the browsing styles of online users.
Why should you consider a Bachelor of Science in Data Analytics (BSDA)?
With every field turning to data to improve decision-making and performance, Data Analytics is one of the fastest growing professions today but there aren’t enough trained data analysts to fill that need. A degree in Data Analytics that trains you to analyze data can therefore give help you in finding jobs with attractive salaries.
A report from the employment outlook firm Burning Glass produced jointly with IBM and the Business Higher Education Forum identified several job categories in the data science and analytics field, including data driven decision makers (“leverage data to inform strategic and operational decisions”) and functional analysts (“utilize data and analytical models to inform specific functions and business decisions”). They estimated a national demand of 1.8 million job postings nationwide for 2020, with a 5-year growth rate of approximately 15%. Importantly, the report also states: “39% of Data Scientists and Advanced Analysts require a Master’s or Ph.D. These degrees take additional years of schooling to complete, so it will take a significant time investment to train a larger pool of workers. Therefore, because these roles are already undersupplied and projected to grow rapidly, the skills shortage is in danger of worsening.”
The Bureau of Labor Statistics also projects that Computer and Information Research Scientists category of jobs will grow 15% over the 2019-2019 period and describes this as: “…much faster than average for all occupations.1 Job prospects are expected to be excellent” and states that the “median annual wage for computer and information research scientists was $126,830 in May 2020.” BLS also classifies this as a category in which most jobs require a Master’s degree.
Additional evidence of demand is also seen in investments made by employers like Northwestern Mutual that have invested significant resources of $15 million in the establishment of the Northwestern Mutual Data Science Institute to support the launch and growth of undergraduate and graduate programs related to data including data science and data analytics.
1 Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, Computer and Information Research Scientists, at https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm (visited January 04, 2022)
Data Analytics or Data Science?
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 computer programming and algorithms, you may wish to explore UWM’s bachelor’s degree in data science.
Overview of the Program
The B.S. in Data Analytics degree consists of 120 credits composed of the following areas: (a) 33 credits of required UW-Milwaukee General Education Requirements, (b) 16 credits of foundations, (c) 33 credits of core courses, (d) 24 credits of Analytics Specialization, and (e) 14 credits of general electives. Students are required to complete a capstone requirement which may be met through an internship, practicum, project, or a thesis in their discipline of interest. The program will be overseen by a faculty oversight committee (FOC) led by a program director. Faculty from participating schools/colleges will make up the oversight committee.
Student Learning Outcomes and Program Objectives
The core objective of the B.S. in Data Analytics is to prepare students for careers in data analytics in a variety of disciplines and employment settings. The program is designed to allow students to progress through key areas involving distinct learning outcomes. Specifically, graduates of the program will be able to:
- Demonstrate a strong understanding of the foundations of data analytics including linear algebra, calculus, statistics, and computer programming.
- Acquire knowledge and concepts that represent the fundamentals of data analytics, including programming languages, databases, analytics, big data, data mining and visualization, statistics, communication, and ethics.
- Apply data analytics concepts inter-disciplinarily to real-world problems in a variety of fields and settings.
- Effectively communicate with users and management during problem formulation, analysis, and investigation, and while presenting the results of the analysis.
- Appreciate and abide by ethical uses of data and insights from the analysis.
The core knowledge in these key areas will be reinforced through a capstone course, either through an internship, fieldwork, or a thesis. Furthermore, a unique feature of this degree, students will have the opportunity to pursue analytics electives related to their primary discipline of interest via courses offered in a variety of disciplines including business, biological sciences, computer science, geography, sociology, among others. Finally, students will round out their degree through general electives, which can extend their area of focus, supplement it with electives from complementary areas including nursing, health sciences, and public health, or apply the credits towards a complementary discipline-specific certificate. Additionally, the general education outcomes which are based on the UW System Shared Learning Goals will apply to all students in the program.
Program Requirements and Curriculum
For admission to the B.S. in Data Analytics 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.
The Table below illustrates the curriculum for the proposed program. The program requirements are comprised of 120 credits, of which there are 16 credits of foundations, 33 credits of core courses, 24 credits of analytics specialization courses, and 14 credits of general electives, and UW-Milwaukee general education requirements.
General education and breadth courses (33 credits)
|Oral and Written Communication Part A||3|
|Oral and Written Communication Part B: ENGLISH 205 Business Writing||3|
|Quantitative Literacy Part A Algebra Requirement||3|
|Quantitative Literacy Part B Calculus Requirement: MATH 208 |
or one of 211 or 213, 221, 231
|Natural Sciences including one lab or field experience||6|
Foundations (16 credits)
|MATH 240 Matrices and Applications||3|
|MATH 212 Survey in Calculus and Analytic Geometry II||4|
|BUS ADM 210 Statistical Modeling in Business Analytics or |
BUS ADM 211 Business Scholars: Statistical Modeling in Business Analytics or
Econ 210 Economic Statistics or MTHSTAT 215 Elementary Statistical Analysis
|Computer Literacy 1|
BUS ADM 230 Introduction to Information Technology Management,
HS 224 Computational Tools for Healthcare Professionals,
COMPSCI 150 Survey of Computer Science
|Computer Literacy 2|
COMPSCI 202 Introductory Programming Using Python,
240 Introduction to Engineering Programming,
250 Introductory Computer Programming, or
INFO ST 350 Introduction to Application Development
a computer literacy 1 and 2 can be satisfied by COMPSCI 250 and 251
Core courses (33 credits)
(2 of the following 10 courses – 6 credits)
|BUS ADM 335 Introduction to Business Application Development||3|
|BUS ADM 432 Object-Oriented Systems Development||3|
|INFO ST 350 Introduction to Application Development*||3|
|INFO ST 440 Web Application Development||3|
|BIO SCI 502 Introduction to Programming and Modeling in Ecology and Evolution||3|
|COMPSCI 351 Data Structures and Algorithms||3|
|MTHSTAT 216 Introduction to Statistical Computing and Data Science or |
MTHSTAT 566 Computational Statistics
|GOEG 325 Introduction to Data Science with R, Python, and GIS||3|
|GOEG 215 Introduction to Geographic Information Science||3|
|GOEG 525 Geographic Information Science||3|
(1 of the following 4 courses – 3 credits)
|BUS ADM 434 Data Base Management Systems||3|
|INFO ST 410 Database Information Retrieval Systems||3|
|HCA 537 Health Information Technology and Management||3|
|Comp Sci 557 Introduction to Database Systems||3|
Analytics and Big Data/Data Mining
(2 of the following 8 courses – 6 credits)
|BUS ADM 336 Enterprise Systems and Data Analytics||3|
|BUS ADM 536 Business Intelligence||3|
|INFO ST 582 Introduction to Data Science||3|
|INFO ST 687 Data Analysis for Data Science||3|
|AtmSci 600 Data Analytics||3|
|COMPSCI 411 Machine Learning and Applications||3|
|COMPSCI 422 Introduction to Artificial Intelligence||3|
|COMPSCI 425 Introduction to Data Mining||3|
|Econ 411 Economic Forecasting Methods||3|
|INFO ST 691 Special Topics – Computer Forensics*||3|
(1 of the following 3 courses – 3 credits)
|BUS ADM 438 Information Technology Management Topics: Social Network Analytics||3|
|INFO ST 370 Data Analysis and Visualization for the Information Professional||3|
|GOEG 405 Cartography||3|
(2 of the following 5 courses – 6 credits)
|MTHSTAT 361 Introduction to Mathematical Statistics I||3|
|MTHSTAT 362 Introduction to Mathematical Statistics II||3|
|ATM SCI 500 Statistical Methods in Atmospheric Sciences||3|
|ECON 413 Statistics for Economists||3|
|ECON 513 Introduction to Econometrics||3|
|ENGLISH 310 Writing, Speaking, and Technoscience in the 21st Century||3|
Ethics (1 of the following 6 courses – 3 credits)
|BUS ADM 530 Introduction to eBusiness||3|
|HS 311 Law and Ethics for Healthcare Professionals||3|
|PHILOS 237 Technology, Values, and Society||3|
|COMPSCI 395 Social, Professional, and Ethical Issues||3|
|SOCIOL 327 Data, Technology, and Society||3|
|INFO ST 661 Information Ethics||3|
Capstone / Fieldwork / Thesis
(1 of the following 21 courses – 3 credits)
|BUS ADM 389 Real Estate Internship||3|
|BUS ADM 394 Human Resources Management Internship||3|
|BUS ADM 396 Finance Internship||3|
|BUS ADM 397 Marketing Internship||3|
|BUS ADM 398 Supply Chain & Operations Management Internship||3|
|BUS ADM 400 Accounting Professional Internship||3|
|BUS ADM 439 Information Technology Management Professional Internship||3|
|BUS ADM 459 Finance Professional Internship||3|
|BUS ADM 469 Marketing Professional Internship||3|
|BUS ADM 479 Supply Chain & Operations Management Professional Internship||3|
|BUS ADM 494 International Business Internship||3|
|BUS ADM 534 Information Technology Practicum||3|
|BUS ADM 600 Management Analysis||3|
|Econ 489: Internship in Economics, Upper Division||3|
|INFO ST 408 Nonprofit Information Technology||3|
|INFO ST 490 Senior Capstone||3|
|INFO ST 495 Information Internship||3|
|Comp Sci 595 Capstone Project||3|
|MTHSTAT 489 Internship in Mathematical Statistics, Upper Division||3|
|MATH 599 Capstone||3|
|GOEG 600 Perspectives on Geography||3|
|GOEG 698 GIS/Cartography Internship||3|
Electives in an area of specialization
(choose 24 credits in an area)
|BUS ADM 536 Business Intelligence||3|
|BUS ADM 532 Web Development for Open Business Systems||3|
|BUS ADM 533 Introduction to Connected Systems for Business||3|
|BUS ADM 537 Enterprise Systems Concepts and Issues||3|
|BUS ADM 539 Web Application Server Development||3|
|BUS ADM 540 ERP Certification||3|
|BUS ADM 370 Introduction to Supply Chain Management||3|
|BUS ADM 478 Supply Chain Analytics||3|
|BUS ADM 571 Quality and Six Sigma Tools||3|
|BUS ADM 436 Systems Analysis and Design||3|
|BUS ADM 360 Principles of Marketing||3|
|BUS ADM 462 Marketing Research||3|
|BUS ADM 350 Principles of Finance and||3|
|BUS ADM 450 Intermediate Finance||3|
|BUS ADM 451 Investment Finance||3|
|BUS ADM 457 Financial Modeling||3|
|BUS ADM 458 Venture Finance||3|
|Recommend: BUS ADM 300 Career and Professional Development||3|
Information Science and Technology
|INFO ST 240 Web Design I||3|
|INFO ST 350 Introduction to Application Development||3|
|INFO ST 315 Knowledge Organization for Information Science and Technology||3|
|INFO ST 340 Introduction to Systems Analysis||3|
|INFO ST 320 Web Design II||3|
|INFO ST 325 Information Security I||3|
|INFO ST 375 Multimedia Web Design||3|
|INFO ST 383 Native Mobile Applications||3|
|INFO ST 430 Multimedia Application Development||3|
|INFO ST 465 Legal Aspects of Information Products and Services||3|
|INFO ST 583 Survey of Information Security||3|
|INFO ST 584 Survey of Web and Mobile Content Development||3|
|INFO ST 695 Ethical Hacking I||3|
|INFO ST 491* Advanced Topics in Information Science & Technology;||3|
|INFO ST 691* Special Topics in Information Science||3|
This specialization will require 3-6 credits from a different specialization as approved by the Program Director.
|HCA 444 Introduction to Text Retrieval and Its Applications in Biomedicine||3|
|HCA 307 Epidemiology for the Health Sciences||3|
|HCA 541 Healthcare Information Systems Analysis and Design||3|
|HCA 542 Healthcare Database Design and Management||3|
|PH 355 Public Health Research Methods I||3|
|PH 410 True Lies: Consuming and Communicating Quantitative Information||3|
|PH 455 Public Health Research Methods II||3|
|Recommend: HS 222 Language of Medicine|
or BMS 205 Introduction to Diagnostic Medicine
or NURS 352 Health and Illness Concepts 1: Introduction
|BIO SCI 469 Genomic Data Analysis||3|
|FRSHWTR 640 Sequence Analysis||3|
|FRSHWTR 504 Quantitative Freshwater Analysis||3|
|FRSHWTR 514 Analytical Techniques in Freshwater Sciences||3|
|MTHSTAT 563 Regression Analysis||3|
|MTHSTAT 564 Time Series Analysis||3|
|MTHSTAT 568 Multivariate Statistical Analysis||3|
|MATH 571 Introduction to Probability Models||3|
|ACTSCI 391 Investment Mathematics I||3|
|ACTSCI 591 Investment Mathematics II||3|
|ACTSCI 593 Actuarial Models I||3|
|ACTSCI 594 Actuarial Models II||3|
|ACTSCI 596 Actuarial Statistics I||3|
|ACTSCI 597 Actuarial Statistics II||3|
Choose at most one of the following methods courses:
|CRM JST 662 Methods of Social Welfare Research||3|
|POL SCI203 Introduction to Political Science Research||3|
|PSYCH 325 Research Methods in Psychology||3|
|AFRIC 301 Research Methods in African & African Diaspora Studies||3|
|SOCIOL 361 Research Methods in Sociology||3|
Choose at most one of the following multiple regression courses:
|ECON 310 Research Methods for Economics||3|
|PSYCH 610 Experimental Design||3|
|SOCIOL 461 Social Data Analysis Using Regression||3|
Take courses from the list below to complete 24 credits:
|CRM JST 510 Introduction to Crime Analysis||3|
|CRM JST 520 Analysis Oriented Technology: Spatial Data Analysis;|
Crime Mapping; ArcGIS
|GOEG 215 Introduction to Geographic Information Science||3|
|GOEG 525 Geographic Information Science||3|
|GOEG 547 Spatial Analysis||3|
|POL SCI390 Political Data Analysis||3|
|POL SCI392 Survey Research||3|
|PSYCH 510 Advanced Psychological Statistics||3|
|SOCIOL 352 Social Networks||3|
|GEOG 403 Remote Sensing: Environmental and Land Use Analysis||3|
|GEOG 437 Qualitative Methods in Geography qualitative data is data||3|
|GEOG 547 Spatial Analysis||3|
|GEOG 515 Watershed Analysis and Modeling||3|
|GEOG 625 Intermediate Geographic Information Science||3|
|GEOG 647 ArcGIS Programming with Python||3|
|URBPLAN 591 Introduction to Urban Geographic Information Systems GIS in Planning||3|
|CRM JST 520 Analysis Oriented Technology: Spatial Data Analysis||3|
Are you interested in learning more about the Data Analytics program? You can contact the program advisor at firstname.lastname@example.org.
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.