Data Analytics and Applied Artificial Intelligence, BS (School of Information Studies)
There is data all around us. Businesses are looking to hire people who can manage that data, analyze it, and use it for more effective decision making. The Bachelor of Science in Data Analytics (BSDA) is designed for you to learn those skills.
The Bachelor of Science in Data Analytics is a special degree program that includes courses from the College of Letters & Science, the College of Community Engagement & Professions, the Lubar College of Business, and the College of Engineering & Applied Science to provide a solid general education as well as an interdisciplinary approach to data analytics.
The BS in Data Analytics at UWM is unique because its goal is to train students to practice data analytics in a field they are most passionate about. If you enroll in this program, you will take foundational classes to build core data analytics skills, then specialize in data analytics for business, health, information science, natural sciences, social sciences, or geographic information sciences.
The career prospects for individuals with data analytics degrees are very positive. Data analytics skills are being used not only in industries that are obviously oriented toward using data, like information technology, sciences and business, but also in fields that more recently have begun to take full advantage of their data resources, like agriculture, atmospheric sciences, environmental sciences, geography, and healthcare.
Requirements
| Code | Title | Credits |
|---|---|---|
| General Education Requirements | 30 | |
| Major Requirements | 73 | |
| Electives | 17 | |
| Total Credits | 120 | |
Credit numbers reflect total possible credits towards degree. Due to the ability to count courses towards more than one requirement, credit amounts will vary. Please work with your academic advisor on your plan of study.
Preparatory Coursework
Based on individual placement results, some students may be required to complete preparatory coursework before enrolling in the courses listed here. This may include English language or composition preparation, developmental math, introductory chemistry, and/or student support courses for students participating in the First Year Bridge program.
General Education Requirements (GER)
UW-Milwaukee has General Education Requirements that must be met in order to earn a bachelor’s or associate degree. They include at minimum 30 credits (10 courses) in six categories that are designed to assure basic student competencies and provide a broad body of knowledge as a context for specialization.
Some degree requirements may fulfill GERs. Please review the requirements and consult with your academic advisor.
| Code | Title | Credits |
|---|---|---|
| General Education Categories and Credits | ||
| Civics and Perspectives (CP) | 6 | |
| Communication and Literacy (CL) | 6 | |
| Humanities and Arts (HA) | 6 | |
| Mathematics and Quantitative Reasoning (MQR) | 3 | |
| Natural Science and Wellness (NSW/NSWL) | 6 | |
| Social and Behavioral Science (SBS) | 3 | |
| Total Credits | 30 | |
Major Requirements
The B.S. in Data Analytics and Applied Artificial Intelligence requires 16 credits in Foundation courses, 33 credits in Core courses, 24 credits in a Specialization, and electives to reach a total of 120 credits.
An average GPA of 2.000 on all coursework attempted at UWM is required for this degree. In addition, students must achieve an average 2.000 GPA on all coursework attempted, including transfer work. A minimum 2.000 GPA must be earned, on average, on 300-level and above courses taken to satisfy the advanced requirements, including transfer work. Students satisfy the residency requirement for the degree by completing at UWM both a minimum of 15 credits of the required advanced courses in the major (300 level and above) and a minimum one of 30 credits overall.
Foundation Courses
| Code | Title | Credits |
|---|---|---|
| Mathematics | ||
| MATH 212 | Survey in Calculus and Analytic Geometry II | 4 |
| MATH 240 | Matrices and Applications | 3 |
| Statistics | ||
| Choose one of the following: | 3 | |
| Statistical Modeling in Business Analytics | ||
| Business Scholars: Statistical Modeling in Business Analytics | ||
| Economic Statistics | ||
| Elementary Statistical Analysis | ||
| Computer Literacy 1 1 | ||
| Choose one of the following: | 3 | |
| Introduction to Information Technology Management | ||
| Survey of Computer Science | ||
| Computational Tools for Healthcare Professionals | ||
| Computer Literacy 2 1 | ||
| Choose one of the following: | 3 | |
| Introductory Programming Using Python | ||
| Introduction to Engineering Programming | ||
| Introductory Computer Programming | ||
| Introduction to Application Development | ||
| Total Credits | 16 | |
- 1
Computer Literacy 1 and 2 can be satisfied by COMPSCI 250 and COMPSCI 251.
Core Courses
| Code | Title | Credits |
|---|---|---|
| Programming Languages | ||
| Choose two of the following: | 6 | |
| Introduction to Business Application Development | ||
| Object-Oriented Systems Development | ||
| Introduction to Application Development (Cannot be used in this category if it was used to satisfy the ‘Computer Literacy 2’ requirement) | ||
| Web Application Development | ||
| Introduction to Programming and Modeling in Ecology and Evolution | ||
| Data Structures and Algorithms | ||
| Introduction to Statistical Computing and Data Science (Cannot be double counted in both the Programming Languages and Statistics core categories) | ||
| Databases | ||
| Choose one of the following: | 3 | |
| Data Base Management Systems | ||
| Database Information Retrieval Systems | ||
| Health Information Technology and Management | ||
| Introduction to Database Systems | ||
| Analytics and Artificial Intelligence | ||
| Choose two of the following: | 6 | |
| ERP Simulation and Data Analysis | ||
| Introduction to Machine Learning for Business | ||
| Introduction to Artificial Intelligence for Business | ||
| Introduction to Social Media Analytics for Business | ||
| Business Intelligence | ||
| Introduction to Data Science | ||
| Data Analysis for Data Science | ||
| Special Topics in Information Science: ('Computer Forensics' is eligible. Other topics offered in a specific offering of this course must be approved for the degree by the Director of the Program.) | ||
| Data Analytics | ||
| Machine Learning and Applications | ||
| Introduction to Artificial Intelligence | ||
| Introduction to Data Mining | ||
| Economic Forecasting Methods | ||
| Introduction to Geographic Information Science | ||
| Geographic Information Science (4 credits) | ||
| Visualization | ||
| Choose one of the following: | 3 | |
| Information Technology Management Topics: | ||
| Data Analysis and Visualization for the Information Professional | ||
| Cartography (4 credits) | ||
| Statistics | ||
| Choose two of the following: | 6 | |
| Statistical Methods in Atmospheric Sciences | ||
| Introduction to Econometrics and Data Science | ||
| Economic Forecasting Methods | ||
| Introduction to Statistical Computing and Data Science (Cannot be double counted in both the Programming Languages and Statistics core categories) | ||
| Introduction to Mathematical Statistics I | ||
| Introduction to Mathematical Statistics II | ||
| Communication | ||
| ENGLISH 310 | Writing, Speaking, and Technoscience in the 21st Century | 3 |
| Ethics | ||
| Choose one of the following: | 3 | |
| Privacy and Information Security for Business | ||
| Information Ethics | ||
| Social, Professional, and Ethical Issues | ||
| Law and Ethics for Healthcare Professionals | ||
| Technology, Values, and Society | ||
| Data, Technology, and Society | ||
| Capstone/Fieldwork/Thesis | ||
| Choose one of the following: | 3 | |
| Real Estate Internship | ||
| Human Resources Management Internship | ||
| Finance Internship | ||
| Marketing Internship | ||
| Supply Chain & Operations Management Internship | ||
| Accounting Professional Internship | ||
| Information Technology Management Professional Internship | ||
| Finance Professional Internship | ||
| Marketing Professional Internship | ||
| Supply Chain & Operations Management Professional Internship | ||
| International Business Internship | ||
| Information Technology Practicum | ||
| Management Analysis | ||
| Nonprofit Information Technology | ||
| Senior Capstone | ||
| Information Internship | ||
| Capstone Project | ||
| Internship in Economics, Upper Division | ||
| Internship in Mathematical Statistics, Upper Division | ||
| Capstone Experience (1 credit) | ||
| Perspectives on Geography | ||
| GIS/Cartography Internship | ||
| Total Credits | 33 | |
Electives in Different Specializations (24 credits in each specialization)
| Code | Title | Credits |
|---|---|---|
| Business | 24 | |
| Select any 24 credits; Sub-specializations are listed so students may focus their coursework. | ||
| Web Development for Open Business Systems | ||
| Introduction to Connected Systems for Business | ||
| Business Intelligence | ||
| ERP Concepts and Issues | ||
| Web Application Server Development | ||
| ERP Certification | ||
Supply Chain | ||
| Introduction to Supply Chain Management | ||
| Systems Analysis and Design | ||
| Supply Chain Analytics | ||
| Quality and Six Sigma Tools | ||
Marketing | ||
| Principles of Marketing | ||
| Marketing Research | ||
Finance | ||
| Principles of Finance | ||
| Intermediate Finance | ||
| Investment Finance | ||
| Financial Modeling | ||
| Venture Finance | ||
Recommended 2 | ||
| Career and Professional Development (1 credit) | ||
| Information Science and Technology | 24 | |
| Web Design I | ||
| Generative AI Literacy | ||
| Knowledge Organization for Information Science and Technology | ||
| Web Design II | ||
| Information Security I | ||
| Introduction to Systems Analysis | ||
| Introduction to Application Development (If not used already as part of the Foundations requirement) | ||
| Multimedia Web Design | ||
| Native Mobile Applications | ||
| Legal Aspects of Information Products and Services | ||
| Foundations of Artificial Intelligence in Information Science & Technology | ||
| Advanced Topics in Information Science & Technology: 3 | ||
| Survey of Information Security | ||
| Survey of Web and Mobile Content Development | ||
| Special Topics in Information Science: 3 | ||
| Ethical Hacking I | ||
| Ethical Hacking II | ||
| Health | 24 | |
| This specialization will require 3-6 credits from a different specialization as approved by the Program Director. | ||
| Epidemiology for the Health Sciences | ||
| Introduction to Text Retrieval and Its Applications in Biomedicine | ||
| Healthcare Information Systems Analysis and Design | ||
| Public Health Research Methods I | ||
| True Lies: Consuming and Communicating Quantitative Information | ||
| Public Health Research Methods II | ||
Recommend one of the following: 2 | ||
| Language of Medicine | ||
| Foundations of Diagnostic Science: Exploring Health, Technology, and Ethics | ||
| Health and Illness Concepts 1: Introduction | ||
| Natural Sciences | 24 | |
| Biostatistics | ||
| Genomic Data Analysis (2 credits) | ||
| Introduction to Environmental Data Systems | ||
| Quantitative Freshwater Analysis | ||
| Analytical Techniques in Freshwater Sciences | ||
| Applied Water Statistics and Data Manipulation | ||
| Sequence Analysis | ||
| Data Preparation and Exploration | ||
| Design of Experiments | ||
| Regression Analysis | ||
| Time Series Analysis | ||
| Multivariate Statistical Analysis | ||
| Introduction to Probability Models | ||
| Investment Mathematics I (4 credits) | ||
| Investment Mathematics II | ||
| Actuarial Models I | ||
| Actuarial Models II | ||
| Actuarial Statistics I | ||
| Actuarial Statistics II | ||
| Social Sciences | 24 | |
| Choose at most one of the following methods courses: | ||
| Methods of Social Welfare Research | ||
| Introduction to Political Science Research | ||
| Research Methods in Psychology (4 credits) | ||
| Research Methods in African & African Diaspora Studies | ||
| Research Methods & Data in Sociology | ||
| Choose at most one of the following multiple regression courses: | ||
| Introduction to Econometrics and Data Science | ||
| Experimental Design | ||
| Social Data Analysis Using Regression | ||
| And, take courses from the list below to complete 24 credits: | ||
| Introduction to Crime Analysis | ||
| Analysis Oriented Technology: Spatial Data Analysis; Crime Mapping; ArcGIS | ||
| Data Driven Policing Strategies and Police Intelligence | ||
| Introduction to Geographic Information Science | ||
| Geographic Information Science (4 credits) | ||
| Spatial Analysis (4 credits) | ||
| Political Data Analysis | ||
| Survey Research | ||
| Cyberpolitics | ||
| Data Science for Psychology | ||
| Advanced Psychological Statistics | ||
| Social Networks | ||
| Geographic Information Science | 24 | |
| Data Science and Environmental Applications | ||
| Remote Sensing: Environmental and Land Use Analysis (4 credits) | ||
| Qualitative Methods in Geography | ||
| Spatial Analysis (4 credits) | ||
| Watershed Analysis and Modeling | ||
| Intermediate Geographic Information Science (4 credits) | ||
| Introduction to Urban Geographic Information Systems (GIS) in Planning | ||
| Analysis Oriented Technology: Spatial Data Analysis; Crime Mapping; ArcGIS | ||
- 2
Recommended courses do not count toward the specialization unless approved by the Director. They are merely recommended additional courses.
- 3
Specific topics courses need to be approved for the degree by the Program Director. A topic course cannot be used again if applied to a prior degree requirement category.
General Electives
With the help of their academic advisor, students will select electives to complete the 120 total credits required for the degree. Electives are tailored to each student’s interests and career goals.
Second Degree
A student wishing to complete a second degree in BSDA will need to complete all 33 credits of the Core Courses. They must complete the Foundations courses to be eligible for this degree. They are not required to complete the Electives with specialization, as their first major may fulfill that role in the degree.
College of Community Engagement and Professions Dean's Honor List
GPA of 3.750 or above, earned on a full-time student's GPA on 12 or more graded credits in a given semester.
Honors College Degree and Honors College Degree with Distinction
Granted to graduating seniors who complete Honors College requirements, as listed in the Honors College section of this site.
Commencement Honors
Students with a cumulative GPA of 3.500 or above, based on a minimum of 40 graded UWM credits earned prior to the final semester, will receive all-university commencement honors and be awarded the traditional gold cord at the December or May Honors Convocation. Please note that for honors calculation, the GPA is not rounded and is truncated at the third decimal (e.g., 3.499).
Final Honors
Earned on a minimum of 60 graded UWM credits: Cum Laude - 3.500 or above; Magna Cum Laude - 3.650 or above; Summa Cum Laude - 3.800 or above.