Overview
The Bachelor of Science in Data Analytics and Intelligence for Social Impact (DAISI) equips students to become leaders who can collect,, analyze, and use data to generate insights, enabling them to make data-driven decisions that increase social impact. It is a collaborative, interdisciplinary, and customizable program that will prepare UM graduates with the technical capabilities of data intelligence and analytics, critical thinking skills, and a strong theoretical foundation in education and social sciences such as sociology, psychology, geology, communication. As communities require solutions that address the complexities of the challenges they encounter, and organizations, especially non-profits, drive to be more impactful, this integrated approach will offer students the knowledge and skill sets to not only learn how to collect, measure, and report data, but more importantly, they will be able to contextualize the data, detect potential areas for bias, and derive insights that result in more responsible data-driven information and decision-making.
In a collaboration with various departments at UM, students will learn key analytic skills (i.e., data collection, data cleaning, data management, and data analysis) and tools (i.e., Excel, Tableau, SQL, R/SAS/Python programming, and machine learning) that are required for collecting, managing, and analyzing data and persuasively communicating insights that address real-world challenges. In addition to the required courses, students will have the flexibility to customize their studies with a selection of courses drawn from various disciplines. This integrated approach provides students with an opportunity to apply substantive knowledge and skills to a discipline based on their areas of interest and professional goals. The experience culminates in field experiences and practicums which allow students to collaborate with community partners, and critically apply theories, methodologies, and knowledge relevant for more responsible data-driven information and decision-making. This program is ideal for students, who would like to make a definitive, long-lasting social impact that is equally beneficial for all individuals and communities based on more representative and unbiased data as it is applied in various fields such as health services, education, and community development, and public affairs.
Curriculum Requirements
| Code | Title | Credit Hours |
|---|---|---|
| MAJOR REQUIREMENTS (21 courses) | ||
| Introduction to Statistical Concept (1 course): | ||
| EPS 351 | Intro to Statistics for the Social, Behavioral, and Educational Sciences (or equivalent course (PSY292, MAS311, MAS201)) | 3 |
| Foundation: Core Courses (9 courses): | ||
| EPS 201 | Psychosocial Change and Well-being | 3 |
| CIM 203 | Intro to Creative Coding | 3 |
| EPS 251 | Developing Data Wrangling Skills for Social, Behavioral, and Educational Data (Excel/Tableau (or equivalencies)) | 3 |
| EPS 371 | Applied Social Research Methods | 3 |
| EPS 401 | Applied Regression in the Social and Behavioral Sciences | 3 |
| EPS 402 | Statistical Programming: R, Python, and SQL for Social and Behavioral Data | 3 |
| EPS 403 | Applied Machine Learning in the Social and Behavioral Sciences | 3 |
| EPS 405 | Text Mining for the Social and Behavioral Sciences (Text/Sentiment Analysis) | 3 |
| JMM 331 | Introduction to Infographics and Data Visualization | 3 |
| Diversity (Choose minimum 1 course): | 3 | |
| Migration, Well Being, and Human Development | ||
| Creating Belonging Through Dialogue | ||
| Contemporary Issues in Disability and Society | ||
| Practicum (2 courses): | ||
| EPS 578 | Community and Applied Psychological Studies Practicum | 3 |
| EPS 579 | Community and Applied Psychological Studies Practicum Seminar | 3 |
| Advanced Courses (Choose 8 courses): | 24 | |
| Moneyball and Beyond: Ethical and Responsible Sports Analytics | ||
or KIN 406 | Moneyball and beyond: Ethical and Responsible Sports Analytics | |
| Advanced Infographics and Data Visualization | ||
| Survey Methodology for the Social and Behavioral Sciences | ||
| Geographic Information Systems I | ||
| Spatial Data Analysis I | ||
| Behavioral Analytics and Research in Sport Industry | ||
or KIN 407 | Behavioral Analytics and Research in Sport Industry | |
| Applied Multivariate Statistics (Applied Multivariate Statistics) | ||
| Deep Learning and Natural Language Processing (NLP): Foundations and Application (Deep Learning and NLP: Foundations and Applications) | ||
| Augmented Reality | ||
| Design with Artificial Intelligence (AI) | ||
| ELECTIVES (8 courses) Consultation with Academic Advisor | 24 | |
| SPECIAL ELECTIVES (2 courses): | ||
| EPS 589 | Individual Study Professional Development | 3 |
| EPS 504 | Mentored Research Studies | 3 |
| GENERAL EDUCATION REQUIREMENTS | ||
| Written Communication Skills Foote Fellows are not required to complete writing requirements | ||
| WRS 105 | First-Year Writing I | 3 |
| WRS 106 | First-Year Writing II | 3 |
| or ENG 106 | Writing About Literature and Culture | |
| or WRS 107 | First-Year Writing II: STEM | |
| Quantitative Skills: | ||
| MTH 108 | Precalculus Mathematics II | 3 |
| Areas of Knowledge: | ||
| Arts & Humanities Cognate (3 courses) | 9 | |
| People and Society Cognate (3 courses) | 9 | |
| STEM Cognate (fulfilled through the major) | ||
| Total Credit Hours | 120 | |
** Every student majoring in Data Analytics and Intelligence for Social Impact will complete the Advanced Writing and Communication Requirement upon fulfillment of their major courses. These courses have a prerequisite requirement of WRS105 and WRS106/WRS107/ENG106 and will be identified as either writing intensive or as an oral/verbal communication proficiency course or both. Competency in both written and oral communication will also be assessed. Writing intensive courses require a minimum of 2500 written words; assignments will be assessed for analytical ability, synthesis of information, grammar, content, and style. Courses designated as oral/verbal proficiency classes will provide students an opportunity to demonstrate their presentation skills using accurate, standard English structure and syntax, non-verbal cues and gestures, as well as audience-appropriate language. Courses in Data Analytics and Intelligence for Social Impact, which meet the Advanced Writing and Communication Requirements are EPS371, EPS401, and EPS579.
Students in EPS371 are required to demonstrate their competency in both written and oral communication skills by completing a research-intensive paper that summarizes 1) research problems, the population of interests, and the goals/objectives of the project; 2) a literature review of theoretical and empirical backgrounds relevant to the context and research design; and 3) detailed description of the research design, data collection, and data analysis plan. Students in EPS401 are required to demonstrate their competency in both written and oral communication skills by completing a research-intensive paper that (1) describes data collection and analysis procedures, (2) summarizes findings from the data both numerically and visually, and (3) provides the implication of their findings relevant to all potential stakeholders.
Students' research papers in those oral/verbal proficiency classes should be in a format for publication (must follow the style and formatting guidance [e.g., APA, MLA]). Also, peer-, self-, and faculty evaluations of the oral paper presentations will be used to assess students' verbal and non-verbal communication skills.
Sample special electives 1 – Data Analytics and Intelligence for Social Impacts
EPS 311 Group Processes and Development: Fall & Spring
EPS 365 Psychological Study of Children, Families, and the Law, Fall
GSS 315 Gender, Race, and Class, Fall
SOC 487 Race, Ethnicity, and Criminal Justice, Fall & Spring
BPH 305 Issues in Health Disparities Spring
MSC 220 Climate and Global Change Fall & Spring
GEG 410 Geographic Information Systems II
EPS 291 Community and Character Development
EPS 361 Community Psychology and Development
EPS 452 Community Program Development and Evaluation
EPS 462 Community Consultation and Leadership
JMM 309 Storytelling with Data (3 s.h.)
JMM 463 Introduction to Generative AI for Data Analytics (3 s.h.)
JMM 433 Social Media (3 s.h.)
CIM 433 Augmented Reality
CIM 563 Design with Artificial Intelligence (AI) (3 s.h.)
Sample special electives 2 – Data Intelligence and Analytics for Environmental Justice
ECS 113 Introduction to Environmental Policy, Fall & Spring
ECS 302 Perspectives on Environmental Decision Making, Fall & Spring
ECS 371 Readings in Ecosystem Science and Policy, Fall, Spring & Summer
ECS 204 Environmental Statistics, Fall & Spring
MSC 342 Decision Making and the Environment, Spring
MSC 220 Climate and Global Change, Fall & Spring
CIM 203 Intro to Creative Coding
GEG 410 Geographic Information Systems II
Sample special electives 3 - Data Intelligence and Analytics for Public Health
GHS 201 Introduction to Global Health
SOC 321 Applied Health Policy
INS 201 Globalization and Change in World Politics
INS 509 International Migration and the Health Care System
GHS 330 Topics in Global Health Studies: Humanities
COS 324 Health Communication Fall & Spring
BPH 305 Issues in Health Disparities Spring
HCS 465 Public Health Statistics and Data Management, Fall
CIM 563 Design with Artificial Intelligence (AI)
GEG 410 Geographic Information Systems II
Sample special electives 4 - Data Intelligence and Analytics for Criminal Justice
SOC 101 Introduction to Sociology, Fall, Spring, & Summer
SOC 271 Criminal Justice, Fall, Spring, & Summer
SOC 371 Criminology, Fall & Spring
SOC 487 Race, Ethnicity, and Criminal Justice Fall & Spring
GSS 315 Gender, Race, and Class, Fall
EPS 365 Psychological Study of Children, Families, and the Law, Fall
GEG 410 Geographic Information Systems II
Sample special electives 5 - Data Intelligence and Analytics for Sports
EPS 408 Healthcare Data, Remote Management, and the Future of Medicine (3 s.h.)
KIN 422 Introduction to Sport Analytics (3 s.h.)
Suggested Plan of Study
B.S. Data Analytics and Intelligence for Social Impacts
| Freshman Year | ||
|---|---|---|
| Fall | Credit Hours | |
| WRS 105 | First-Year Writing I | 3 |
| MTH 108 | Precalculus Mathematics II | 3 |
| EPS 251 | Developing Data Wrangling Skills for Social, Behavioral, and Educational Data | 3 |
| EPS 351 | Intro to Statistics for the Social, Behavioral, and Educational Sciences | 3 |
| CSC 115 | Python Programming for Everyone | 3 |
| Credit Hours | 15 | |
| Spring | ||
| WRS 106, 107, or ENG 106 | First-Year Writing II or First-Year Writing II: STEM or Writing About Literature and Culture | 3 |
| EPS 401 | Applied Regression in the Social and Behavioral Sciences | 3 |
| EPS 201 | Psychosocial Change and Well-being | 3 |
| EPS 402 | Statistical Programming: R, Python, and SQL for Social and Behavioral Data | 3 |
| JMM 331 | Introduction to Infographics and Data Visualization | 3 |
| EPS 589 | Individual Study (Professional Development Special Elective 1) | 3 |
| Credit Hours | 18 | |
| Sophomore Year | ||
| Fall | ||
| EPS 371 | Applied Social Research Methods | 3 |
| EPS 403 | Applied Machine Learning in the Social and Behavioral Sciences | 3 |
| JMM 429 | Advanced Infographics and Data Visualization | 3 |
| EPS 409 | Applied Multivariate Statistics (Applied Multivariate Statistics) | 3 |
| Arts and Humanities Cognate 1 | 3 | |
| Credit Hours | 15 | |
| Spring | ||
| EPS 405 | Text Mining for the Social and Behavioral Sciences (Deep Learning and NLP: Foundations and Applications) | 3 |
| EPS 372 | Survey Methodology for the Social and Behavioral Sciences | 3 |
| EPS 404 | Deep Learning and Natural Language Processing (NLP): Foundations and Application (Deep Learning and NLP: Foundations and Applications) | 3 |
| EPS 578 | Community and Applied Psychological Studies Practicum | 3 |
| EPS 430 | Creating Belonging Through Dialogue | 3 |
| Credit Hours | 15 | |
| Junior Year | ||
| Fall | ||
| GEG 305 | Spatial Data Analysis I | 3 |
| DAISI Elective 1 | 3 | |
| DAISI Elective 2 | 3 | |
| People and Society Cognate 1 | 3 | |
| EPS 504 | Mentored Research Studies (Team-based research project) | 3 |
| Credit Hours | 15 | |
| Spring | ||
| EPS 406 | Moneyball and Beyond: Ethical and Responsible Sports Analytics | 3 |
| GEG 310 | Geographic Information Systems I | 3 |
| People and Society Cognate 2 | 3 | |
| DAISI Elective 3 | 3 | |
| DAISI Elective 4 | 3 | |
| Credit Hours | 15 | |
| Senior Year | ||
| Fall | ||
| EPS 407 | Behavioral Analytics and Research in Sport Industry | 3 |
| EPS 579 | Community and Applied Psychological Studies Practicum Seminar | 3 |
| DAISI Elective 5 | 3 | |
| DAISI Elective 6 | 3 | |
| Arts and Humanities Cognate 2 | 3 | |
| Credit Hours | 15 | |
| Spring | ||
| DAISI Elective 7 | 3 | |
| DAISI Elective 8 | 3 | |
| Arts and Humanities Cognate 3 | 3 | |
| People and Society Cognate 3 | 3 | |
| Credit Hours | 12 | |
| Total Credit Hours | 120 | |
Additional Major Requirements
| Code | Title | Credit Hours |
|---|---|---|
| MAJOR REQUIREMENTS (20 courses) | ||
| Introduction to Statistical Concepts (1 course) | ||
| EPS 351 | Intro to Statistics for the Social, Behavioral, and Educational Sciences (or equivalent course (PSY292, MAS311, MAS201)) | 3 |
| Core Courses (9 courses) | ||
| EPS 201 | Psychosocial Change and Well-being | 3 |
| CIM 203 | Intro to Creative Coding | 3 |
| EPS 251 | Developing Data Wrangling Skills for Social, Behavioral, and Educational Data (Excel/Tableau (or equivalencies)) | 3 |
| EPS 371 | Applied Social Research Methods | 3 |
| EPS 401 | Applied Regression in the Social and Behavioral Sciences | 3 |
| EPS 402 | Statistical Programming: R, Python, and SQL for Social and Behavioral Data | 3 |
| EPS 403 | Applied Machine Learning in the Social and Behavioral Sciences | 3 |
| EPS 405 | Text Mining for the Social and Behavioral Sciences (Text/Sentiment Analysis) | 3 |
| JMM 331 | Introduction to Infographics and Data Visualization | 3 |
| Practicum (2 courses) | ||
| EPS 578 | Community and Applied Psychological Studies Practicum | 3 |
| EPS 579 | Community and Applied Psychological Studies Practicum Seminar | 3 |
| Advanced Courses (Choose 8 courses) | 24 | |
| Moneyball and Beyond: Ethical and Responsible Sports Analytics | ||
or KIN 406 | Moneyball and beyond: Ethical and Responsible Sports Analytics | |
| Advanced Infographics and Data Visualization | ||
| Survey Methodology for the Social and Behavioral Sciences (Survey research) | ||
| Geographic Information Systems I | ||
| Spatial Data Analysis I | ||
| Behavioral Analytics and Research in Sport Industry | ||
or KIN 407 | Behavioral Analytics and Research in Sport Industry | |
| Deep Learning and Natural Language Processing (NLP): Foundations and Application (Deep Learning and NLP: Foundations and Applications) | ||
| Applied Multivariate Statistics (Applied Multivariate Statistics) | ||
| Augmented Reality | ||
| Design with Artificial Intelligence (AI) | ||
| Total Credit Hours | 60 | |
Program Mission
It is the mission of the Data Analytics and Intelligence for Social Impact (DAISI) program to generate socially responsible change agents, who are empowered with the knowledge, skills, and attitudes to draw unbiassed and equitable data-driven information for policies and practices that promote social, cultural, economic, structural, political, and environmental justice. We strive to produce undergraduates who generate, collect, assess, and analyze trustworthy and reliable data, and can persuasively communicate data-driven insights that increase social impact. With a strong theoretical and methodological foundation for understanding and catalyzing social change; mastering analytic skills and tools required for data intelligence and data analytics in each stage of the data lifecycle, the training in visualization and communication, UM undergraduates will be able to critically assess and understand the deep-rooted complexities of real-world social challenges, systematically tackle the social, cultural, economic, and structural disparities, and contribute to increasing social impact.
Program Goals
The BS in Data Intelligence and Analytics for Social Impact (DAISI) will create an interdisciplinary and collaborative learning environment that teaches students how to critically apply theories, knowledge, skills, and attitudes that will draw data-driven decisions for unbiased, representative, inclusive, and equitable practices and policies based on trustworthy and reliable data and its use. The program promotes an understanding of the social, cultural, economic, political, and structural issues, the value of data-driven decision-making processes and community engagements, and the importance of social responsibility and active participation in civic life.
Student Learning Outcomes
Upon successful completion of this program:
- Students will be able to formulate a research plan to investigate a complex social issue.
- Students will be able to transform raw, complex data into a clean, structured format and develop interactive visualizations to represent key features to stakeholders.
- Students will be able to identify sources of bias in data and analytical models and evaluate their impact on representative and equitable outcomes.
- Students will be able to build a predictive model that is practically useful for addressing a social issue and evaluate the appropriate use of various algorithm.
- Students will be able to persuasively communicate data-driven insights and their social implications to a stakeholder audience.
- Students will be able to integrate skills from across the data lifecycle to execute a complete data project that supports community or individual social good.
