Data Analytics
College of Arts and Sciences, Interdisciplinary Program
Director: Kim (ECON); Advisory committee: Barlow (OPMT), Berg (CISC), Lynch (POLS), Marrinan (CISC), Okamoto (BIOL), Shemyakin (MATH)
Data Analytics Program website
Contact: dataanalytics@stthomas.edu
Data Analytics is the practice of using data to drive strategy and decision-making. Data analysts use cutting-edge statistical and technological tools to discover trends and predict outcomes in nearly every sphere of contemporary life, including healthcare, social media, education, public policy, business, politics, climate change, criminal justice, insurance, travel, and recreation.
The defining characteristic of the interdisciplinary Data Analytics major is the explicit emphasis that statistical, computational, and context-specific knowledge jointly contribute to effective data analysis. Hence, the purpose of the Data Analytics major is three-fold:
- To equip students with the statistical and computational tools to conduct meaningful data analyses;
- To provide students with the disciplinary-specific context to articulate and comprehend the meaningful data analytic questions within a domain;
- To encourage students to develop their skills to effectively communicate data-driven insights.
Major in Data Analytics (B.S.)
Around 63 credits
Core Requirements*:
The major core requirements in computer science and statistics equip students with the analytical methods and techniques required to formulate and conduct meaningful data analyses.
- CISC 131 Introduction to Programming and Problem Solving (4 credits)
- CISC 260^ Data Fundamentals and Applications (4 credits)
or CISC 450 Database I (4 credits) - STAT 220 Statistics I (4 credits)
or STAT 314 Mathematical Statistics (4 credits) - STAT 320 Statistics II (4 credits)
or STAT 333 Predictive Modeling (4 credits)
or ECON 315 Introduction to Econometrics (4 credits) - STAT 336 Data Communication and Visualization (4 credits)
or CISC 360 Data Visualization (4 credits) - STAT 360 Computational Methods in Statistics (4 credits)
- STAT 400 Data Mining and Machine Learning (4 credits)
- DATA 200 Data Analytics Seminar (1 credit)
- DATA 400 Data Analytics Capstone (2 credits)
*Note: A grade of C- or above must be earned by majors in these courses.
^Note: Students who receive credit for CISC 260 may not receive credit for CISC 200, CISC 110 or CISC 216.
Note: Students in most domains are strongly encouraged to take STAT 220 with the R lab and should consult with their advisors to determine which STAT 220 lab is appropriate.
Allied Requirements:
- MATH 113 Calculus I (4 credits)
or (MATH 108 Calculus with Review I (4 credits) and MATH 109 Calculus with Review II (4 credits)) - COMM 100 Public Speaking (4 credits)
or COMM 105 Communication in the Workplace (4 credits)
or STCM 111 Introduction to Journalism and Mass Media (4 credits) - ENGL 256 Introduction to Professional Writing (4 credits)
Domain Requirements:
A domain is an area of application for data analytics; its purpose is to provide students the disciplinary-specific context to articulate and understand meaningful questions within a domain that can be addressed using data analytics. Therefore, each student must select a domain to develop a theoretical foundation to engage their statistical and technological competencies.
A domain will typically consist of 16–20 credits; the exact number of required credits depends on the domain. Each domain consists of the relevant introductory and upper-level courses for building a domain-oriented lens for data analysis. In addition, each domain must provide their students an opportunity for applied data analysis within the context of the domain. Furthermore, students are required to present their analysis in some formal manner (i.e., visual, written, and/or oral). Domain departments are responsible for the implementation and approval of any domain-related requirements.
The suitability and availability of domain courses that support the major potentially varies—both across domains and over time. Therefore, domain courses must be chosen in consultation with, and approved by, the student’s domain advisor or the domain department chair to ensure the selection of the most appropriate courses.
Domain courses marked with * may have formal pre-requisites or co-requisites that may be waived on a case-by-case basis in consultation with the student’s domain department chair.
Actuarial Science [20 credits]
Introductory Courses:
- MATH 114 Calculus II (4 credits)
- MATH 200 Multi-Variable Calculus (4 credits)
Upper-Level Courses:
- ACSC 264 Theory of Interest (4 credits)
or ACSC 220 Risk Management and Insurance (4 credits) - MATH 240 Linear Algebra (4 credits)
Applied Data Analysis:
- STAT 460 Statistical Research/Practicum (4 credits)
Note: Students following the Actuarial Science domain are strongly encouraged to take MATH 303 or STAT 314 as a substitute for the STAT 220 core requirement (please see Data Analytics program director). In addition, students are strongly encouraged to take STAT 333 in the core. Both of these recommendations are meant to take advantage of the existing actuarial focus that occurs in the recommended courses mentioned.
American Politics [16-20 credits]
Introductory Courses:
- POLS 104 Government and Politics (4 credits)
- POLS 205 Citizen Participation and Public Policy (4 credits)
Upper-Level Courses—choose two of the following:
- POLS 301 Political Identity and Participation (4 credits)
- POLS 305 Congress and the Presidency (4 credits)
- POLS 312 Judicial Process and Politics (4 credits)
Applied Data Analysis—choose one of the following:
- POLS 404 Seminar in American Politics (4 credits)
- POLS 495 Individual Study (4 credits)
- Other approved undergraduate research opportunity (e.g., Young Scholars)
Biology [16 credits]
Introductory Course—choose one of the following, or consult with Biology Department Chair or Biology Domain Advisor for additional options:
- BIOL 101 General Biology (4 credits)
- BIOL 102 Conservation Biology (4 credits)
- BIOL 105 Human Biology (4 credits)
- BIOL 110 Genetics and Society
- BIOL 162 Medical Geology (4 credits)
- *BIOL 207 Genetics, Ecology Evolution (4 credits)
- *BIOL 208 Biological Communication and Energetics (4 credits)
- *BIOL 209 Biology of Sustainability (4 credits)
Upper-Level Courses—to be determined in consultation with Biology Department Chair or Biology Domain Advisor:
- One BIOL 200- or 300-level course (4 credits)
- One BIOL 300-level course (4 credits). Likely course options include:
- *BIOL 391 Research (4 credits)
- *BIOL 398 Darwin’s Algorithms (4 credits)
- *ESCI 310 Environmental Problem Solving (4 credits)
Applied Data Analysis:
- One BIOL 400-level course (4 credits) that fulfills the “applied data analysis” requirement to be determined in consultation with Biology Department Chair or Biology Domain Advisor. Likely course options include:
- *BIOL 464 Bioinformatics (4 credits)
- *BIOL 467 Emerging Infectious Disease (4 credits)
- *BIOL 480 Urban Ecosystem Ecology (4 credits)
- *BIOL 486 Seminar (4 credits)
- *BIOL 491 Research (4 credits)
Note: Courses marked with * may have pre-requisites or co-requisites. These requirements can be waived in some cases. Students should consult Biology Department Chair or Course Instructor for waiver options.
Chemistry [16-20 credits]
Students complete one of the following introductory course options and choose sufficient upper-level courses that focus on a sub-discipline (analytical, organic, physical, or biochemistry) to yield a total of 16 credits of introductory and upper-level courses.
Introductory Courses—choose one of the following options:
- CHEM 111 General Chemistry I (4 credits) and CHEM 112 General ChemistryII (4 credits)
- CHEM 115 Accelerated General Chemistry (4 credits)
- CHEM 109 General Chemistry for Engineering (4 credits)
Note: The introductory course requirement may be waived for students with sufficient chemistry background, in which case additional upper-level courseswill be required to reach 16 credits.
Upper-Level Courses—select from the following:
- *CHEM 201 Organic Chemistry II (4 credits)
- *CHEM 202 Organic Chemistry II (4 credits)
- *CHEM 300 Quantitative Analysis (4 credits)
- *CHEM 320 Instrumental Analysis (4 credits)
- *CHEM 331 Chemical Thermodynamics and Reaction Dynamics (4 credits)
- *CHEM 440 Biochemistry I (4 credits)
- *Other approved CHEM courses at the 200-level or above
Applied Data Analysis—choose one of the following:
- CHEM 491 Individual Research (2 credits) and CHEM 492 Individual Research(2 credits)
- Other approved CHEM course that applies data analytics in chemistry
Note: The applied data analysis credit requirement may be waived for students conducting paid research through Young Scholars grant or similar paid research experience.
Criminal Justice [20 credits]
Introductory Courses:
- SOCI 100 Introduction to Sociology (4 credits)
- SOCI 200 Introduction to Criminal Justice (4 credits)
Upper-Level Courses:
- SOCI 210 Research Methods in Sociology (4 credits)
- SOCI 312 Crime and Delinquency (4 credits)
Applied Data Analysis—choose one of the following:
- SOCI 491 Individual Study (4 credits)
- Other approved undergraduate research opportunity (e.g., Young Scholars)
Economics [20 credits]
Introductory Courses:
- ECON 251 Principles of Macroeconomics (4 credits)
- ECON 252 Principles of Microeconomics (4 credits)
Upper-Level Courses:
- ECON 351 Macroeconomic Theory (4 credits)
or ECON 352 Microeconomic Theory (4 credits) - One ECON 300- or 400-level field course (i.e., not ECON 311, ECON 315, ECON 351, or ECON 352)
Applied Data Analysis:
- ECON 311 Forecasting (4 credits)
or ECON 315 Introduction to Econometrics (4 credits)
Note: ECON 315 may not satisfy both the domain requirement and the STAT 320/STAT 333 requirement.
Environmental Science[16 credits]
Introductory Courses − choose one course each from two of the following categories:
- Environmental Science
- ESCI 132 Introduction to Environmental Science (4 credits)
- Biology
- BIOL 101 General Biology (4 credits)
- BIOL 102 Conservation Biology (4 credits)
- BIOL 105 Human Biology (4 credits)
- BIOL 162 Medical Geology (4 credits)
- BIOL 207 Genetics, Ecology Evolution (4 credits)
- BIOL 209 Biology of Sustainability (4 credits)
- Chemistry
- CHEM 100 Chemistry in Our World (4 credits)
- CHEM 101 Environmental Chemistry (4 credits)
- CHEM 109 General Chemistry for ENGR (4 credits)
- CHEM 111 General Chemistry I (4 credits)
- CHEM 115 Accelerated General Chemistry (4 credits)
- Geology
- GEOL 111 Introductory Physical Geology (4 credits)
- GEOL 114 The Science of Natural Disasters (4 credits)
- GEOL 115 Environmental Geology (4 credits)
- GEOL 162 The Earth’s Record of Climate (4 credits)
Upper-Level Course—choose one of the following:
- ESCI 310 Environmental Problem Solving (4 credits)
- ESCI 389 Research (4 credits)
Applied Data Analysis:
- ESCI 430 Senior Research Seminar (4 credits)
- ESCI 491 Research (4 credits)
NOTE: Because suitability of ESCI 310 and ESCI 430 for Data Analytics major will vary(course projects may or may not lend themselves to data analytics) students should consult ESCI Program Director or ESCI Domain Advisor to select the most appropriate courses.
Geographic Information Systems [16 credits]
Introductory Course:
- GEOG 321 Geographic Information Systems (4 credits)
Upper-Level Courses:
- GEOG 331 Conservation Geography (4 credits)
- GEOG 350 Geography of Global Health (4 credits)
Applied Data Analysis:
- GEOG 421 Applied Geographic Information Systems (4 credits)
Geology[16 credits]
Introductory Course—choose one of the following:
- GEOL 111 Introductory Physical Geology (4 credits)
- GEOL 115 Environmental Geology (4 credits)
- GEOL 162 The Earth’s Record of Climate (4 credits)
Upper-Level Course—choose one of the following:
- GEOL 252 Earth Surface Processes and Geomorphology (4 credits)
- GEOL 260 Regional Geology and Geological Field Methods (4 credits)
Applied Data Analysis:—choose two of the following, at least one at the 400-level:
- GEOL 252 Earth Surface Processes and Geomorphology (4 credits) [If not taken to satisfy the upper-level course requirement above]
- GEOL 220 Oceanography (4 credits)
- GEOL 410 Hydrogeology (4 credits)
- GEOL 462 Advanced Earth’s Record of Climate (4 credits) [If GEOL 162 not taken for introductory course]
Information Systems [16 credits]
Introductory Courses:
- CISC 230 Object‐Oriented Design and Programming (4 credits)
- SEIS 610 Software Engineering (3 credits)
Upper-Level Courses:
- SEIS 615 DevOps and Cloud Infrastructure (3 credits)
- SEIS 630 Database Management Systems and Design (3 credits)
Applied Data Analysis:
- SEIS 632 Data Analytics and Visualization (3 credits)
Note: Students in the Information Systems domain are strongly encouraged to take the R lab option with STAT 220.
Note: STAT 220 with the R lab will count as SEIS 631: Foundations of Data Analysis for domain courses that have SEIS 631 listed as a pre-requisite.
Note: CISC 131 will count as SEIS 603: Foundations of Software Development for domain courses that have SEIS 603 listed as a pre-requisite.
FastTrack to Master of Science in Data Science: Students who complete the Bachelor of Science in Data Analytics with an Information Systems domain have the option to pursue a graduate degree in Data Science through Graduate Programs in Software at the University of St. Thomas. Eight additional graduate courses, beyond those required for the Bachelor of Science in Data Analytics with an Information Systems domain, are required for a Master of Science in Data Science degree. Please see MS in Data Science for additional details.
Physics [20 credits]
Introductory Courses:
- PHYS 211 Classical Physics I (4 credits)
- PHYS 212 Classical Physics II (4 credits)
- MATH 114 Calculus II (4 credits)
Upper-Level Courses—choose one of the following:
- PHYS 215 Foundations of Modern Physics (4 credits)
- PHYS 225 Applications of Modern Physics (4 credits)
Applied Data Analysis—choose one of the following:
- PHYS 323 Methods of Experimental Physics (4 credits)
- PHYS 325 Methods of Computational Physics (4 credits)
Public Health [16 credits]
Introductory Courses—choose two of the following courses:
- PUBH 200 Emerging Infectious Disease and Global Health (4 credits)
- PUBH 210 One Health: Humans, Animals and the Environment (4 credits)
- PUBH 220 Introduction to Public Health and Social Justice (4 credits)
- PUBH 225 Global Health and Development (4 credits)
Upper-Level Course:
- One PUBH 300-level course (4 credits) to be determined in consultation with Health Sciences Chair or Public Health Domain Advisor.
Likely course options include:- PUBH 300 Introduction to Epidemiology (4 credits)
Applied Data Analysis:
- One 400-level PUBH course (4 credits) to be determined in a consultation with Health Sciences Chair or Public Health Domain Advisor.
Likely courseoptions include:- PUBH 465 Public Health Research Methods (4 credits)
- PUBH 470 Experiential Learning in Public Health (4 credits)
- PUBH 491 Research (4 credits)
Sociology [16-20 credits]
Introductory Course:
- SOCI 100 Introduction to Sociology (4 credits)
Upper-Level Courses:
- SOCI 210 Research Methods in Sociology (4 credits)
- * SOCI 474 Sociological Theory and Praxis: The Capstone Experience (4 credits)
- One SOCI 200 or 300-level course (4 credits)
Applied Data Analysis-Choose one of the following:
- SOCI 491 Individual Study (4 credits)
- Other approved undergraduate research opportunity (e.g., Young Scholars)
Strategic Communication [20 credits]
Introductory courses:
- STCM 234 Principles of Strategic Communication (4 credits)
- STCM 244 Research, Measurement, and Evaluation (4 credits)
Upper-level courses:
- STCM 344 Writing for Strategic Communication (4 credits)
- STCM 346 Digital Content and Strategy in Strategic Communication (4 credits)
Applied data analysis:
- STCM 480 Capstone: Strategic Communication Campaigns (4 credits)
Data Analytics Undergraduate Courses
| Course Number | Title | Credits | |
|---|---|---|---|
| DATA 200 | Data Analytics Seminar | 1 | |
| Description of course Data Analytics Seminar : | This seminar is designed to facilitate students’ exploration of their domain fields as they relate to Data Analytics. It brings together students from all domain areas to improve their data communication skills and broaden their understanding of data analytics. The seminar primarily focuses on the communication and dissemination of data analytic work, which may vary by domain. Prerequisites: Junior standing and one of the following: STAT 220, STAT 314, STAT 201, or MATH 303. | ||
| DATA 400 | Data Analytics Capstone | 2 | |
| Description of course Data Analytics Capstone : | This seminar is designed to fulfill the senior capstone experience in Data Analytics. It brings together students from all domain areas to fine-tune their data communication skills, broaden their understanding of data analytics, and produce a portfolio of work. The seminar primarily focuses on the communication and dissemination of data analytic work, which may vary by domain. This course should be completed in the final Spring semester prior to graduation. Prerequisites: Senior standing, DATA 200, COMM 100, and one of the following: STAT 320, STAT 333, or ECON 315. | ||
| DATA 478 | Experiential Learning 0 Credit | 0 | |
| Description of course Experiential Learning 0 Credit : | No description is available. | ||