Data Science, BS
General Requirements
To earn a degree, students must satisfy all requirements in each of the three areas below, in addition to their individual major requirements.
- CU Denver Graduation Requirements
- CU Denver Undergraduate Core Curriculum
-
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Program Requirements
- Students must complete a total of 62-71 major credit hours, from approved courses.
- Students must complete at least 30 upper-division (3000-level and above) credit hours in the major.
- Students must earn a minimum grade of C- (1.7) in all courses that apply to the major and must achieve a minimum cumulative major GPA of 2.25. Courses taken using P+/P/F or S/U grading cannot apply to major requirements.
- Students must complete a minimum of 15 upper-division level credit hours with CU Denver faculty.
- Students must pick from one of the following concentration options: General, Business, Chemistry, Computer Science, Economics, Geography, or Mathematics.
General Data Science Option
| Code | Title | Hours |
|---|---|---|
| Take the required courses listed below: | 39 | |
| Introduction to Business | ||
| Fundamentals of Computing | ||
| Business Problem Solving Tools | ||
| Data Governance and Ethics | ||
| Business Data and Database Management | ||
| Calculus I | ||
| Calculus II | ||
| Calculus III | ||
| Data Wrangling & Visualization | ||
| Statistical Theory | ||
| Introduction to Probability | ||
| Machine Learning Methods | ||
| Applied Regression Analysis | ||
| Choose one: | 3 | |
| Foundations of Data Science | ||
| Introductory Statistics | ||
| Choose one: | 3-4 | |
| Applied Linear Algebra | ||
| Linear Algebra and Differential Equations | ||
| Choose one path: | 2-3 | |
| Career and Professional Development | ||
OR | ||
| Cultivating Emotional Intelligence and Career and Professional Development | ||
| Choose one (any course other than MATH 4779 requires the approval of the Director of Data Science and must be taken for 3 credit hours): | 3 | |
| Math Clinic | ||
| Internship | ||
| Independent Study | ||
| Internship | ||
| Independent Study | ||
| Choose one: | 3 | |
| Business Analytics Process | ||
| Forecasting Techniques | ||
| Statistics for Business Analytics | ||
| Computing for Business Analytics | ||
| Prescriptive Analytics with Optimization | ||
| Causal Analytics | ||
| Evaluative Analytics | ||
| Choose nine elective credits not previously taken: | 9 | |
| Business Analytics Process | ||
| Forecasting Techniques | ||
| Transformative Technologies Impacting Globalization | ||
| Statistics for Business Analytics | ||
| Computing for Business Analytics | ||
| Time-Series Forecasting | ||
| Decision Analysis | ||
| Project Management | ||
| Predictive Analytics and Machine Learning | ||
| Prescriptive Analytics with Optimization | ||
| Causal Analytics | ||
| Supply Chain Analytics | ||
| Data Visualization | ||
| Evaluative Analytics | ||
| AI for Business | ||
| Special Topics | ||
| Physical Chemistry: Quantum and Spectroscopy | ||
| Molecular Informatics | ||
| Artificial Intelligence in Chemistry and Biochemistry | ||
| Molecular Modeling and Drug Design | ||
| Data Mining | ||
| Big Data Mining | ||
| Bioinformatics | ||
| Bioinformatics | ||
| Special Topics (must be relevant to Data Science) | ||
| Machine Learning | ||
| Deep Learning | ||
| Big Data Systems | ||
| Data Analysis with SAS | ||
| Introduction to Econometrics | ||
| Advanced Econometric Methods | ||
| Remote Sensing I: Introduction to Environmental Remote Sensing | ||
| Remote Sensing II: Advanced Remote Sensing | ||
| Introduction to GIS | ||
| Cartography | ||
| GIS Applications for the Urban Environment | ||
| Environmental Modeling with Geographic Information Systems | ||
| Open Source Software for Geospatial Applications | ||
| GIS Programming and Automation | ||
| Deploying GIS Functionality on the Web | ||
| GIS Applications in the Health Sciences | ||
| Technology In Business | ||
| System Strategy, Architecture and Design | ||
| Information Systems Security and Privacy | ||
| Business Intelligence for Financial Modeling | ||
| Project Management and Practice | ||
| Database Management Systems | ||
| Text Data Analytics | ||
| Elementary Differential Equations | ||
| Introduction to Optimization | ||
| Game Theory | ||
| Applied Graph Theory | ||
| Numerical Analysis I | ||
| Numerical Analysis II | ||
| Partial Differential Equations | ||
| Probabilistic Modeling | ||
| Marketing Research | ||
| Total Hours | 62-64 | |
Business Option
| Code | Title | Hours |
|---|---|---|
| Take the required course list below: | 39 | |
| Introduction to Business | ||
| Fundamentals of Computing | ||
| Business Problem Solving Tools | ||
| Business Data and Database Management | ||
| Calculus I | ||
| Calculus II | ||
| Calculus III | ||
| Data Wrangling & Visualization | ||
| Statistical Theory | ||
| Introduction to Probability | ||
| Applied Regression Analysis | ||
| Machine Learning Methods | ||
| Choose one: | 3 | |
| Foundations of Data Science | ||
| Introductory Statistics | ||
| Choose one: | 3-4 | |
| Applied Linear Algebra | ||
| Linear Algebra and Differential Equations | ||
| Choose one path: | 2-3 | |
| Career and Professional Development | ||
OR | ||
| Cultivating Emotional Intelligence and Career and Professional Development | ||
| Choose one (any course other than MATH 4779 requires the approval of the Director of Data Science and must be taken for 3 credit hours): | 3 | |
| Math Clinic | ||
| Internship | ||
| Independent Study | ||
| Internship | ||
| Independent Study | ||
| Choose one: | 3 | |
| Business Analytics Process | ||
| Forecasting Techniques | ||
| Statistics for Business Analytics | ||
| Computing for Business Analytics | ||
| Prescriptive Analytics with Optimization | ||
| Causal Analytics | ||
| Evaluative Analytics | ||
| Choose nine elective credits not previously taken: | 9 | |
| Business Analytics Process | ||
| Forecasting Techniques | ||
| Transformative Technologies Impacting Globalization | ||
| Statistics for Business Analytics | ||
| Computing for Business Analytics | ||
| Time-Series Forecasting | ||
| Decision Analysis | ||
| Project Management | ||
| Predictive Analytics and Machine Learning | ||
| Prescriptive Analytics with Optimization | ||
| Causal Analytics | ||
| Supply Chain Analytics | ||
| Data Visualization | ||
| Evaluative Analytics | ||
| Technology In Business | ||
| System Strategy, Architecture and Design | ||
| Business Intelligence for Financial Modeling | ||
| Project Management and Practice | ||
| Database Management Systems | ||
| Text Data Analytics | ||
| Marketing Research | ||
| Total Hours | 62-64 | |
Chemistry Option
| Code | Title | Hours |
|---|---|---|
| Take the required course list below: | 39 | |
| Introduction to Business | ||
| Fundamentals of Computing | ||
| Business Problem Solving Tools | ||
| Data Governance and Ethics | ||
| Business Data and Database Management | ||
| Calculus I | ||
| Calculus II | ||
| Calculus III | ||
| Data Wrangling & Visualization | ||
| Introduction to Probability | ||
| Applied Regression Analysis | ||
| Machine Learning Methods | ||
| Choose one: | 3 | |
| Foundations of Data Science | ||
| Introductory Statistics | ||
| Choose one: | 3-4 | |
| Applied Linear Algebra | ||
| Linear Algebra and Differential Equations | ||
| Choose one path: | 2-3 | |
| Career and Professional Development | ||
OR | ||
| Cultivating Emotional Intelligence and Career and Professional Development | ||
| Choose one (any course other than MATH 4779 requires the approval of the Director of Data Science and must be taken for 3 credit hours): | 3 | |
| Math Clinic | ||
| Internship | ||
| Independent Study | ||
| Internship | ||
| Independent Study | ||
| Choose one: | 3 | |
| Business Analytics Process | ||
| Forecasting Techniques | ||
| Statistics for Business Analytics | ||
| Computing for Business Analytics | ||
| Prescriptive Analytics with Optimization | ||
| Causal Analytics | ||
| Choose nine elective credits not previously taken: | 9 | |
| Physical Chemistry: Quantum and Spectroscopy | ||
| Molecular Informatics | ||
| Artificial Intelligence in Chemistry and Biochemistry | ||
| Molecular Modeling and Drug Design | ||
| Total Hours | 62-64 | |
Computer Science Option
| Code | Title | Hours |
|---|---|---|
| Take the required course list below: | 57 | |
| Fundamentals of Computing | ||
| Object Oriented Programming | ||
| Data Structures and Program Design | ||
| Discrete Structures | ||
| Algorithms | ||
| Database System Concepts | ||
| Data Science | ||
| Data Governance and Ethics | ||
| Calculus I | ||
| Calculus II | ||
| Calculus III | ||
| Data Wrangling & Visualization | ||
| Statistical Theory | ||
| Introduction to Probability | ||
| Applied Regression Analysis | ||
| Choose one: | 3 | |
| Foundations of Data Science | ||
| Introductory Statistics | ||
| Choose one: | 3-4 | |
| Applied Linear Algebra | ||
| Linear Algebra and Differential Equations | ||
| Choose one path: | 2-3 | |
| Career and Professional Development | ||
OR | ||
| Cultivating Emotional Intelligence and Career and Professional Development | ||
| Choose one (any course other than MATH 4779 requires the approval of the Director of Data Science and must be taken for 3 credit hours): | 3 | |
| Independent Study | ||
| Internship | ||
| Internship | ||
| Math Clinic | ||
| Independent Study | ||
| Internship | ||
| Independent Study | ||
| Choose six elective credits not previously taken: | 6 | |
| Data Mining | ||
| Big Data Mining | ||
| Bioinformatics | ||
| Special Topics | ||
| Machine Learning | ||
| Deep Learning | ||
| Big Data Systems | ||
| Total Hours | 74-76 | |
Economics Option
| Code | Title | Hours |
|---|---|---|
| Take the required course list below: | 39 | |
| Introduction to Business | ||
| Fundamentals of Computing | ||
| Business Problem Solving Tools | ||
| Data Governance and Ethics | ||
| Business Data and Database Management | ||
| Calculus I | ||
| Calculus II | ||
| Calculus III | ||
| Data Wrangling & Visualization | ||
| Statistical Theory | ||
| Introduction to Probability | ||
| Applied Regression Analysis | ||
| Machine Learning Methods | ||
| Choose one: | 3 | |
| Foundations of Data Science | ||
| Introductory Statistics | ||
| Choose one: | 3-4 | |
| Applied Linear Algebra | ||
| Linear Algebra and Differential Equations | ||
| Choose one path: | 2-3 | |
| Career and Professional Development | ||
OR | ||
| Cultivating Emotional Intelligence and Career and Professional Development | ||
| Choose one (any course other than MATH 4779 requires the approval of the Director of Data Science and must be taken for 3 credit hours): | 3 | |
| Math Clinic | ||
| Internship | ||
| Independent Study | ||
| Internship | ||
| Independent Study | ||
| Choose one: | 3 | |
| Business Analytics Process | ||
| Forecasting Techniques | ||
| Statistics for Business Analytics | ||
| Computing for Business Analytics | ||
| Prescriptive Analytics with Optimization | ||
| Causal Analytics | ||
| Evaluative Analytics | ||
| Take the following elective courses: | 9 | |
| Data Analysis with SAS | ||
| Introduction to Econometrics | ||
| Advanced Econometric Methods | ||
Geography Option
| Code | Title | Hours |
|---|---|---|
| Take the required course list below: | 39 | |
| Introduction to Business | ||
| Business Problem Solving Tools | ||
| Data Governance and Ethics | ||
| Business Data and Database Management | ||
| Calculus I | ||
| Calculus II | ||
| Calculus III | ||
| Data Wrangling & Visualization | ||
| Statistical Theory | ||
| Introduction to Probability | ||
| Applied Regression Analysis | ||
| Machine Learning Methods | ||
| Choose one: | 3 | |
| Foundations of Data Science | ||
| Introductory Statistics | ||
| Choose one: | 3-4 | |
| Applied Linear Algebra | ||
| Linear Algebra and Differential Equations | ||
| Choose one path: | 2-3 | |
| Career and Professional Development | ||
OR | ||
| Cultivating Emotional Intelligence and Career and Professional Development | ||
| Choose one (any course other than MATH 4779 requires the approval of the Director of Data Science and must be taken for 3 credit hours): | 3 | |
| Math Clinic | ||
| Internship | ||
| Independent Study | ||
| Internship | ||
| Independent Study | ||
| Choose one: | 3 | |
| Business Analytics Process | ||
| Forecasting Techniques | ||
| Statistics for Business Analytics | ||
| Computing for Business Analytics | ||
| Prescriptive Analytics with Optimization | ||
| Causal Analytics | ||
| Evaluative Analytics | ||
| Choose nine elective credits not previously taken: | 9 | |
| Remote Sensing I: Introduction to Environmental Remote Sensing | ||
| Remote Sensing II: Advanced Remote Sensing | ||
| Introduction to GIS | ||
| Cartography | ||
| GIS Applications for the Urban Environment | ||
| Environmental Modeling with Geographic Information Systems | ||
| Open Source Software for Geospatial Applications | ||
| GIS Programming and Automation | ||
| Deploying GIS Functionality on the Web | ||
| GIS Applications in the Health Sciences | ||
| Total Hours | 62-64 | |
Mathematics Option
| Code | Title | Hours |
|---|---|---|
| Take the required course list below: | 39 | |
| Introduction to Business | ||
| Fundamentals of Computing | ||
| Business Problem Solving Tools | ||
| Data Governance and Ethics | ||
| Intermediate Excel for Business | ||
| Calculus I | ||
| Calculus II | ||
| Calculus III | ||
| Data Wrangling & Visualization | ||
| Introduction to Probability | ||
| Applied Regression Analysis | ||
| Machine Learning Methods | ||
| Choose one: | 3 | |
| Foundations of Data Science | ||
| Introductory Statistics | ||
| Choose one: | 3-4 | |
| Applied Linear Algebra | ||
| Linear Algebra and Differential Equations | ||
| Choose one path: | 2-3 | |
| Career and Professional Development | ||
OR | ||
| Cultivating Emotional Intelligence and Career and Professional Development | ||
| Choose one (any course other than MATH 4779 requires the approval of the Director of Data Science and must be taken for 3 credit hours): | 3 | |
| Math Clinic | ||
| Internship | ||
| Independent Study | ||
| Internship | ||
| Independent Study | ||
| Choose one: | 3 | |
| Business Analytics Process | ||
| Forecasting Techniques | ||
| Statistics for Business Analytics | ||
| Computing for Business Analytics | ||
| Prescriptive Analytics with Optimization | ||
| Causal Analytics | ||
| Evaluative Analytics | ||
| Choose nine elective credits not previously taken: | 9 | |
| Elementary Differential Equations | ||
| Introduction to Optimization | ||
| Game Theory | ||
| Applied Graph Theory | ||
| Numerical Analysis I | ||
| Numerical Analysis II | ||
| Partial Differential Equations | ||
| Probabilistic Modeling | ||
| Total Hours | 62-64 | |
The program’s student learning goals that define what the students should know and be able to do by the time of graduation are to:
- Math & Programming Skills: Apply the math and programming skills necessary for the work of data science.
- Data Cycle: Explore technical and practical data science by applying the data cycle to transform data into knowledge.
- Data Preparation: Assess and improve the quality of data relative to analytical needs.
- Data Management: Address data challenges of volume, variety, and velocity to enable efficient and effective data analysis.
- Data Analysis: Apply techniques, methodologies, and technologies for various forms of data analysis such as data modeling and data mining.
- Data Visualization: Create visualizations of complex data and results for delivery to diverse audiences.
- Data Storytelling: Explain data and results in writing and verbally, equipping stakeholders to make data-informed decisions.
- Data Ethics: Assess ethical implications in data science, such as privacy and bias.
- Application Domains: Apply data science in a variety of domains, such as healthcare, social sciences, natural sciences, physical science, business, education, and public administration.
- Interprofessional Collaboration & Teamwork: Exhibit the qualities of an effective interprofessional collaborator as part of a data science team and within organizations with diverse roles.
Graduates will be able to demonstrate these capabilities in a broad range of data science activities. The degree will prepare students for careers as data analysts, data scientists, data strategist and many other diverse careers that rely on data, which is essentially every corner of the job market today.
The following plans of study are examples of pathways that students can follow, depending on their entry level MATH placement.
To review a list of courses will fulfill CU Denver Core Arts, Behavioral Science, Humanities and Natural and Physical Sciences with and without a lab, please check the CU Denver Core Curriculum.
Calculus I
| Year 1 | ||
|---|---|---|
| Fall | Hours | |
| BMIN 1000 | Introduction to Business | 3 |
| ENGL 1020 | Core Composition I | 3 |
| MATH 1376 | Programming for Data Science | 3 |
| MATH 1401 | Calculus I | 4 |
| Core Arts, Humanities, Social Science, Behavioral Science, International Perspectives or Cultural Diversity | 3 | |
| Hours | 16 | |
| Spring | ||
| ENGL 2030 | Core Composition II | 3 |
| CSCI 2800 | Special Topics (Data Science Thinking) | 3 |
| MATH 2830 | Introductory Statistics | 3 |
| MATH 2411 | Calculus II | 4 |
| Core Arts, Humanities, Social Science, Behavioral Science, International Perspectives or Cultural Diversity | 3 | |
| Hours | 16 | |
| Year 2 | ||
| Fall | ||
| BMIN 2200 Career and Professional Development | 3 | |
| CSCI 2400 Data Structures and Program Design for Data Science | 3 | |
| ISMG 3100 Data Governance and Ethics | 3 | |
| MATH 2421 | Calculus III | 4 |
| Core Arts, Humanities, Social Science, Behavioral Science, International Perspectives or Cultural Diversity | 3 | |
| Hours | 16 | |
| Spring | ||
| Core Arts, Humanities, Social Science, Behavioral Science, International Perspectives or Cultural Diversity | 3 | |
| CU Denver Core Natural and Physical Sciences with a lab | 4-5 | |
| CSCI 3400 Databases for Data Science | 3 | |
| MATH 2700 Data Analysis with R | 3 | |
| MATH 3376 | Data Wrangling & Visualization | 3 |
| Hours | 16-17 | |
| Year 3 | ||
| Fall | ||
| Core Arts, Humanities, Social Science, Behavioral Science, International Perspectives or Cultural Diversity | 3 | |
| MATH 3810 | Introduction to Probability | 3 |
| CSCI 3450 Algorithms for Data Science | 3 | |
| MATH 3191 | Applied Linear Algebra | 3 |
| Open elective-student choice | 3 | |
| Hours | 15 | |
| Spring | ||
| Application Domain Elective | 3 | |
| Core Arts, Humanities, Social Science, Behavioral Science, International Perspectives or Cultural Diversity | 3 | |
| BANA 4110 Business Analytics Processes | 3 | |
| CSCI 4580 | Data Science | 3 |
| MATH 3382 | Statistical Theory | 3 |
| Hours | 15 | |
| Year 4 | ||
| Fall | ||
| Application Domain Elective | 3 | |
| BANA 4120 Forecasting Techniques | 3 | |
| CSCI 4455 | Data Mining | 3 |
| CSCI 4931 | Deep Learning | 3 |
| Open elective-student choice | 3 | |
| Hours | 15 | |
| Spring | ||
| Application Domain Elective | 3 | |
| CSCI 4930 | Machine Learning | 3 |
| CSCI 4951 | Big Data Systems | 3 |
| MATH 4387 | Applied Regression Analysis | 3 |
| Hours | 12 | |
| Total Hours | 121-122 | |
Precalculus
| Year 1 | ||
|---|---|---|
| Fall | Hours | |
| BMIN 1000 | Introduction to Business | 3 |
| ENGL 1020 | Core Composition I | 3 |
| MATH 1376 | Programming for Data Science | 3 |
| MATH 1130 | Precalculus Mathematics | 4 |
| Core Arts, Humanities, Social Science, Behavioral Science, International Perspectives or Cultural Diversity | 3 | |
| Hours | 16 | |
| Spring | ||
| ENGL 2030 | Core Composition II | 3 |
| CSCI 2800 | Special Topics (Data Science Thinking) | 3 |
| MATH 2830 | Introductory Statistics | 3 |
| MATH 1401 | Calculus I | 4 |
| Core Arts, Humanities, Social Science, Behavioral Science, International Perspectives or Cultural Diversity | 3 | |
| Hours | 16 | |
| Year 2 | ||
| Fall | ||
| BMIN 2200 Career and Professional Development | 3 | |
| CSCI 2400 Data Structures and Program Design for Data Science | 3 | |
| ISMG 3100 Data Governance and Ethics | 3 | |
| MATH 2411 | Calculus II | 4 |
| Core Arts, Humanities, Social Science, Behavioral Science, International Perspectives or Cultural Diversity | 3 | |
| Hours | 16 | |
| Spring | ||
| Core Arts, Humanities, Social Science, Behavioral Science, International Perspectives or Cultural Diversity | 3 | |
| CSCI 3400 Databases for Data Science | 3 | |
| MATH 2700 Data Analysis with R | 3 | |
| MATH 2421 | Calculus III | 4 |
| MATH 3376 | Data Wrangling & Visualization | 3 |
| Hours | 16 | |
| Year 3 | ||
| Fall | ||
| Core Arts, Humanities, Social Science, Behavioral Science, International Perspectives or Cultural Diversity | 3 | |
| MATH 3810 | Introduction to Probability | 3 |
| CSCI 3450 Algorithms for Data Science | 3 | |
| MATH 3191 | Applied Linear Algebra | 3 |
| CU Denver Core Natural and Physical Sciences with a lab | 4-5 | |
| Hours | 16-17 | |
| Spring | ||
| Application Domain Elective | 3 | |
| Core Arts, Humanities, Social Science, Behavioral Science, International Perspectives or Cultural Diversity | 3 | |
| BANA 4110 Business Analytics Processes | 3 | |
| CSCI 4580 | Data Science | 3 |
| MATH 3382 | Statistical Theory | 3 |
| Hours | 15 | |
| Year 4 | ||
| Fall | ||
| Application Domain Elective | 3 | |
| BANA 4120 Forecasting Techniques | 3 | |
| CSCI 4455 | Data Mining | 3 |
| CSCI 4931 | Deep Learning | 3 |
| Open elective-student choice | 3 | |
| Hours | 15 | |
| Spring | ||
| Application Domain Elective | 3 | |
| CSCI 4930 | Machine Learning | 3 |
| CSCI 4951 | Big Data Systems | 3 |
| MATH 4387 | Applied Regression Analysis | 3 |
| Hours | 12 | |
| Total Hours | 122-123 | |
Algebra
| Year 1 | ||
|---|---|---|
| Fall | Hours | |
| BMIN 1000 | Introduction to Business | 3 |
| ENGL 1020 | Core Composition I | 3 |
| Core Arts, Humanities, Social Science, Behavioral Science, International Perspectives or Cultural Diversity | 3 | |
| MATH 1110 | College Algebra | 4 |
| MATH 2830 | Introductory Statistics | 3 |
| Hours | 16 | |
| Spring | ||
| ENGL 2030 | Core Composition II | 3 |
| CSCI 2800 | Special Topics (Data Science Thinking) | 3 |
| Core Arts, Humanities, Social Science, Behavioral Science, International Perspectives or Cultural Diversity | 3 | |
| MATH 1120 | College Trigonometry | 3 |
| MATH 1376 | Programming for Data Science | 3 |
| Hours | 15 | |
| Summer | ||
| MATH 1401 | Calculus I | 4 |
| Hours | 4 | |
| Year 2 | ||
| Fall | ||
| BMIN 2200 Career and Professional Development | 3 | |
| CSCI 2400 Data Structures and Program Design for Data Science | 3 | |
| ISMG 3100 Data Governance and Ethics | 3 | |
| MATH 2411 | Calculus II | 4 |
| Core Arts, Humanities, Social Science, Behavioral Science, International Perspectives or Cultural Diversity | 3 | |
| Hours | 16 | |
| Spring | ||
| Core Arts, Humanities, Social Science, Behavioral Science, International Perspectives or Cultural Diversity | 3 | |
| CSCI 3400 Databases for Data Science | 3 | |
| MATH 2700 Data Analysis with R | 3 | |
| MATH 2421 | Calculus III | 4 |
| MATH 3376 | Data Wrangling & Visualization | 3 |
| Hours | 16 | |
| Year 3 | ||
| Fall | ||
| Core Arts, Humanities, Social Science, Behavioral Science, International Perspectives or Cultural Diversity | 3 | |
| MATH 3810 | Introduction to Probability | 3 |
| CSCI 3450 Algorithms for Data Science | 3 | |
| MATH 3191 | Applied Linear Algebra | 3 |
| CU Denver Core Natural and Physical Sciences with a lab | 4-5 | |
| Hours | 16-17 | |
| Spring | ||
| Application Domain Elective | 3 | |
| Core Arts, Humanities, Social Science, Behavioral Science, International Perspectives or Cultural Diversity | 3 | |
| BANA 4110 Business Analytics Processes | 3 | |
| CSCI 4580 | Data Science | 3 |
| MATH 3382 | Statistical Theory | 3 |
| Hours | 15 | |
| Year 4 | ||
| Fall | ||
| Application Domain Elective | 3 | |
| BANA 4120 Forecasting Techniques | 3 | |
| CSCI 4455 | Data Mining | 3 |
| CSCI 4931 | Deep Learning | 3 |
| Hours | 12 | |
| Spring | ||
| Application Domain Elective | 3 | |
| CSCI 4930 | Machine Learning | 3 |
| CSCI 4951 | Big Data Systems | 3 |
| MATH 4387 | Applied Regression Analysis | 3 |
| Hours | 12 | |
| Total Hours | 122-123 | |