Manuel Rossetti

Director

J.B. Hunt Center 111

479-575-6756

Email: rossetti@uark.edu

Karl D. Schubert

Associate Director

J.B. Hunt Center 110

479-575-2264

Email: karl.schubert@uark.edu

Data scientists make sense of huge sets of data to help businesses, governments, nonprofits and other organizations make smarter decisions. The university's interdisciplinary Bachelor of Science in Data Science will prepare students for a successful career in data science with a strategic skill set, including the ability to:

- Use and apply state-of-the-art technologies for data representation, retrieval, manipulation, storage, governance, understanding, analysis, privacy, and security.
- Develop descriptive, predictive and prescriptive models to abstract complex systems and organizational problems, and to use computational methods to draw data-supported conclusions.
- Use foundational knowledge and apply critical thinking skills to identify and solve problems, make decisions, and visualize data, all with an awareness of societal and ethical impacts.
- Adapt analytics concepts to interpret and communicate findings and implications to senior decision-makers.
- Work effectively in an interdisciplinary team and transfer findings between knowledge domains and to others with no domain experience.
- Communicate using technical and non-technical language in writing and verbally.

Three colleges at the university — the College of Engineering, the Fulbright College of Arts and Sciences, and the Sam M. Walton College of Business — contribute expertise to the overall major while providing deeper insight into the concentrations they offer, including:

- Accounting Analytics
- Bioinformatics
- Biomedical and Healthcare Informatics
- Business Data Analytics
- Computational Analytics
- Data Science Statistics
- Geospatial Data Analytics
- Operations Analytics
- Social Data Analytics
- Supply Chain Analytics

### Requirements for B.S. in Data Science with Accounting Analytics Concentration

Below are the general requirements for a Bachelor of Science degree with a major in Data Science, followed by specific requirements for the Accounting Analytics Concentration. Below those is a recommended eight-semester plan to achieve those requirements in a timely fashion.

**Requirements for B.S. in Data Science**

Each student in Data Science is required to complete 120 hours of coursework including the state minimum core. To be eligible for graduation, all students must complete at least 60 hours of Data Science (DTSC) Core required classes at the University of Arkansas. Each student in Data Science is also required to complete an additional 20-21 hours (depending on the student's chosen concentration) of required and elective concentration courses to meet the requirements for a concentration.

Additional opportunities are available to enhance the educational experience of students in these areas. Students should consult their academic adviser for recommendations.

State Minimum Core and General Education (36 hours) | ||

ENGL 1013 | Composition I (ACTS Equivalency = ENGL 1013) | 3 |

ENGL 1033 | Technical Composition II (ACTS Equivalency = ENGL 1023) | 3 |

MATH 2554 | Calculus I (ACTS Equivalency = MATH 2405) | 4 |

Science state minimum electives (two courses with labs) | 8 | |

Fine Arts state minimum core | 3 | |

Humanities state minimum core | ||

PHIL 3103 | Ethics and the Professions | 3 |

U.S. History and Government state minimum core | 3 | |

History of the American People to 1877 (ACTS Equivalency = HIST 2113) | ||

or HIST 2013 | History of the American People, 1877 to Present (ACTS Equivalency = HIST 2123) | |

or PLSC 2003 | American National Government (ACTS Equivalency = PLSC 2003) | |

Social Science state minimum core electives | 6 | |

ECON 2143 | Basic Economics: Theory and Practice (represents 3 of the 9 required credit hours for Social Science elective) | 3 |

Data Science Required Core (47 hours) | ||

DASC 1001 | Introduction to Data Science (First-Year Program - Introduction to Data Science) | 1 |

DASC 1104 | Programming Languages for Data Science (Programming Languages for Data Science (R, Python)) | 4 |

DASC 1204 | Introduction to Object Oriented Programming for Data Science (Introduction to Object Oriented Programming for Data Science (JAVA)) | 4 |

DASC 2594 | Multivariable Math for Data Scientists (Multivariable Math for Data Scientists) | 4 |

DASC 1222 | Role of Data Science in Today's World (Role of Data Science in Today's World) | 2 |

DASC 2103 | Data Structures & Algorithms (Data Structures & Algorithms) | 3 |

DASC 2113 | Principles and Techniques of Data Science (Principles & Techniques of Data Science) | 3 |

DASC 2203 | Data Management and Data Base (Data Management & Data Base) | 3 |

DASC 2213 | Data Visualization and Communication (Data Visualization & Communication (Tableau)) | 3 |

DASC 3103 | Cloud Computing and Big Data (Cloud Computing & Big Data) | 3 |

DASC 3203 | Optimization Methods in Data Science (Optimization Methods in Data Science) | 3 |

DASC 3213 | Statistical Learning (Statistical Learning) | 3 |

DASC 4892 | Data Science Practicum I (Data Science Practicum I) | 2 |

DASC 4113 | Machine Learning (Machine Learning) | 3 |

DASC 4123 | Social Problems in Data Science and Analytics (Social Problems (Issues) in DASC & Analytics) | 3 |

DASC 4993 | Data Science Practicum II (Data Science Practicum II) | 3 |

Data Science Required Additional Courses | ||

MATH 2564 | Calculus II (ACTS Equivalency = MATH 2505) | 4 |

SEVI 2053 | Business Foundations | 3 |

Choose from one of these two-course sequences | 6 | |

Applied Probability and Statistics for Engineers I and Applied Probability and Statistics for Engineers II (Applied Probability and Statistics for Engineers II) | ||

Or | ||

Introduction to Probability and Statistical Methods (Statistical Methods) | ||

Data Science Concentration Courses | 20-21 | |

General Electives | 3-4 | |

Total Hours | 120 |

### Required Accounting Analytics Concentration Courses

ACCT 2013 | Accounting Principles | 3 |

ACCT 2023 | Accounting Principles II | 3 |

ACCT 3533 | Accounting Technology | 3 |

ACCT 3543 | Accounting Analytics | 3 |

ISYS 4193 | Business Analytics and Visualization | 3 |

ISYS 4293 | Business Intelligence | 3 |

Elective Accounting Analytics Concentration Courses (Select 3 hours) | 3 | |

Financial Analysis | ||

Microeconomic Theory | ||

Introduction to Econometrics | ||

Forecasting | ||

Introduction to Marketing | ||

Marketing Research | ||

Total Hours | 21 |

### Data Science B.S. with Accounting Analytics Concentration

Eight-Semester Program

First Year | Units | |
---|---|---|

Fall | Spring | |

MATH 2554 Calculus I (ACTS Equivalency = MATH 2405) (Satisfies General Education Outcome 2.1)^{1} | 4 | |

ENGL 1013 Composition I (ACTS Equivalency = ENGL 1013) (Satisfies General Education Outcome 1.1) | 3 | |

State Minimum Core Natural Science Elective with Lab (Satisfies General Education Outcome 3.4) | 4 | |

DASC 1001 Introduction to Data Science | 1 | |

DASC 1104 Programming Languages for Data Science | 4 | |

MATH 2564 Calculus II (ACTS Equivalency = MATH 2505) | 4 | |

DASC 1204 Introduction to Object Oriented Programming for Data Science | 4 | |

DASC 1222 Role of Data Science in Today's World | 2 | |

ACCT 2013 Accounting Principles | 3 | |

ENGL 1033 Technical Composition II (ACTS Equivalency = ENGL 1023) (Satisfies General Education Outcome 1.2) | 3 | |

Year Total: | 16 | 16 |

Second Year | Units | |

Fall | Spring | |

DASC 2594 Multivariable Math for Data Scientists | 4 | |

DASC 2113 Principles and Techniques of Data Science | 3 | |

ACCT 2023 Accounting Principles II | 3 | |

INEG 2313 Applied Probability and Statistics for Engineers I^{4}or STAT 3013 Introduction to Probability | 3 | |

DASC 2213 Data Visualization and Communication | 3 | |

DASC 2203 Data Management and Data Base | 3 | |

SEVI 2053 Business Foundations | 3 | |

ACCT 3533 Accounting Technology | 3 | |

INEG 2333 Applied Probability and Statistics for Engineers II^{4}or STAT 3003 Statistical Methods | 3 | |

DASC 2103 Data Structures & Algorithms | 3 | |

Year Total: | 16 | 15 |

Third Year | Units | |

Fall | Spring | |

PHIL 3103 Ethics and the Professions (Satisfies General Education Outcome 5.1) | 3 | |

DASC 3103 Cloud Computing and Big Data | 3 | |

ACCT 3543 Accounting Analytics | 3 | |

ISYS 4193 Business Analytics and Visualization | 3 | |

State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.2 and 3.3)^{2} | 3 | |

ISYS 4293 Business Intelligence | 3 | |

DASC 3203 Optimization Methods in Data Science | 3 | |

DASC 3213 Statistical Learning | 3 | |

ECON 2143 Basic Economics: Theory and Practice (Satisfies General Education Outcome 3.3) | 3 | |

State Minimum Core Natural Science with Lab Elective (Satisfies General Education Outcome 3.4) | 4 | |

Year Total: | 15 | 16 |

Fourth Year | Units | |

Fall | Spring | |

DASC 4892 Data Science Practicum I | 2 | |

DASC 4113 Machine Learning | 3 | |

DASC 4123 Social Problems in Data Science and Analytics | 3 | |

Accounting Analtyics Concentration Elective | 3 | |

State Minimum Core Fine Arts Elective (Satisfies General Education Outcome 3.1)^{3} | 3 | |

DASC 4993 Data Science Practicum II (Satisfies General Education Outcome 6.1) | 3 | |

State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.3 and 4.1)^{4} | 3 | |

General Education Elective^{5} | 3 | |

State Minimum Core U.S. History or Government Elective (Satisfies General Education Outcome 4.2) | 3 | |

Year Total: | 14 | 12 |

Total Units in Sequence: | 120 |

^{1} | Students have demonstrated successful completion of the learning indicators identified for learning outcome 2.1, by meeting the prerequisites for MATH 2554. |

^{2} | The Social Science Elective courses which satisfy General Education Outcomes 3.2 and 3.3 include: HIST 1113, HIST 1113H, HIST 1123, HIST 1123H, HIST 2003, or HIST 2013. Note, courses cannot be counted twice in degree requirements. |

^{3} | The Fine Arts Elective courses which satisfy General Education Outcome 3.1 include: ARCH 1003, ARHS 1003, COMM 1003, DANC 1003, LARC 1003, MLIT 1003, MLIT 1003H, MLIT 1013, MLIT 1013H, MLIT 1333, THTR 1003, THTR 1013, or THTR 1013H. |

^{4} | The Social Sciences Elective courses which satisfy General Education Outcomes 3.3 and 4.1 include: ANTH 1023, COMM 1023, HDFS 1403, HDFS 2413, HIST 1113, HIST 1113H, HIST 1123, HIST 1123H, HIST 2093, HUMN 1114H, HUMN 2114H, INST 2013, INST 2813, INST 2813H, PLSC 2013, PLSC 2813, PLSC 2813H, RESM 2853, SOCI 2013, SOCI 2013H, or SOCI 2033. |

^{5} | Students are required to complete 40 hours of upper division courses (3000-4000 level). It is recommended that students consult with their adviser when making course selections. |

### Requirements for B.S. in Data Science with Bioinformatics Concentration

Below are the general requirements for a Bachelor of Science degree with a major in Data Science, followed by specific requirements for the Bioinformatics Concentration. Below those is a recommended eight-semester plan to achieve those requirements in a timely fashion.

**Requirements for B.S. in Data Science**

Each student in Data Science is required to complete 120 hours of coursework including the state minimum core. To be eligible for graduation, all students must complete at least 60 hours of Data Science (DTSC) Core required classes at the University of Arkansas. Each student in Data Science is also required to complete an additional 20-21 hours (depending on the student's chosen concentration) of required and elective concentration courses to meet the requirements for a concentration.

Additional opportunities are available to enhance the educational experience of students in these areas. Students should consult their academic adviser for recommendations.

State Minimum Core and General Education (36 hours) | ||

ENGL 1013 | Composition I (ACTS Equivalency = ENGL 1013) | 3 |

ENGL 1033 | Technical Composition II (ACTS Equivalency = ENGL 1023) | 3 |

MATH 2554 | Calculus I (ACTS Equivalency = MATH 2405) | 4 |

Science state minimum electives (two courses with labs) | 8 | |

Fine Arts state minimum core | 3 | |

Humanities state minimum core | ||

PHIL 3103 | Ethics and the Professions | 3 |

U.S. History and Government state minimum core | 3 | |

History of the American People to 1877 (ACTS Equivalency = HIST 2113) | ||

or HIST 2013 | History of the American People, 1877 to Present (ACTS Equivalency = HIST 2123) | |

or PLSC 2003 | American National Government (ACTS Equivalency = PLSC 2003) | |

Social Science state minimum core electives | 6 | |

ECON 2143 | Basic Economics: Theory and Practice (represents 3 of the 9 required credit hours for Social Science elective) | 3 |

Data Science Required Core (47 hours) | ||

DASC 1001 | Introduction to Data Science (First-Year Program - Introduction to Data Science) | 1 |

DASC 1104 | Programming Languages for Data Science (Programming Languages for Data Science (R, Python)) | 4 |

DASC 1204 | Introduction to Object Oriented Programming for Data Science (Introduction to Object Oriented Programming for Data Science (JAVA)) | 4 |

DASC 2594 | Multivariable Math for Data Scientists (Multivariable Math for Data Scientists) | 4 |

DASC 1222 | Role of Data Science in Today's World (Role of Data Science in Today's World) | 2 |

DASC 2103 | Data Structures & Algorithms (Data Structures & Algorithms) | 3 |

DASC 2113 | Principles and Techniques of Data Science (Principles & Techniques of Data Science) | 3 |

DASC 2203 | Data Management and Data Base (Data Management & Data Base) | 3 |

DASC 2213 | Data Visualization and Communication (Data Visualization & Communication (Tableau)) | 3 |

DASC 3103 | Cloud Computing and Big Data (Cloud Computing & Big Data) | 3 |

DASC 3203 | Optimization Methods in Data Science (Optimization Methods in Data Science) | 3 |

DASC 3213 | Statistical Learning (Statistical Learning) | 3 |

DASC 4892 | Data Science Practicum I (Data Science Practicum I) | 2 |

DASC 4113 | Machine Learning (Machine Learning) | 3 |

DASC 4123 | Social Problems in Data Science and Analytics (Social Problems (Issues) in DASC & Analytics) | 3 |

DASC 4993 | Data Science Practicum II (Data Science Practicum II) | 3 |

Data Science Required Additional Courses | ||

MATH 2564 | Calculus II (ACTS Equivalency = MATH 2505) | 4 |

SEVI 2053 | Business Foundations | 3 |

Choose from one of these two-course sequences | 6 | |

Applied Probability and Statistics for Engineers I and Applied Probability and Statistics for Engineers II (Applied Probability and Statistics for Engineers II) | ||

Or | ||

Introduction to Probability and Statistical Methods (Statistical Methods) | ||

Data Science Concentration Courses | 20-21 | |

General Electives | 3-4 | |

Total Hours | 120 |

### Required Bioinformatics Concentration Courses

BIOL 2533 | Cell Biology | 3 |

BIOL 2323 | General Genetics | 3 |

Choose one of the following courses: | 3 | |

Evolutionary Biology | ||

General Ecology | ||

Elective Bioinformatics Concentration Courses (Select 12 hours) | 12 | |

Note: May not fulfill concentration electives with all GIS courses | ||

Conservation Genetics | ||

Bacterial Lifestyles | ||

Special Topics in Biological Sciences | ||

Practical Programming for Biologists | ||

Special Topics in Biological Sciences | ||

Geospatial Applications and Information Science | ||

Spatial Analysis Using ArcGIS | ||

Geospatial Data Mining | ||

Introduction to Raster GIS | ||

Total Hours | 21 |

### Data Science B.S. with Bioinformatics Concentration

Eight-Semester Program

First Year | Units | |
---|---|---|

Fall | Spring | |

MATH 2554 Calculus I (ACTS Equivalency = MATH 2405) (Satisfies General Education Outcome 2.1)^{1} | 4 | |

ENGL 1013 Composition I (ACTS Equivalency = ENGL 1013) (Satisfies General Education Outcome 1.1) | 3 | |

Satisfies General Education Outcome 3.4: | ||

BIOL 1543 Principles of Biology (ACTS Equivalency = BIOL 1014 Lecture) & BIOL 1541L Principles of Biology Laboratory (ACTS Equivalency = BIOL 1014 Lab) | 4 | |

DASC 1001 Introduction to Data Science | 1 | |

DASC 1104 Programming Languages for Data Science | 4 | |

MATH 2564 Calculus II (ACTS Equivalency = MATH 2505) | 4 | |

Satisfies General Education Outcome 3.4: | ||

CHEM 1103 University Chemistry I (ACTS Equivalency = CHEM 1414 Lecture) & CHEM 1101L University Chemistry I Laboratory (ACTS Equivalency = CHEM 1414 Lab) | 4 | |

DASC 1204 Introduction to Object Oriented Programming for Data Science | 4 | |

DASC 1222 Role of Data Science in Today's World | 2 | |

ENGL 1033 Technical Composition II (ACTS Equivalency = ENGL 1023) (Satisfies General Education Outcome 1.2) | 3 | |

Year Total: | 16 | 17 |

Second Year | Units | |

Fall | Spring | |

DASC 2594 Multivariable Math for Data Scientists | 4 | |

DASC 2113 Principles and Techniques of Data Science | 3 | |

BIOL 2533 Cell Biology | 3 | |

Bioinformatics Elective | 3 | |

DASC 2213 Data Visualization and Communication | 3 | |

DASC 2203 Data Management and Data Base | 3 | |

INEG 2313 Applied Probability and Statistics for Engineers I^{4}or STAT 3013 Introduction to Probability | 3 | |

DASC 2103 Data Structures & Algorithms | 3 | |

BIOL 2323 General Genetics | 3 | |

SEVI 2033 Business Foundations for Innovators and Entrepreneurs | 3 | |

Year Total: | 16 | 15 |

Third Year | Units | |

Fall | Spring | |

PHIL 3103 Ethics and the Professions (Satisfies General Education Outcome 5.1) | 3 | |

DASC 3103 Cloud Computing and Big Data | 3 | |

INEG 2333 Applied Probability and Statistics for Engineers II^{4}or STAT 3003 Statistical Methods | 3 | |

BIOL 3863 General Ecology or BIOL 3023 Evolutionary Biology | 3 | |

Bioinformatics Elective | 3 | |

DASC 3203 Optimization Methods in Data Science | 3 | |

DASC 3213 Statistical Learning | 3 | |

ECON 2143 Basic Economics: Theory and Practice (Satisfies General Education Outcome 3.3) | 3 | |

Bioinformatics Elective | 3 | |

State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.2 and 3.3)^{2} | 3 | |

Year Total: | 15 | 15 |

Fourth Year | Units | |

Fall | Spring | |

DASC 4892 Data Science Practicum I | 2 | |

DASC 4113 Machine Learning | 3 | |

DASC 4123 Social Problems in Data Science and Analytics | 3 | |

Bioinformatics Elective | 3 | |

State Minimum Core Fine Arts Elective (Satisfies General Education Outcome 3.1)^{3} | 3 | |

DASC 4993 Data Science Practicum II (Satisfies General Education Outcome 6.1) | 3 | |

State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.3 and 4.1)^{4} | 3 | |

General Education Elective^{5} | 3 | |

State Minimum Core U.S. History or Government Elective (Satisfies General Education Outcome 4.2) | 3 | |

Year Total: | 14 | 12 |

Total Units in Sequence: | 120 |

^{1} | Students have demonstrated successful completion of the learning indicators identified for learning outcome 2.1, by meeting the prerequisites for MATH 2554. |

^{2} | The Social Science Elective courses which satisfy General Education Outcomes 3.2 and 3.3 include: HIST 1113, HIST 1113H, HIST 1123, HIST 1123H, HIST 2003, or HIST 2013. Note, courses cannot be counted twice in degree requirements. |

^{3} | The Fine Arts Elective courses which satisfy General Education Outcome 3.1 include: ARCH 1003, ARHS 1003, COMM 1003, DANC 1003, LARC 1003, MLIT 1003, MLIT 1003H, MLIT 1013, MLIT 1013H, MLIT 1333, THTR 1003, THTR 1013, or THTR 1013H. |

^{4} | The Social Sciences Elective courses which satisfy General Education Outcomes 3.3 and 4.1 include: ANTH 1023, COMM 1023, HDFS 1403, HDFS 2413, HIST 1113, HIST 1113H, HIST 1123, HIST 1123H, HIST 2093, HUMN 1114H, HUMN 2114H, INST 2013, INST 2813, INST 2813H, PLSC 2013, PLSC 2813, PLSC 2813H, RESM 2853, SOCI 2013, SOCI 2013H, or SOCI 2033. |

^{5} | Students are required to complete 40 hours of upper division courses (3000-4000 level). It is recommended that students consult with their adviser when making course selections. |

### Requirements for B.S. in Data Science with Biomedical and Healthcare Concentration

Below are the general requirements for a Bachelor of Science degree with a major in Data Science, followed by specific requirements for the Biomedical and Healthcare Concentration. Below those is a recommended eight-semester plan to achieve those requirements in a timely fashion.

**Requirements for B.S. in Data Science**

Each student in Data Science is required to complete 120 hours of coursework including the state minimum core. To be eligible for graduation, all students must complete at least 60 hours of Data Science (DTSC) Core required classes at the University of Arkansas. Each student in Data Science is also required to complete an additional 20-21 hours (depending on the student's chosen concentration) of required and elective concentration courses to meet the requirements for a concentration.

Additional opportunities are available to enhance the educational experience of students in these areas. Students should consult their academic adviser for recommendations.

State Minimum Core and General Education (36 hours) | ||

ENGL 1013 | Composition I (ACTS Equivalency = ENGL 1013) | 3 |

ENGL 1033 | Technical Composition II (ACTS Equivalency = ENGL 1023) | 3 |

MATH 2554 | Calculus I (ACTS Equivalency = MATH 2405) | 4 |

Science state minimum electives (two courses with labs) | 8 | |

Fine Arts state minimum core | 3 | |

Humanities state minimum core | ||

PHIL 3103 | Ethics and the Professions | 3 |

U.S. History and Government state minimum core | 3 | |

History of the American People to 1877 (ACTS Equivalency = HIST 2113) | ||

or HIST 2013 | History of the American People, 1877 to Present (ACTS Equivalency = HIST 2123) | |

or PLSC 2003 | American National Government (ACTS Equivalency = PLSC 2003) | |

Social Science state minimum core electives | 6 | |

ECON 2143 | Basic Economics: Theory and Practice (represents 3 of the 9 required credit hours for Social Science elective) | 3 |

Data Science Required Core (47 hours) | ||

DASC 1001 | Introduction to Data Science (First-Year Program - Introduction to Data Science) | 1 |

DASC 1104 | Programming Languages for Data Science (Programming Languages for Data Science (R, Python)) | 4 |

DASC 1204 | Introduction to Object Oriented Programming for Data Science (Introduction to Object Oriented Programming for Data Science (JAVA)) | 4 |

DASC 2594 | Multivariable Math for Data Scientists (Multivariable Math for Data Scientists) | 4 |

DASC 1222 | Role of Data Science in Today's World (Role of Data Science in Today's World) | 2 |

DASC 2103 | Data Structures & Algorithms (Data Structures & Algorithms) | 3 |

DASC 2113 | Principles and Techniques of Data Science (Principles & Techniques of Data Science) | 3 |

DASC 2203 | Data Management and Data Base (Data Management & Data Base) | 3 |

DASC 2213 | Data Visualization and Communication (Data Visualization & Communication (Tableau)) | 3 |

DASC 3103 | Cloud Computing and Big Data (Cloud Computing & Big Data) | 3 |

DASC 3203 | Optimization Methods in Data Science (Optimization Methods in Data Science) | 3 |

DASC 3213 | Statistical Learning (Statistical Learning) | 3 |

DASC 4892 | Data Science Practicum I (Data Science Practicum I) | 2 |

DASC 4113 | Machine Learning (Machine Learning) | 3 |

DASC 4123 | Social Problems in Data Science and Analytics (Social Problems (Issues) in DASC & Analytics) | 3 |

DASC 4993 | Data Science Practicum II (Data Science Practicum II) | 3 |

Data Science Required Additional Courses | ||

MATH 2564 | Calculus II (ACTS Equivalency = MATH 2505) | 4 |

SEVI 2053 | Business Foundations | 3 |

Choose from one of these two-course sequences | 6 | |

Applied Probability and Statistics for Engineers I and Applied Probability and Statistics for Engineers II (Applied Probability and Statistics for Engineers II) | ||

Or | ||

Introduction to Probability and Statistical Methods (Statistical Methods) | ||

Data Science Concentration Courses | 20-21 | |

General Electives | 3-4 | |

Total Hours | 120 |

### Required Biomedical and Healthcare Informatics Concentration Courses

Students completing the Biomedical and Healthcare Informatics Concentration must select CHEM 1103 and PHYS 2054 for the State Minimum Core Science Electives.

BMEG 2614 | Introduction to Biomedical Engineering | 4 |

CHEM 1123 | University Chemistry II (ACTS Equivalency = CHEM 1424 Lecture) | 3 |

BIOL 2213 | Human Physiology (ACTS Equivalency = BIOL 2414 Lecture) | 3 |

BMEG 3801 | Clinical Observations and Needs Finding | 1 |

Elective Biomedical and Healthcare Informatics Concentration (Select 10 credit hours) | 10 | |

Cardiovascular Physiology and Devices | ||

Regenerative Medicine | ||

Tissue Engineering | ||

Biomedical Microscopy | ||

Biomedical Optics and Imaging | ||

Biomedical Data and Image Analysis | ||

Genome Engineering and Synthetic Biology | ||

Human Physiology Laboratory (ACTS Equivalency = BIOL 2414 Lab) | ||

University Chemistry II Laboratory (ACTS Equivalency = CHEM 1424 Lab) | ||

Total Hours | 21 |

### Data Science B.S. with Biomedical and Healthcare Informatics Concentration Eight-Semester Program

First Year | Units | |
---|---|---|

Fall | Spring | |

MATH 2554 Calculus I (ACTS Equivalency = MATH 2405) (Satisfies General Education Outcome 2.1 )^{1} | 4 | |

ENGL 1013 Composition I (ACTS Equivalency = ENGL 1013) (Satisfies General Education Outcome 1.1) | 3 | |

Satisfies General Education Outcome 3.4: | ||

CHEM 1103 University Chemistry I (ACTS Equivalency = CHEM 1414 Lecture) & CHEM 1101L University Chemistry I Laboratory (ACTS Equivalency = CHEM 1414 Lab) | 4 | |

DASC 1001 Introduction to Data Science | 1 | |

DASC 1104 Programming Languages for Data Science | 4 | |

MATH 2564 Calculus II (ACTS Equivalency = MATH 2505) | 4 | |

ENGL 1033 Technical Composition II (ACTS Equivalency = ENGL 1023) (Satisfies General Education Outcome 1.2) | 3 | |

PHYS 2054 University Physics I (ACTS Equivalency = PHYS 2034) (Satisfies General Education Outcome 3.4) | 4 | |

DASC 1204 Introduction to Object Oriented Programming for Data Science | 4 | |

DASC 1222 Role of Data Science in Today's World | 2 | |

Year Total: | 16 | 17 |

Second Year | Units | |

Fall | Spring | |

DASC 2594 Multivariable Math for Data Scientists | 4 | |

INEG 2313 Applied Probability and Statistics for Engineers I^{4}or STAT 3013 Introduction to Probability | 3 | |

DASC 2113 Principles and Techniques of Data Science | 3 | |

BMEG 2614 Introduction to Biomedical Engineering | 4 | |

DASC 2213 Data Visualization and Communication | 3 | |

SEVI 2053 Business Foundations | 3 | |

INEG 2333 Applied Probability and Statistics for Engineers II^{4}or STAT 3003 Statistical Methods | 3 | |

DASC 2203 Data Management and Data Base | 3 | |

CHEM 1123 University Chemistry II (ACTS Equivalency = CHEM 1424 Lecture) | 3 | |

DASC 2103 Data Structures & Algorithms | 3 | |

Year Total: | 17 | 15 |

Third Year | Units | |

Fall | Spring | |

PHIL 3103 Ethics and the Professions (Satisfies General Education Outcome 5.1) | 3 | |

DASC 3103 Cloud Computing and Big Data | 3 | |

BIOL 2213 Human Physiology (ACTS Equivalency = BIOL 2414 Lecture) | 3 | |

State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.2 and 3.3)^{2} | 3 | |

State Minimum Core Fine Arts Elective (Satisfies General Education Outcome 3.1)^{3} | 3 | |

DASC 3203 Optimization Methods in Data Science | 3 | |

DASC 3213 Statistical Learning | 3 | |

BMEG 3801 Clinical Observations and Needs Finding | 1 | |

ECON 2143 Basic Economics: Theory and Practice (Satisfies General Education Outcome 3.3) | 3 | |

State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.3 and 4.1) ^{4} | 3 | |

Year Total: | 15 | 13 |

Fourth Year | Units | |

Fall | Spring | |

DASC 4892 Data Science Practicum I | 2 | |

DASC 4113 Machine Learning | 3 | |

Concentration Elective Course | 3 | |

DASC 4123 Social Problems in Data Science and Analytics | 3 | |

Concentration Elective Course | 3 | |

DASC 4993 Data Science Practicum II (Satisfies General Education Outcome 6.1) | 3 | |

General Elective Course^{5} | 3 | |

Concentration Elective Course(s)^{5} | 4 | |

State Minimum Core U.S. History or Government Elective (Satisfies General Education Outcome 4.2) | 3 | |

Year Total: | 14 | 13 |

Total Units in Sequence: | 120 |

^{1} | Students have demonstrated successful completion of the learning indicators identified for learning outcome 2.1, by meeting the prerequisites for MATH 2554. |

^{2} | The Social Science Elective courses which satisfy General Education Outcomes 3.2 and 3.3 include: HIST 1113, HIST 1113H, HIST 1123, HIST 1123H, HIST 2003, or HIST 2013. Note, courses cannot be counted twice in degree requirements. |

^{3} | The Fine Arts Elective courses which satisfy General Education Outcome 3.1 include: ARCH 1003, ARHS 1003, COMM 1003, DANC 1003, LARC 1003, MLIT 1003, MLIT 1003H, MLIT 1013, MLIT 1013H, MLIT 1333, THTR 1003, THTR 1013, or THTR 1013H. |

^{4} | The Social Sciences Elective courses which satisfy General Education Outcomes 3.3 and 4.1 include: ANTH 1023, COMM 1023, HDFS 1403, HDFS 2413, HIST 1113, HIST 1113H, HIST 1123, HIST 1123H, HIST 2093, HUMN 1114H, HUMN 2114H, INST 2013, INST 2813, INST 2813H, PLSC 2013, PLSC 2813, PLSC 2813H, RESM 2853, SOCI 2013, SOCI 2013H, or SOCI 2033. |

^{5} | Students are required to complete 40 hours of upper division courses (3000-4000 level). It is recommended that students consult with their adviser when making course selections. |

### Requirements for B.S. in Data Science with Business Data Analytics Concentration

Below are the general requirements for a Bachelor of Science degree with a major in Data Science, followed by specific requirements for the Business Data Analytics Concentration. Below those is a recommended eight-semester plan to achieve those requirements in a timely fashion.

**Requirements for B.S. in Data Science**

State Minimum Core and General Education (36 hours) | ||

ENGL 1013 | Composition I (ACTS Equivalency = ENGL 1013) | 3 |

ENGL 1033 | Technical Composition II (ACTS Equivalency = ENGL 1023) | 3 |

MATH 2554 | Calculus I (ACTS Equivalency = MATH 2405) | 4 |

Science state minimum electives (two courses with labs) | 8 | |

Fine Arts state minimum core | 3 | |

Humanities state minimum core | ||

PHIL 3103 | Ethics and the Professions | 3 |

U.S. History and Government state minimum core | 3 | |

History of the American People to 1877 (ACTS Equivalency = HIST 2113) | ||

or HIST 2013 | History of the American People, 1877 to Present (ACTS Equivalency = HIST 2123) | |

or PLSC 2003 | American National Government (ACTS Equivalency = PLSC 2003) | |

Social Science state minimum core electives | 6 | |

ECON 2143 | 3 | |

Data Science Required Core (47 hours) | ||

DASC 1001 | Introduction to Data Science (First-Year Program - Introduction to Data Science) | 1 |

DASC 1104 | Programming Languages for Data Science (Programming Languages for Data Science (R, Python)) | 4 |

DASC 1204 | 4 | |

DASC 2594 | Multivariable Math for Data Scientists (Multivariable Math for Data Scientists) | 4 |

DASC 1222 | Role of Data Science in Today's World (Role of Data Science in Today's World) | 2 |

DASC 2103 | Data Structures & Algorithms (Data Structures & Algorithms) | 3 |

DASC 2113 | Principles and Techniques of Data Science (Principles & Techniques of Data Science) | 3 |

DASC 2203 | Data Management and Data Base (Data Management & Data Base) | 3 |

DASC 2213 | Data Visualization and Communication (Data Visualization & Communication (Tableau)) | 3 |

DASC 3103 | Cloud Computing and Big Data (Cloud Computing & Big Data) | 3 |

DASC 3203 | Optimization Methods in Data Science (Optimization Methods in Data Science) | 3 |

DASC 3213 | Statistical Learning (Statistical Learning) | 3 |

DASC 4892 | Data Science Practicum I (Data Science Practicum I) | 2 |

DASC 4113 | Machine Learning (Machine Learning) | 3 |

DASC 4123 | Social Problems in Data Science and Analytics (Social Problems (Issues) in DASC & Analytics) | 3 |

DASC 4993 | Data Science Practicum II (Data Science Practicum II) | 3 |

Data Science Required Additional Courses | ||

MATH 2564 | Calculus II (ACTS Equivalency = MATH 2505) | 4 |

SEVI 2053 | Business Foundations | 3 |

Choose from one of these two-course sequences | 6 | |

Applied Probability and Statistics for Engineers I and Applied Probability and Statistics for Engineers II (Applied Probability and Statistics for Engineers II) | ||

Or | ||

Introduction to Probability and Statistical Methods (Statistical Methods) | ||

Data Science Concentration Courses | 20-21 | |

General Electives | 3-4 | |

Total Hours | 120 |

### Required Business Data Concentration Courses

ACCT 2013 | Accounting Principles | 3 |

ACCT 2023 | Accounting Principles II | 3 |

WCOB 1033 | Data Analysis and Interpretation | 3 |

ISYS 4193 | Business Analytics and Visualization | 3 |

ISYS 4293 | Business Intelligence | 3 |

Elective Business Data Analytics Concentration Courses (Select 6 hours) | 6 | |

Principles of Finance | ||

Financial Analysis | ||

Introduction to Econometrics | ||

Forecasting | ||

Introduction to Marketing | ||

Marketing Research | ||

Total Hours | 21 |

### Data Science B.S. with Business Data Concentration

Eight-Semester Program

First Year | Units | |
---|---|---|

Fall | Spring | |

MATH 2554 Calculus I (ACTS Equivalency = MATH 2405) (Satisfies General Education Outcome 2.1)^{1} | 4 | |

ENGL 1013 Composition I (ACTS Equivalency = ENGL 1013) (Satisfies General Education Outcome 1.1) | 3 | |

State Minimum Core Natural Science Elective with Lab (Satisfies General Education Outcome 3.4) | 4 | |

DASC 1001 Introduction to Data Science | 1 | |

DASC 1104 Programming Languages for Data Science | 4 | |

MATH 2564 Calculus II (ACTS Equivalency = MATH 2505) | 4 | |

DASC 1204 Introduction to Object Oriented Programming for Data Science | 4 | |

DASC 1222 Role of Data Science in Today's World | 2 | |

ACCT 2013 Accounting Principles | 3 | |

3 | ||

Year Total: | 16 | 16 |

Second Year | Units | |

Fall | Spring | |

DASC 2594 Multivariable Math for Data Scientists | 4 | |

DASC 2113 Principles and Techniques of Data Science | 3 | |

WCOB 1033 Data Analysis and Interpretation^{ } | 3 | |

ACCT 2023 Accounting Principles II^{ } | 3 | |

DASC 2213 Data Visualization and Communication | 3 | |

DASC 2103 Data Structures & Algorithms | 3 | |

INEG 2313 Applied Probability and Statistics for Engineers I^{4}or STAT 3013 Introduction to Probability | 3 | |

SEVI 2053 Business Foundations | 3 | |

State Minimum Core U.S. History or Government Elective (Satisfies General Education Outcome 4.2) | 3 | |

DASC 2203 Data Management and Data Base | 3 | |

Year Total: | 16 | 15 |

Third Year | Units | |

Fall | Spring | |

PHIL 3103 Ethics and the Professions (Satisfies General Education Outcome 5.1) | 3 | |

DASC 3103 Cloud Computing and Big Data | 3 | |

ISYS 4193 Business Analytics and Visualization | 3 | |

INEG 2333 Applied Probability and Statistics for Engineers II^{4}or STAT 3003 Statistical Methods | 3 | |

State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.2 and 3.3)^{2} | 3 | |

ISYS 4293 Business Intelligence | 3 | |

DASC 3203 Optimization Methods in Data Science | 3 | |

DASC 3213 Statistical Learning | 3 | |

ECON 2143 Basic Economics: Theory and Practice (Satisfies General Education Outcome 3.3) | 3 | |

State Minimum Core Natural Science with Lab Elective (Satisfies General Education Outcome 3.4) | 4 | |

Year Total: | 15 | 16 |

Fourth Year | Units | |

Fall | Spring | |

DASC 4892 Data Science Practicum I | 2 | |

DASC 4113 Machine Learning | 3 | |

DASC 4123 Social Problems in Data Science and Analytics | 3 | |

Business Data Analytics Elective | 3 | |

State Minimum Core Fine Arts Elective (Satisfies General Education Outcome 3.1)^{3} | 3 | |

DASC 4993 Data Science Practicum II (Satisfies General Education Outcome 6.1) | 3 | |

Business Data Analytics Elective | 3 | |

State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.3 and 4.1)^{4} | 3 | |

General Education Elective^{5} | 3 | |

Year Total: | 14 | 12 |

Total Units in Sequence: | 120 |

^{1} | |

^{2} | |

^{3} | |

^{4} | |

^{5} |

### Requirements for B.S. in Data Science with Computational Analytics Concentration

Below are the general requirements for a Bachelor of Science degree with a major in Data Science, followed by specific requirements for the Computational Analytics Concentration. Below those is a recommended eight-semester plan to achieve those requirements in a timely fashion.

**Requirements for B.S. in Data Science**

State Minimum Core and General Education (36 hours) | ||

ENGL 1013 | Composition I (ACTS Equivalency = ENGL 1013) | 3 |

ENGL 1033 | Technical Composition II (ACTS Equivalency = ENGL 1023) | 3 |

MATH 2554 | Calculus I (ACTS Equivalency = MATH 2405) | 4 |

Science state minimum electives (two courses with labs) | 8 | |

Fine Arts state minimum core | 3 | |

Humanities state minimum core | ||

PHIL 3103 | Ethics and the Professions | 3 |

U.S. History and Government state minimum core | 3 | |

History of the American People to 1877 (ACTS Equivalency = HIST 2113) | ||

or HIST 2013 | History of the American People, 1877 to Present (ACTS Equivalency = HIST 2123) | |

or PLSC 2003 | American National Government (ACTS Equivalency = PLSC 2003) | |

Social Science state minimum core electives | 6 | |

ECON 2143 | 3 | |

Data Science Required Core (47 hours) | ||

DASC 1001 | Introduction to Data Science (First-Year Program - Introduction to Data Science) | 1 |

DASC 1104 | Programming Languages for Data Science (Programming Languages for Data Science (R, Python)) | 4 |

DASC 1204 | 4 | |

DASC 2594 | Multivariable Math for Data Scientists (Multivariable Math for Data Scientists) | 4 |

DASC 1222 | Role of Data Science in Today's World (Role of Data Science in Today's World) | 2 |

DASC 2103 | Data Structures & Algorithms (Data Structures & Algorithms) | 3 |

DASC 2113 | Principles and Techniques of Data Science (Principles & Techniques of Data Science) | 3 |

DASC 2203 | Data Management and Data Base (Data Management & Data Base) | 3 |

DASC 2213 | Data Visualization and Communication (Data Visualization & Communication (Tableau)) | 3 |

DASC 3103 | Cloud Computing and Big Data (Cloud Computing & Big Data) | 3 |

DASC 3203 | Optimization Methods in Data Science (Optimization Methods in Data Science) | 3 |

DASC 3213 | Statistical Learning (Statistical Learning) | 3 |

DASC 4892 | Data Science Practicum I (Data Science Practicum I) | 2 |

DASC 4113 | Machine Learning (Machine Learning) | 3 |

DASC 4123 | Social Problems in Data Science and Analytics (Social Problems (Issues) in DASC & Analytics) | 3 |

DASC 4993 | Data Science Practicum II (Data Science Practicum II) | 3 |

Data Science Required Additional Courses | ||

MATH 2564 | Calculus II (ACTS Equivalency = MATH 2505) | 4 |

SEVI 2053 | Business Foundations | 3 |

Choose from one of these two-course sequences | 6 | |

Applied Probability and Statistics for Engineers I and Applied Probability and Statistics for Engineers II (Applied Probability and Statistics for Engineers II) | ||

Or | ||

Introduction to Probability and Statistical Methods (Statistical Methods) | ||

Data Science Concentration Courses | 20-21 | |

General Electives | 3-4 | |

Total Hours | 120 |

### Required Computational Analytics Concentration Courses

CSCE 3513 | Software Engineering | 3 |

CSCE 4143 | Data Mining | 3 |

CSCE 4613 | Artificial Intelligence | 3 |

Elective Computational Analytics Concentration Courses (Select 12 hours) | 12 | |

Cluster Computing | ||

Special Topics | ||

Algorithms | ||

Concurrent Computing | ||

Information Security | ||

Information Retrieval | ||

Note: Other courses from CSCE and/or other concentrations of DASC can also be added to the concentration electives. | ||

Total Hours | 21 |

### Data Science B.S. with Computational Analytics Concentration

Eight-Semester Program

First Year | Units | |
---|---|---|

Fall | Spring | |

MATH 2554 Calculus I (ACTS Equivalency = MATH 2405) (Satisfies General Education Outcome 2.1)^{1} | 4 | |

ENGL 1013 Composition I (ACTS Equivalency = ENGL 1013) (Satisfies General Education Outcome 1.1) | 3 | |

State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.2 and 3.3) ^{2} | 3 | |

DASC 1001 Introduction to Data Science | 1 | |

DASC 1104 Programming Languages for Data Science | 4 | |

MATH 2564 Calculus II (ACTS Equivalency = MATH 2505) | 4 | |

DASC 1204 Introduction to Object Oriented Programming for Data Science | 4 | |

DASC 1222 Role of Data Science in Today's World | 2 | |

State Minimum Core Natural Science Elective with Lab (Satisfies General Education Outcome 3.4) | 4 | |

3 | ||

Year Total: | 15 | 17 |

Second Year | Units | |

Fall | Spring | |

DASC 2594 Multivariable Math for Data Scientists | 4 | |

DASC 2113 Principles and Techniques of Data Science | 3 | |

State Minimum Core U.S. History or Government Elective (Satisfies General Education Outcome 4.2) | 3 | |

DASC 2213 Data Visualization and Communication | 3 | |

DASC 2203 Data Management and Data Base | 3 | |

CSCE 3513 Software Engineering | 3 | |

DASC 2103 Data Structures & Algorithms | 3 | |

INEG 2313 Applied Probability and Statistics for Engineers I^{In order to meet upper division prerequisites, students completing the Computational Analytics Concentration should select INEG 2313 and INEG 2333}or STAT 3013 Introduction to Probability | 3 | |

SEVI 2053 Business Foundations | 3 | |

Year Total: | 13 | 15 |

Third Year | Units | |

Fall | Spring | |

PHIL 3103 Ethics and the Professions (Satisfies General Education Outcome 5.1) | 3 | |

DASC 3103 Cloud Computing and Big Data | 3 | |

INEG 2333 Applied Probability and Statistics for Engineers II^{In order to meet upper division prerequisites, students completing the Computational Analytics Concentration should select INEG 2313 and INEG 2333}or STAT 3003 Statistical Methods | 3 | |

CSCE 4613 Artificial Intelligence | 3 | |

Computational Analytics Elective | 3 | |

DASC 3203 Optimization Methods in Data Science | 3 | |

DASC 3213 Statistical Learning | 3 | |

CSCE 4143 Data Mining | 3 | |

State Minimum Core Natural Science Elective with Lab (Satisfies General Education Outcome 3.4) | 4 | |

ECON 2143 Basic Economics: Theory and Practice (Satisfies General Education Outcome 3.3) | 3 | |

Year Total: | 15 | 16 |

Fourth Year | Units | |

Fall | Spring | |

DASC 4892 Data Science Practicum I | 2 | |

DASC 4113 Machine Learning | 3 | |

DASC 4123 Social Problems in Data Science and Analytics | 3 | |

Computational Analytics Elective | 3 | |

State Minimum Core Fine Arts Elective (Satisfies General Education Outcome 3.1)^{3} | 3 | |

DASC 4993 Data Science Practicum II (Satisfies General Education Outcome 6.1) | 3 | |

General Education Elective | 3 | |

Computational Analytics Electives | 6 | |

State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.3 and 4.1^{4} | 3 | |

Year Total: | 14 | 15 |

Total Units in Sequence: | 120 |

^{1} | |

^{2} | |

^{3} | |

^{4} |

### Requirements for B.S. in Data Science with Data Science Statistics Concentration

Below are the general requirements for a Bachelor of Science degree with a major in Data Science, followed by specific requirements for the Data Science Statistics Concentration. Below those is a recommended eight-semester plan to achieve those requirements in a timely fashion.

**Requirements for B.S. in Data Science**

State Minimum Core and General Education (36 hours) | ||

ENGL 1013 | Composition I (ACTS Equivalency = ENGL 1013) | 3 |

ENGL 1033 | Technical Composition II (ACTS Equivalency = ENGL 1023) | 3 |

MATH 2554 | Calculus I (ACTS Equivalency = MATH 2405) | 4 |

Science state minimum electives (two courses with labs) | 8 | |

Fine Arts state minimum core | 3 | |

Humanities state minimum core | ||

PHIL 3103 | Ethics and the Professions | 3 |

U.S. History and Government state minimum core | 3 | |

History of the American People to 1877 (ACTS Equivalency = HIST 2113) | ||

or HIST 2013 | History of the American People, 1877 to Present (ACTS Equivalency = HIST 2123) | |

or PLSC 2003 | American National Government (ACTS Equivalency = PLSC 2003) | |

Social Science state minimum core electives | 6 | |

ECON 2143 | 3 | |

Data Science Required Core (47 hours) | ||

DASC 1001 | Introduction to Data Science (First-Year Program - Introduction to Data Science) | 1 |

DASC 1104 | Programming Languages for Data Science (Programming Languages for Data Science (R, Python)) | 4 |

DASC 1204 | 4 | |

DASC 2594 | Multivariable Math for Data Scientists (Multivariable Math for Data Scientists) | 4 |

DASC 1222 | Role of Data Science in Today's World (Role of Data Science in Today's World) | 2 |

DASC 2103 | Data Structures & Algorithms (Data Structures & Algorithms) | 3 |

DASC 2113 | Principles and Techniques of Data Science (Principles & Techniques of Data Science) | 3 |

DASC 2203 | Data Management and Data Base (Data Management & Data Base) | 3 |

DASC 2213 | Data Visualization and Communication (Data Visualization & Communication (Tableau)) | 3 |

DASC 3103 | Cloud Computing and Big Data (Cloud Computing & Big Data) | 3 |

DASC 3203 | Optimization Methods in Data Science (Optimization Methods in Data Science) | 3 |

DASC 3213 | Statistical Learning (Statistical Learning) | 3 |

DASC 4892 | Data Science Practicum I (Data Science Practicum I) | 2 |

DASC 4113 | Machine Learning (Machine Learning) | 3 |

DASC 4123 | Social Problems in Data Science and Analytics (Social Problems (Issues) in DASC & Analytics) | 3 |

DASC 4993 | Data Science Practicum II (Data Science Practicum II) | 3 |

Data Science Required Additional Courses | ||

MATH 2564 | Calculus II (ACTS Equivalency = MATH 2505) | 4 |

SEVI 2053 | Business Foundations | 3 |

Choose from one of these two-course sequences | 6 | |

Applied Probability and Statistics for Engineers I and Applied Probability and Statistics for Engineers II (Applied Probability and Statistics for Engineers II) | ||

Or | ||

Introduction to Probability and Statistical Methods (Statistical Methods) | ||

Data Science Concentration Courses | 20-21 | |

General Electives | 3-4 | |

Total Hours | 120 |

### Required Data Science Statistics Concentration Courses

STAT 3113 | Introduction to Mathematical Statistics | 3 |

STAT 4373 | Experimental Design | 3 |

STAT 4013 | Statistical Forecasting and Prediction (Statistical Forecasting and Prediction) | 3 |

STAT 4333 | Analysis of Categorical Responses | 3 |

Elective Data Science Statistics Concentration (Select 9 hours) | 9 | |

Bayesian Methods (Bayesian Methods) | ||

Sampling Techniques | ||

Nonparametric Statistical Methods | ||

Artificial Intelligence | ||

Foundations of Geospatial Data Analysis | ||

Geospatial Applications and Information Science | ||

Geospatial Data Mining | ||

Total Hours | 21 |

### Data Science B.S. with Statistics Concentration

Eight-Semester Program

First Year | Units | |
---|---|---|

Fall | Spring | |

MATH 2554 Calculus I (ACTS Equivalency = MATH 2405) (Satisfies General Education Outcome 2.1)^{1} | 4 | |

ENGL 1013 Composition I (ACTS Equivalency = ENGL 1013) (Satisfies General Education Outcome 1.1) | 3 | |

State Minimum Core Natural Science Elective with Lab (Satisfies General Education Outcome 3.4) | 4 | |

DASC 1104 Programming Languages for Data Science | 4 | |

DASC 1001 Introduction to Data Science | 1 | |

State Minimum Core U.S. History or Government (Satisfies General Education Outcome 4.2) | 3 | |

MATH 2564 Calculus II (ACTS Equivalency = MATH 2505) | 4 | |

DASC 1204 Introduction to Object Oriented Programming for Data Science | 4 | |

DASC 1222 Role of Data Science in Today's World | 2 | |

3 | ||

Year Total: | 16 | 16 |

Second Year | Units | |

Fall | Spring | |

DASC 2594 Multivariable Math for Data Scientists | 4 | |

DASC 2113 Principles and Techniques of Data Science | 3 | |

Choose one of the following^{2} | 3 | |

STAT 3013 Introduction to Probability | ||

INEG 2313 Applied Probability and Statistics for Engineers I | ||

State Minimum Core Fine Arts Elective (Satisfies General Education Outcome 3.1)^{3} | 3 | |

DASC 2213 Data Visualization and Communication | 3 | |

SEVI 2053 Business Foundations | 3 | |

DASC 2203 Data Management and Data Base | 3 | |

STAT 3113 Introduction to Mathematical Statistics | 3 | |

DASC 2103 Data Structures & Algorithms | 3 | |

Choose one of the following^{2} | 3 | |

STAT 3003 Statistical Methods (Statistical Methods (renumbered from STAT 4003)) | ||

INEG 2333 Applied Probability and Statistics for Engineers II | ||

Year Total: | 16 | 15 |

Third Year | Units | |

Fall | Spring | |

PHIL 3103 Ethics and the Professions (Satisfies General Education Outcome 5.1) | 3 | |

DASC 3103 Cloud Computing and Big Data | 3 | |

STAT 4373 Experimental Design | 3 | |

State Minimum Core Natural Science Elective with Lab (Satisfies General Education Outcome 3.4) | 4 | |

State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.2 and 3.3)^{4} | 3 | |

DASC 3203 Optimization Methods in Data Science | 3 | |

DASC 3213 Statistical Learning | 3 | |

STAT 4333 Analysis of Categorical Responses | 3 | |

ECON 2143 Basic Economics: Theory and Practice (Satisfies General Education Outcome 3.3) | 3 | |

State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.3 and 4.1)^{5} | 3 | |

Year Total: | 16 | 15 |

Fourth Year | Units | |

Fall | Spring | |

DASC 4892 Data Science Practicum I | 2 | |

DASC 4113 Machine Learning | 3 | |

DASC 4123 Social Problems in Data Science and Analytics | 3 | |

STAT 4013 Statistical Forecasting and Prediction (Statistical Forecasting and Prediction) | 3 | |

Concentration Elective | 3 | |

DASC 4993 Data Science Practicum II (Satisfies General Education Outcome 6.1) | 3 | |

Concentration Elective | 3 | |

General Elective | 3 | |

Concentration Elective | 3 | |

Year Total: | 14 | 12 |

Total Units in Sequence: | 120 |

^{1} | |

^{2} | Data Science Statistics Concentration students are advised to select STAT 3013/STAT 3003 in order to meet prerequisites required in the concentration. |

^{3} | |

^{4} | |

^{5} |

### Requirements for B.S. in Data Science with Geospatial Data Analytics Concentration

Below are the general requirements for a Bachelor of Science degree with a major in Data Science, followed by specific requirements for the Geospatial Data Analytics Concentration. Below those is a recommended eight-semester plan to achieve those requirements in a timely fashion.

**Requirements for B.S. in Data Science**

State Minimum Core and General Education (36 hours) | ||

ENGL 1013 | Composition I (ACTS Equivalency = ENGL 1013) | 3 |

ENGL 1033 | Technical Composition II (ACTS Equivalency = ENGL 1023) | 3 |

MATH 2554 | Calculus I (ACTS Equivalency = MATH 2405) | 4 |

Science state minimum electives (two courses with labs) | 8 | |

Fine Arts state minimum core | 3 | |

Humanities state minimum core | ||

PHIL 3103 | Ethics and the Professions | 3 |

U.S. History and Government state minimum core | 3 | |

History of the American People to 1877 (ACTS Equivalency = HIST 2113) | ||

or HIST 2013 | History of the American People, 1877 to Present (ACTS Equivalency = HIST 2123) | |

or PLSC 2003 | American National Government (ACTS Equivalency = PLSC 2003) | |

Social Science state minimum core electives | 6 | |

ECON 2143 | 3 | |

Data Science Required Core (47 hours) | ||

DASC 1001 | Introduction to Data Science (First-Year Program - Introduction to Data Science) | 1 |

DASC 1104 | Programming Languages for Data Science (Programming Languages for Data Science (R, Python)) | 4 |

DASC 1204 | 4 | |

DASC 2594 | Multivariable Math for Data Scientists (Multivariable Math for Data Scientists) | 4 |

DASC 1222 | Role of Data Science in Today's World (Role of Data Science in Today's World) | 2 |

DASC 2103 | Data Structures & Algorithms (Data Structures & Algorithms) | 3 |

DASC 2113 | Principles and Techniques of Data Science (Principles & Techniques of Data Science) | 3 |

DASC 2203 | Data Management and Data Base (Data Management & Data Base) | 3 |

DASC 2213 | Data Visualization and Communication (Data Visualization & Communication (Tableau)) | 3 |

DASC 3103 | Cloud Computing and Big Data (Cloud Computing & Big Data) | 3 |

DASC 3203 | Optimization Methods in Data Science (Optimization Methods in Data Science) | 3 |

DASC 3213 | Statistical Learning (Statistical Learning) | 3 |

DASC 4892 | Data Science Practicum I (Data Science Practicum I) | 2 |

DASC 4113 | Machine Learning (Machine Learning) | 3 |

DASC 4123 | Social Problems in Data Science and Analytics (Social Problems (Issues) in DASC & Analytics) | 3 |

DASC 4993 | Data Science Practicum II (Data Science Practicum II) | 3 |

Data Science Required Additional Courses | ||

MATH 2564 | Calculus II (ACTS Equivalency = MATH 2505) | 4 |

SEVI 2053 | Business Foundations | 3 |

Choose from one of these two-course sequences | 6 | |

Applied Probability and Statistics for Engineers I and Applied Probability and Statistics for Engineers II (Applied Probability and Statistics for Engineers II) | ||

Or | ||

Introduction to Probability and Statistical Methods (Statistical Methods) | ||

Data Science Concentration Courses | 20-21 | |

General Electives | 3-4 | |

Total Hours | 120 |

### Required Geospatial Data Analytics Concentration Courses

GEOS 3543 | Geospatial Applications and Information Science | 3 |

GEOS 3553 | Spatial Analysis Using ArcGIS | 3 |

GEOS 3563 | Geospatial Data Mining | 3 |

GEOS 3593 | Introduction to Geodatabases | 3 |

GEOS 4263 | Geospatial Data Science - Sources and Characteristics | 3 |

GEOS 4653 | GIS Analysis and Modeling | 3 |

Elective Geospatial Data Analytics Concentration Courses (Select 3 hours) | 3 | |

Introduction to Cartography | ||

Principles of Remote Sensing | ||

Radar Remote Sensing | ||

Advanced Cartographic Techniques & Production | ||

Introduction to Raster GIS | ||

Introduction to Global Positioning Systems and Global Navigation Satellite Systems | ||

Total Hours | 21 |

### Data Science B.S. with Geospatial Data Analytics Concentration

Eight-Semester Program

First Year | Units | |
---|---|---|

Fall | Spring | |

MATH 2554 Calculus I (ACTS Equivalency = MATH 2405) (Satisfies General Education Outcome 2.1)^{1} | 4 | |

ENGL 1013 Composition I (ACTS Equivalency = ENGL 1013) (Satisfies General Education Outcome 1.1) | 3 | |

State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.2 and 3.3)^{2} | 3 | |

DASC 1001 Introduction to Data Science | 1 | |

DASC 1104 Programming Languages for Data Science | 4 | |

MATH 2564 Calculus II (ACTS Equivalency = MATH 2505) | 4 | |

DASC 1204 Introduction to Object Oriented Programming for Data Science | 4 | |

DASC 1222 Role of Data Science in Today's World | 2 | |

State Minimum Core Natural Science Elective with Lab (Satisfies General Education Outcome 3.4) | 4 | |

3 | ||

Year Total: | 15 | 17 |

Second Year | Units | |

Fall | Spring | |

DASC 2594 Multivariable Math for Data Scientists | 4 | |

DASC 2113 Principles and Techniques of Data Science | 3 | |

GEOS 3543 Geospatial Applications and Information Science | 3 | |

GEOS 3563 Geospatial Data Mining | 3 | |

DASC 2213 Data Visualization and Communication | 3 | |

DASC 2203 Data Management and Data Base | 3 | |

INEG 2313 Applied Probability and Statistics for Engineers I or STAT 3013 Introduction to Probability | 3 | |

DASC 2103 Data Structures & Algorithms | 3 | |

SEVI 2053 Business Foundations | 3 | |

State Minimum Core U.S. History or Government Elective (Satisfies General Education Outcome 4.2) | 3 | |

Year Total: | 16 | 15 |

Third Year | Units | |

Fall | Spring | |

PHIL 3103 Ethics and the Professions (Satisfies General Education Outcome 5.1) | 3 | |

DASC 3103 Cloud Computing and Big Data | 3 | |

INEG 2333 Applied Probability and Statistics for Engineers II or STAT 3003 Statistical Methods | 3 | |

GEOS 3553 Spatial Analysis Using ArcGIS | 3 | |

GEOS 3593 Introduction to Geodatabases | 3 | |

DASC 3203 Optimization Methods in Data Science | 3 | |

DASC 3213 Statistical Learning | 3 | |

ECON 2143 Basic Economics: Theory and Practice (Satisfies General Education Outcome 3.3) | 3 | |

Geospatial Data Analytics Elective | 3 | |

State Minimum Core Natural Science Elective with Lab (Satisfies General Education Outcome 3.4) | 4 | |

Year Total: | 15 | 16 |

Fourth Year | Units | |

Fall | Spring | |

DASC 4892 Data Science Practicum I | 2 | |

DASC 4113 Machine Learning | 3 | |

DASC 4123 Social Problems in Data Science and Analytics | 3 | |

GEOS 4653 GIS Analysis and Modeling | 3 | |

State Minimum Core Fine Arts Elective (Satisfies General Education Outcome 3.1)^{3} | 3 | |

DASC 4993 Data Science Practicum II (Satisfies General Education Outcome 6.1) | 3 | |

General Education Elective | 3 | |

State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.3 and 4.1)^{4} | 3 | |

GEOS 4263 Geospatial Data Science - Sources and Characteristics | 3 | |

Year Total: | 14 | 12 |

Total Units in Sequence: | 120 |

^{1} | |

^{2} | |

^{3} | |

^{4} |

### Requirements for B.S. in Data Science with Operations Analytics Concentration

Below are the general requirements for a Bachelor of Science degree with a major in Data Science, followed by specific requirements for the Operations Analytics Concentration. Below those is a recommended eight-semester plan to achieve those requirements in a timely fashion.

**Requirements for B.S. in Data Science**

State Minimum Core and General Education (36 hours) | ||

ENGL 1013 | Composition I (ACTS Equivalency = ENGL 1013) | 3 |

ENGL 1033 | Technical Composition II (ACTS Equivalency = ENGL 1023) | 3 |

MATH 2554 | Calculus I (ACTS Equivalency = MATH 2405) | 4 |

Science state minimum electives (two courses with labs) | 8 | |

Fine Arts state minimum core | 3 | |

Humanities state minimum core | ||

PHIL 3103 | Ethics and the Professions | 3 |

U.S. History and Government state minimum core | 3 | |

History of the American People to 1877 (ACTS Equivalency = HIST 2113) | ||

or HIST 2013 | History of the American People, 1877 to Present (ACTS Equivalency = HIST 2123) | |

or PLSC 2003 | American National Government (ACTS Equivalency = PLSC 2003) | |

Social Science state minimum core electives | 6 | |

ECON 2143 | 3 | |

Data Science Required Core (47 hours) | ||

DASC 1001 | Introduction to Data Science (First-Year Program - Introduction to Data Science) | 1 |

DASC 1104 | Programming Languages for Data Science (Programming Languages for Data Science (R, Python)) | 4 |

DASC 1204 | 4 | |

DASC 2594 | Multivariable Math for Data Scientists (Multivariable Math for Data Scientists) | 4 |

DASC 1222 | Role of Data Science in Today's World (Role of Data Science in Today's World) | 2 |

DASC 2103 | Data Structures & Algorithms (Data Structures & Algorithms) | 3 |

DASC 2113 | Principles and Techniques of Data Science (Principles & Techniques of Data Science) | 3 |

DASC 2203 | Data Management and Data Base (Data Management & Data Base) | 3 |

DASC 2213 | Data Visualization and Communication (Data Visualization & Communication (Tableau)) | 3 |

DASC 3103 | Cloud Computing and Big Data (Cloud Computing & Big Data) | 3 |

DASC 3203 | Optimization Methods in Data Science (Optimization Methods in Data Science) | 3 |

DASC 3213 | Statistical Learning (Statistical Learning) | 3 |

DASC 4892 | Data Science Practicum I (Data Science Practicum I) | 2 |

DASC 4113 | Machine Learning (Machine Learning) | 3 |

DASC 4123 | Social Problems in Data Science and Analytics (Social Problems (Issues) in DASC & Analytics) | 3 |

DASC 4993 | Data Science Practicum II (Data Science Practicum II) | 3 |

Data Science Required Additional Courses | ||

MATH 2564 | Calculus II (ACTS Equivalency = MATH 2505) | 4 |

SEVI 2053 | Business Foundations | 3 |

Choose from one of these two-course sequences | 6 | |

Applied Probability and Statistics for Engineers I and Applied Probability and Statistics for Engineers II (Applied Probability and Statistics for Engineers II) | ||

Or | ||

Introduction to Probability and Statistical Methods (Statistical Methods) | ||

Data Science Concentration Courses | 20-21 | |

General Electives | 3-4 | |

Total Hours | 120 |

### Required Operations Analytics Concentration Courses

INEG 2413 | Engineering Economic Analysis | 3 |

INEG 3613 | Introduction to Operations Research | 3 |

INEG 3623 | Simulation | 3 |

INEG 4553 | Production Planning and Control | 3 |

Elective Operations Analtyics Concentration Courses | 9 | |

Select 6 hours from the following: | ||

Productivity Improvement | ||

Facility Logistics | ||

Transportation Logistics | ||

Decision Support in Industrial Engineering | ||

Any Supply Chain Management (SCMT) course at the 2000 level or higher from the Supply Chain Analytics Concentration | ||

Select 3 hours from the following: | ||

Global Engineering and Innovation | ||

Systems Engineering and Management | ||

Project Management | ||

Total Hours | 21 |

### Data Science B.S. with Operations Analytics Concentration

Eight-Semester Program

First Year | Units | |
---|---|---|

Fall | Spring | |

MATH 2554 Calculus I (ACTS Equivalency = MATH 2405) (Satisfies General Education Outcome 2.1)^{1} | 4 | |

ENGL 1013 Composition I (ACTS Equivalency = ENGL 1013) (Satisfies General Education Outcome 1.1) | 3 | |

State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.2 and 3.3)^{2} | 3 | |

DASC 1001 Introduction to Data Science | 1 | |

DASC 1104 Programming Languages for Data Science | 4 | |

MATH 2564 Calculus II (ACTS Equivalency = MATH 2505) | 4 | |

DASC 1204 Introduction to Object Oriented Programming for Data Science | 4 | |

DASC 1222 Role of Data Science in Today's World | 2 | |

State Minimum Core Natural Science Elective with Lab (Satisfies General Education Outcome 3.4) | 4 | |

3 | ||

Year Total: | 15 | 17 |

Second Year | Units | |

Fall | Spring | |

DASC 2594 Multivariable Math for Data Scientists | 4 | |

DASC 2113 Principles and Techniques of Data Science | 3 | |

INEG 2413 Engineering Economic Analysis | 3 | |

DASC 2213 Data Visualization and Communication | 3 | |

DASC 2203 Data Management and Data Base | 3 | |

INEG 2313 Applied Probability and Statistics for Engineers I or STAT 3013 Introduction to Probability | 3 | |

DASC 2103 Data Structures & Algorithms | 3 | |

SEVI 2053 Business Foundations | 3 | |

State Minimum Core U.S. History or Government Elective (Satisfies General Education Outcome 4.2) | 3 | |

Year Total: | 13 | 15 |

Third Year | Units | |

Fall | Spring | |

PHIL 3103 Ethics and the Professions (Satisfies General Education Outcome 5.1) | 3 | |

DASC 3103 Cloud Computing and Big Data | 3 | |

INEG 2333 Applied Probability and Statistics for Engineers II or STAT 3003 Statistical Methods | 3 | |

INEG 3613 Introduction to Operations Research | 3 | |

INEG 3623 Simulation | 3 | |

DASC 3203 Optimization Methods in Data Science | 3 | |

DASC 3213 Statistical Learning | 3 | |

INEG 4553 Production Planning and Control | 3 | |

ECON 2143 Basic Economics: Theory and Practice (Satisfies General Education Outcome 3.3) | 3 | |

State Minimum Core Natural Science Elective with Lab (Satisfies General Education Outcome 3.4) | 4 | |

Year Total: | 15 | 16 |

Fourth Year | Units | |

Fall | Spring | |

DASC 4892 Data Science Practicum I | 2 | |

DASC 4113 Machine Learning | 3 | |

DASC 4123 Social Problems in Data Science and Analytics | 3 | |

Operations Analytics Elective^{5} | 3 | |

State Minimum Core Fine Arts Elective (Satisfies General Education Outcome 3.1)^{3} | 3 | |

DASC 4993 Data Science Practicum II (Satisfies General Education Outcome 6.1) | 3 | |

General Education Elective^{5} | 3 | |

State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.3 and 4.1)^{4} | 3 | |

Operations Analytics Elective^{5} | 6 | |

Year Total: | 14 | 15 |

Total Units in Sequence: | 120 |

^{1} | |

^{2} | |

^{3} | |

^{4} | |

^{5} |

### Requirements for B.S. in Data Science with Supply Chain Analytics Concentration

Below are the general requirements for a Bachelor of Science degree with a major in Data Science, followed by specific requirements for the Supply Chain Analytics Concentration. Below those is a recommended eight-semester plan to achieve those requirements in a timely fashion.

**Requirements for B.S. in Data Science**

State Minimum Core and General Education (36 hours) | ||

ENGL 1013 | Composition I (ACTS Equivalency = ENGL 1013) | 3 |

ENGL 1033 | Technical Composition II (ACTS Equivalency = ENGL 1023) | 3 |

MATH 2554 | Calculus I (ACTS Equivalency = MATH 2405) | 4 |

Science state minimum electives (two courses with labs) | 8 | |

Fine Arts state minimum core | 3 | |

Humanities state minimum core | ||

PHIL 3103 | Ethics and the Professions | 3 |

U.S. History and Government state minimum core | 3 | |

History of the American People to 1877 (ACTS Equivalency = HIST 2113) | ||

or HIST 2013 | History of the American People, 1877 to Present (ACTS Equivalency = HIST 2123) | |

or PLSC 2003 | American National Government (ACTS Equivalency = PLSC 2003) | |

Social Science state minimum core electives | 6 | |

ECON 2143 | 3 | |

Data Science Required Core (47 hours) | ||

DASC 1001 | Introduction to Data Science (First-Year Program - Introduction to Data Science) | 1 |

DASC 1104 | Programming Languages for Data Science (Programming Languages for Data Science (R, Python)) | 4 |

DASC 1204 | 4 | |

DASC 2594 | Multivariable Math for Data Scientists (Multivariable Math for Data Scientists) | 4 |

DASC 1222 | Role of Data Science in Today's World (Role of Data Science in Today's World) | 2 |

DASC 2103 | Data Structures & Algorithms (Data Structures & Algorithms) | 3 |

DASC 2113 | Principles and Techniques of Data Science (Principles & Techniques of Data Science) | 3 |

DASC 2203 | Data Management and Data Base (Data Management & Data Base) | 3 |

DASC 2213 | Data Visualization and Communication (Data Visualization & Communication (Tableau)) | 3 |

DASC 3103 | Cloud Computing and Big Data (Cloud Computing & Big Data) | 3 |

DASC 3203 | Optimization Methods in Data Science (Optimization Methods in Data Science) | 3 |

DASC 3213 | Statistical Learning (Statistical Learning) | 3 |

DASC 4892 | Data Science Practicum I (Data Science Practicum I) | 2 |

DASC 4113 | Machine Learning (Machine Learning) | 3 |

DASC 4123 | Social Problems in Data Science and Analytics (Social Problems (Issues) in DASC & Analytics) | 3 |

DASC 4993 | Data Science Practicum II (Data Science Practicum II) | 3 |

Data Science Required Additional Courses | ||

MATH 2564 | Calculus II (ACTS Equivalency = MATH 2505) | 4 |

SEVI 2053 | Business Foundations | 3 |

Choose from one of these two-course sequences | 6 | |

Applied Probability and Statistics for Engineers I and Applied Probability and Statistics for Engineers II (Applied Probability and Statistics for Engineers II) | ||

Or | ||

Introduction to Probability and Statistical Methods (Statistical Methods) | ||

Data Science Concentration Courses | 20-21 | |

General Electives | 3-4 | |

Total Hours | 120 |

### Required Supply Chain Analytics Concentration Courses

SCMT 2103 | Integrated Supply Chain Management | 3 |

SCMT 3443 | DELIVER: Transportation and Distribution Management | 3 |

SCMT 3613 | SOURCE: Procurement and Supply Management | 3 |

SCMT 3623 | PLAN: Inventory and Forecasting Analytics | 3 |

SCMT 3643 | International Logistics | 3 |

SCMT 4653 | Supply Chain Strategy and Change Management | 3 |

Elective Supply Chain Analytics Concentration (Select 3 hours) | 3 | |

Supply Chain Service and Customer Management | ||

Project Management: Supply Chain New Product Planning and Launch | ||

Sustainable Logistics and Supply Chain Management | ||

Special Topics in Supply Chain Management | ||

Supply Chain Performance Management and Analytics | ||

Any Industrial Engineering (INEG) course at the 3000 level or higher from the Operations Analytics Concentration | ||

Total Hours | 21 |

### Data Science B.S. with Supply Chain Analytics Concentration

Eight-Semester Program

First Year | Units | |
---|---|---|

Fall | Spring | |

MATH 2554 Calculus I (ACTS Equivalency = MATH 2405) (Satisifies General Education Outcome 2.1)^{1} | 4 | |

ENGL 1013 Composition I (ACTS Equivalency = ENGL 1013) (Satisifies General Education Outcome 1.1) | 3 | |

DASC 1001 Introduction to Data Science | 1 | |

DASC 1104 Programming Languages for Data Science | 4 | |

ECON 2143 Basic Economics: Theory and Practice (Satisifies General Education Outcome 3.3) | 3 | |

MATH 2564 Calculus II (ACTS Equivalency = MATH 2505) | 4 | |

DASC 1204 Introduction to Object Oriented Programming for Data Science | 4 | |

DASC 1222 Role of Data Science in Today's World | 2 | |

ACCT 2013 Accounting Principles^{To be completed as a General Education Elective for prerequisite purposes} | 3 | |

ENGL 1033 Technical Composition II (ACTS Equivalency = ENGL 1023) (Satisifies General Education Outcome 1.2) | 3 | |

Year Total: | 15 | 16 |

Second Year | Units | |

Fall | Spring | |

DASC 2594 Multivariable Math for Data Scientists | 4 | |

DASC 2113 Principles and Techniques of Data Science | 3 | |

SCMT 2103 Integrated Supply Chain Management | 3 | |

State Minimum Core U.S. History or Government Elective (Satisifies General Education Outcome 4.2) | 3 | |

DASC 2213 Data Visualization and Communication | 3 | |

DASC 2203 Data Management and Data Base | 3 | |

INEG 2313 Applied Probability and Statistics for Engineers I or STAT 3013 Introduction to Probability | 3 | |

DASC 2103 Data Structures & Algorithms | 3 | |

SEVI 2053 Business Foundations | 3 | |

SCMT 3443 DELIVER: Transportation and Distribution Management | 3 | |

Year Total: | 16 | 15 |

Third Year | Units | |

Fall | Spring | |

PHIL 3103 Ethics and the Professions (Satisifies General Education Outcome 5.1) | 3 | |

DASC 3103 Cloud Computing and Big Data | 3 | |

INEG 2333 Applied Probability and Statistics for Engineers II or STAT 3003 Statistical Methods | 3 | |

SCMT 3613 SOURCE: Procurement and Supply Management | 3 | |

SCMT 3623 PLAN: Inventory and Forecasting Analytics | 3 | |

DASC 3203 Optimization Methods in Data Science | 3 | |

DASC 3213 Statistical Learning | 3 | |

SCMT 3643 International Logistics | 3 | |

SCMT 4653 Supply Chain Strategy and Change Management | 3 | |

State Minimum Core Natural Science with Lab Elective (Satisifies General Education Outcome 3.4) | 4 | |

Year Total: | 15 | 16 |

Fourth Year | Units | |

Fall | Spring | |

DASC 4892 Data Science Practicum I | 2 | |

DASC 4113 Machine Learning | 3 | |

DASC 4123 Social Problems in Data Science and Analytics | 3 | |

Supply Chain Analytics Elective | 3 | |

State Minimum Core Social Sciences Elective (Satisifies General Education Outcomes 3.2 and 3.3)^{2} | 3 | |

DASC 4993 Data Science Practicum II (Satisifies General Education Outcome 6.1) | 3 | |

State Minimum Core Natural Science with Lab Elective (Satisifies General Education Outcome 3.4) | 4 | |

State Minimum Core Social Sciences Elective (Satisifies General Education Outcomes 3.3 and 4.1)^{3} | 3 | |

State Minimum Core Fine Arts Elective (Satisifies General Education Outcome 3.1)^{4} | 3 | |

Year Total: | 14 | 13 |

Total Units in Sequence: | 120 |

^{1} | |

^{2} | |

^{3} | |

^{4} |

### Faculty

**Barrett, David A.,** Ph.D., M.A. (University of Arkansas), B.A. (Hendrix College), Instructor, Department of Philosophy, 2006.**Cronan, Timothy P.,** Ph.D. (Louisiana Tech University), M.S. (South Dakota State University), B.S. (University of Southwestern Louisiana), Professor, Department of Information Systems, M.D. Matthews Endowed Chair in Information Systems, 1979.**Dennis, Norman D.,** Ph.D. (University of Texas at Austin), M.B.A. (Boston University), M.S.C.E., B.S.C.E. (Missouri University of Science and Technology), University Professor, Department of Civil Engineering, 1996, 2011.**Fugate, Brian,** Ph.D., M.B.A., B.S. (University of Tennessee), Professor, Department of Supply Chain Management, Oren Harris Chair in Transportation, 2015, 2018.**Gauch, John Michael,** Ph.D. (University of North Carolina at Chapel Hill), M.Sc., B.Sc. (Queen’s University, Canada), Professor, Department of Computer Science and Computer Engineering, 2008.**Harris, Casey Taggart,** Ph.D., M.A. (Pennsylvania State University), B.S. (Texas A&M University), Associate Professor, Department of Sociology and Criminology, 2011, 2017.**Keiffer, Elizabeth,** Ph.D., M.A. (University of Arkansas), B.S. (East Central University), Teaching Assistant Professor, Department of Information Systems, 2016, 2019.**Liu, Xiao,** Ph.D. (National University of Singapore), B.S.M.E. (Harbin Institute of Technology, China), Assistant Professor, Department of Industrial Engineering, 2017.**Nakarmi, Ukash,** Ph.D. (University at Buffalo), M.S. (Oklahoma State University), Assistant Professor, Department of Computer Science and Computer Engineering, 2020.**Nolan, Steve,** Ph.D., M.A. (University of Missouri-Columbia), B.A. (Westminster College), Instructor, Department of Information Systems, 2017.**Rao, Raj R.,** Ph.D. (University of Georgia), M.S. (University of Texas), M.Sc., B.E. (Birla Institute of Technology and Sciences, India), Professor, Department of Biomedical Engineering, 2016.**Ridge, Jason,** Ph.D., M.A., B.A. (Oklahoma State University), Associate Professor, Department of Strategic, Entrepreneurship and Venture Innovation, 2015, 2017.**Schubert, Karl,** Ph.D. (University of Arkansas), M.S.Ch.E. (University of Kentucky), B.S.Ch.E (University of Arkansas), Professor of Practice, Department of Industrial Engineering, 2016.**Sullivan, Kelly M.,** Ph.D. (University of Florida), M.S.I.E., B.S.I.E. (University of Arkansas), Associate Professor, Department of Industrial Engineering, 2012, 2019.**Zhan, Justin,** Ph.D. (University of Ottawa, Canada), M.S. (Syracuse University), Professor, Department of Computer Science and Computer Engineering, 2019.

### Courses

**DASC 1001. Introduction to Data Science. 1 Hour.**

Introduction to Data Science is a course providing an overview of Data Science and preparation of Data Science First Year students for the Data Science program and for choosing one of the Data Science program concentrations. Corequisite: Drill component and MATH 2554. Prerequisite: Students must be a DTSCBS or DTSCFR major. (Typically offered: Fall)

**DASC 1104. Programming Languages for Data Science. 4 Hours.**

Programming Languages for Data Science provides a semester-long introduction to basic concepts, tools, and languages for computer programming using Python and R, two powerful programming languages used by data scientists. This class will introduce students to computer programming and provide them with the basic skills and tools necessary to efficiently collect, process, analyze, and visualize datasets. Students will gain hands-on experience with de novo programming in R and Python, finding and utilizing packages, and working in both interactive (Jupyter and RStudio) and non-interactive (Unix) environments. Corequisite: Lab component. Prerequisite: Students must be a DTSCBS or DTSCFR major. (Typically offered: Fall)

**DASC 1204. Introduction to Object Oriented Programming for Data Science. 4 Hours.**

Introduction to Object Oriented Programming for Data Science, introduces object-oriented programming in JAVA. It covers object-oriented programming elements and techniques in JAVA, such as primitive types and expressions, basic I/O, basic programming structures, abstract data type, object class and instance, Methods, Java File I/O, object inheritance, collections and composite objects, advanced input /output: streams and files, and exception handling. Students will gain hands-on programming experience using JAVA. Corequisite: Lab component. Prerequisite: DASC 1104 and must be a DTSCBS or DTSCFR major. (Typically offered: Spring)

**DASC 1222. Role of Data Science in Today's World. 2 Hours.**

Role of Data Science in Today's World is a survey course providing an overview of the Data Science Curriculum and an introduction to the essential elements of data science: data collection and management; summarizing and visualizing data; basic ideas of statistical inference; predictive analytics and machine learning. Students will continue their hands-on experience using the Python and R programming languages and Jupyter notebooks.Prerequisite: DASC 1104 and must be a DTSCBS or DTSCFR major. (Typically offered: Spring)

**DASC 188V. Special Topics in Data Science. 1-6 Hour.**

Special Topics in Data Science is a course for data science topics not covered in other courses. Corequisite: Lab component. Prerequisite: Students must be a DTSCBS or DTSCFR major and Instructor Permission Only. (Typically offered: Fall, Spring and Summer) May be repeated for up to 9 hours of degree credit.

**DASC 2103. Data Structures & Algorithms. 3 Hours.**

Data Structures & Algorithms focuses on fundamental data structures and associated algorithms for computing and data analytics. Topics include the study of data structures such as linked lists, stacks, queues, hash tables, trees, and graphs, recursion, their applications to algorithms such as searching, sorting, tree and graph traversals, divide-and-conquer, greedy algorithms, and dynamic programming, and the theory of NP-completeness. Students will gain hands-on experience using Python or Java. Corequisite: Lab component. Prerequisite: DASC 1204 and must be a DTSCBS major. (Typically offered: Spring)

**DASC 2113. Principles and Techniques of Data Science. 3 Hours.**

Principles and Techniques in Data Science is an intermediate semester-long data science course that follows an overview of data science in today's world. This class bridges between introduction to data science and upper division data science courses as well as methods courses in other concentrations. This class equips students with essential basic elements of data science, ranging from database systems, data acquisition, storage and query, data cleansing, data wrangling, basic data summarization and visualization, and data estimation and modeling. Students will gain hands-on experience using Python and various packages in Python. Corequisite: Lab component. Prerequisite: MATH 2564 and student must be a DTSCBS major. (Typically offered: Fall)

**DASC 2203. Data Management and Data Base. 3 Hours.**

Data Management and Data Base focuses on the investigation and application of data science database concepts including DBMS fundamentals, database technology and administration, data modeling, SQL, data warehousing, and current topics in modern database management. Corequisite: Lab component. Prerequisite: DASC 1204 and students must be a DTSCBS major. (Typically offered: Spring)

**DASC 2213. Data Visualization and Communication. 3 Hours.**

Data Visualization and Communication is a seminar providing an essential element of data science: the ability to effectively communicate data analytics findings using visual, written, and oral forms. Students will gain hands-on experience using data visualization software and preparing multiple formats of written reports (technical, social media, policy) that build a data literacy and communication toolkit for interdisciplinary work. In essence, this is a course emphasizing finding and telling stories from data, including the fundamental principles of data analysis and visual presentation conjoined with traditional written formats. Corequisite: Lab component. Prerequisite: DASC 1104 and DASC 1222 and students must be a DTSCBS major. (Typically offered: Fall)

**DASC 2594. Multivariable Math for Data Scientists. 4 Hours.**

Multivariable Mathematics for Data Scientists provides an in depth look at the multivariate calculus and linear algebra necessary for a successful understanding of modeling for data science. Students will gain an understanding of the mathematical and geometric concepts used in optimization and scientific computation using mathematical and computational techniques. At the end of the course, students will be equipped with the calculus and linear algebra skills and knowledge to be successful in courses in optimization and advanced data science methods. Corequisite: Lab component. Prerequisite: MATH 2564 and DASC 1104 and student must be a DTSCBS major. (Typically offered: Fall)

**DASC 3103. Cloud Computing and Big Data. 3 Hours.**

Cloud Computing and Big Data covers: introduction to distributed data computing and management, MapReduce, Hadoop, cloud computing, NoSQL and NewSQL systems, Big data analytics and scalable machine learning, real-time streaming data analysis. Students will gain hands-on experience using Amazon AWS, MongoDB, Hive, and Spark. Corequisite: Lab component. Prerequisite: DASC 2594 and DASC 2203 and student must be a DTSCBS major. (Typically offered: Fall)

**DASC 3203. Optimization Methods in Data Science. 3 Hours.**

Optimization Methods in Data Science is an advanced mathematical course providing the foundations and concepts of optimization that are essential elements of machine learning algorithms in data science, ranging from mathematical optimization to convex optimization to unconstrained and constrained optimization to nonlinear optimization to stochastic optimization. Students will gain hands-on experience using Python and various optimization packages in Python. Corequisite: Lab component. Prerequisite: DASC 2113 and DASC 2594 and student must be a DTSCBS major. (Typically offered: Spring)

**DASC 3213. Statistical Learning. 3 Hours.**

Statistical Learning is a course providing an in depth look at the theory and practice of applied linear modeling for data science: including model building, selection, regularization, classification and prediction. Students will gain hands-on experience using statistical software to learn from data using applied linear models. Corequisite: Lab component. Prerequisite: DASC 1104 and ((MATH 3013 and STAT 3003) or (INEG 2313 and INEG 2333)) and student must be a DTSCBS major. (Typically offered: Spring)

**DASC 390V. Special Topics in Data Science. 1-6 Hour.**

Special Topics in Data Science is a course for data science topics not covered in other courses. Prerequisite: Student must be a DTSCBS or DTSCFR major and by Permission Only. (Typically offered: Irregular) May be repeated for up to 9 hours of degree credit.

**DASC 4113. Machine Learning. 3 Hours.**

Machine learning covers: logistic regression, ensemble methods, support vector machines, kernel methods, neural networks, Bayesian inference, reinforcement learning, learning theory, and their applications in text, image, and web data processing. Students will gain hands-on experience of developing machine learning algorithms using Python and scikit-learn. Corequisite: Lab component. Prerequisite: DASC 2103 and DASC 3203 and student must be a DTSCBS major. (Typically offered: Fall)

**DASC 4123. Social Problems in Data Science and Analytics. 3 Hours.**

This course explores the ways data analytics and data science are impacted by or intersect with issues of social justice, poverty and economic inequality, racial and ethnic relations, gender, crime, education, health and healthcare, and other contemporary social problems. Corequisite: Lab component. Prerequisite: DASC 1222 and student must be a DTSCBS major. (Typically offered: Fall)

**DASC 4533. Information Retrieval. 3 Hours.**

Information Retrieval is a course providing expertise in processing unstructured data as a key component of data science. It covers text processing, file structures, ranking algorithms, query processing, and web search. Students will gain hands-on experience developing their own search engine from scratch, using Python, C, C++, or Java on a Linux server and making their search engine web accessible. Note: Prior user-level knowledge of Linux for file and directory management and remote login is required for this course. Corequisite: Lab component. Prerequisite: DASC 2103 and student must be a DTSCBS major. (Typically offered: Fall and Spring)

**DASC 4892. Data Science Practicum I. 2 Hours.**

Application of data science, analytics, business intelligence, data mining, machine learning, and data visualization to existing problems. Data Science techniques using current and relevant software and problem-solving methods are applied to current problems for presentation to management. This is the first semester of the required full-year multi-college interdisciplinary practicum using real-world data to solve real-world problems. Corequisite: Lab component, DASC 3213, DASC 4113 and DASC 4123. Prerequisite: DASC 2113, DASC 2213 and DASC 3203 and student must be a DTSCBS major. (Typically offered: Fall)

**DASC 4892H. Honors Data Science Practicum I. 2 Hours.**

Application of data science, analytics, business intelligence, data mining, machine learning, and data visualization to existing problems. Data Science techniques using current and relevant software and problem-solving methods are applied to current problems for presentation to management. This is the first semester of the required full-year multi-college interdisciplinary practicum using real-world data to solve real-world problems. Corequisite: Lab component, DASC 3213, DASC 4113 and DASC 4123. Prerequisite: DASC 2113, DASC 2213 and DASC 3203 and honors standing and student must be a DTSCBS major. (Typically offered: Fall)

**DASC 4993. Data Science Practicum II. 3 Hours.**

Application of data science, analytics, business intelligence, data mining, machine learning, and data visualization to existing problems. Data Science techniques using current and relevant software and problem-solving methods are applied to current problems for presentation to management. This is the second semester of the required full-year multi-college interdisciplinary practicum using real-world data to solve real-world problems. Corequisite: Lab component. Prerequisite: DASC 4892 with a grade of C or better and Data Science (DTSC) majors only. (Typically offered: Spring)

**DASC 4993H. Honors Data Science Practicum II. 3 Hours.**

Application of data science, analytics, business intelligence, data mining, machine learning, and data visualization to existing problems. Data Science techniques using current and relevant software and problem-solving methods are applied to current problems for presentation to management. This is the second semester of the required full-year multi-college interdisciplinary practicum using real-world data to solve real-world problems. Corequisite: Lab component. Prerequisite: DASC 4892 with a grade of C or better, Data Science (DTSC) majors only and honors standing. (Typically offered: Spring)