Manuel Rossetti
Director
Bell Engineering 4164
479-575-6756
Email: rossetti@uark.edu
Karl D. Schubert
Associate Director
J.B. Hunt Center 111
479-575-2264
Email: karl.schubert@uark.edu
Lee Shoultz
Project/Program Specialist
J.B. Hunt Center 110
479-575-5469
Email: eshoultz@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
- Cybersecurity 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 | 6-7 | |
Introduction to Probability and Statistical Methods (Statistical Methods) | ||
Or | ||
Statistics for Industrial Engineers I and Probability and Stochastic Processes for Industrial Engineers | ||
Data Science Concentration Courses | 20-21 | |
General Electives | 2-4 | |
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 | ||
ERP Fundamentals | ||
Introduction to Marketing | ||
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 | |
State Minimum Core Natural Science Elective with Lab (Satisfies General Education Outcome 3.4) | 4 | |
ENGL 1013 Composition I (ACTS Equivalency = ENGL 1013) (Satisfies General Education Outcome 1.1) | 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 | |
ECON 2143 Basic Economics: Theory and Practice (Satisfies General Education Outcome 3.3) | 3 | |
ENGL 1033 Technical Composition II (ACTS Equivalency = ENGL 1023) (Satisfies General Education Outcome 1.2) | 3 | |
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 | 16 |
Second Year | Units | |
Fall | Spring | |
DASC 2594 Multivariable Math for Data Scientists | 4 | |
DASC 2113 Principles and Techniques of Data Science | 3 | |
DASC 2213 Data Visualization and Communication | 3 | |
STAT 3013 Introduction to Probability^{4} or INEG 2314 Statistics for Industrial Engineers I | 3-4 | |
State Minimum Core Fine Arts Elective (Satisfies General Education Outcome 3.1)^{2} | 3 | |
SEVI 2053 Business Foundations (Data Science Majors-only section) | 3 | |
STAT 3003 Statistical Methods^{4} or INEG 2323 Probability and Stochastic Processes for Industrial Engineers | 3 | |
DASC 2103 Data Structures & Algorithms | 3 | |
DASC 2203 Data Management and Data Base | 3 | |
ACCT 2013 Accounting Principles (This is a Concentration pre-req and uses the General Elective credit hours) | 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 2023 Accounting Principles II | 3 | |
State Minimum Core Natural Science with Lab Elective (Satisfies General Education Outcome 3.4) | 4 | |
State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.2 and 3.3)^{2} | 3 | |
DASC 3203 Optimization Methods in Data Science | 3 | |
DASC 3213 Statistical Learning | 3 | |
ACCT 3533 Accounting Technology | 3 | |
State Minimum Core U.S. History or Government Elective (Satisfies General Education Outcome 4.2)^{2} | 3 | |
State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.3 and 4.1)^{2} | 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 | |
ACCT 3543 Accounting Analytics | 3 | |
ISYS 4193 Business Analytics and Visualization | 3 | |
DASC 4993 Data Science Practicum II (Satisfies General Education Outcome 6.1) | 3 | |
ISYS 4293 Business Intelligence | 3 | |
Accounting Analytics Concentration Elective | 3 | |
General Education Elective^{3} | 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} | Students must complete the State Minimum Core requirements as outlined in the Catalog of Studies. The courses that meet the state minimum core also fulfill many of the university's General Education requirements, although there are additional considerations to satisfy the general education learning outcomes. Students are encouraged to consult with their academic adviser when making course selections. |
^{3} | 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. |
^{4} | Data Science Statistics and Computational Analytics Concentration students are advised to select STAT 3013/STAT 3003 to meet the prerequisites required in the concentration. |
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 | 6-7 | |
Introduction to Probability and Statistical Methods (Statistical Methods) | ||
Or | ||
Statistics for Industrial Engineers I and Probability and Stochastic Processes for Industrial Engineers | ||
Data Science Concentration Courses | 20-21 | |
General Electives | 2-4 | |
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 | |
BIOL 1543 Principles of Biology (ACTS Equivalency = BIOL 1014 Lecture) & BIOL 1541L Principles of Biology Laboratory (ACTS Equivalency = BIOL 1014 Lab) | 4 | |
ENGL 1013 Composition I (ACTS Equivalency = ENGL 1013) (Satisfies General Education Outcome 1.1) | 3 | |
Satisfies General Education Outcome 3.4: | ||
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 | |
ENGL 1033 Technical Composition II (ACTS Equivalency = ENGL 1023) (Satisfies General Education Outcome 1.2) | 3 | |
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 | |
STAT 3013 Introduction to Probability^{4} or INEG 2314 Statistics for Industrial Engineers I | 3-4 | |
DASC 2213 Data Visualization and Communication | 3 | |
DASC 2113 Principles and Techniques of Data Science | 3 | |
State Minimum Core Fine Arts Elective (Satisfies General Education Outcome 3.1)^{2} | 3 | |
SEVI 2053 Business Foundations (Data Science Majors-only section) | 3 | |
STAT 3003 Statistical Methods^{4} or INEG 2323 Probability and Stochastic Processes for Industrial Engineers | 3 | |
DASC 2103 Data Structures & Algorithms | 3 | |
DASC 2203 Data Management and Data Base | 3 | |
BIOL 2323 General Genetics | 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 | |
BIOL 2533 Cell Biology | 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)^{2} | 3 | |
DASC 3203 Optimization Methods in Data Science | 3 | |
DASC 3213 Statistical Learning | 3 | |
BIOL 3023 Evolutionary Biology or BIOL 3863 General Ecology | 3 | |
State Minimum Core U.S. History or Government Elective (Satisfies General Education Outcome 4.2)^{2} | 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 | |
Bioinformatics Elective | 3 | |
DASC 4993 Data Science Practicum II (Satisfies General Education Outcome 6.1) | 3 | |
Bioinformatics Elective | 3 | |
Bioinformatics Elective | 3 | |
General Education Elective^{3} | 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} | Students must complete the State Minimum Core requirements as outlined in the Catalog of Studies. The courses that meet the state minimum core also fulfill many of the university's General Education requirements, although there are additional considerations to satisfy the general education learning outcomes. Students are encouraged to consult with their academic adviser when making course selections. |
^{3} | 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. |
^{4} | Data Science Statistics and Computational Analytics Concentration students are advised to select STAT 3013/STAT 3003 to meet the prerequisites required in the concentration. |
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 | 6-7 | |
Introduction to Probability and Statistical Methods (Statistical Methods) | ||
Or | ||
Statistics for Industrial Engineers I and Probability and Stochastic Processes for Industrial Engineers | ||
Data Science Concentration Courses | 20-21 | |
General Electives | 2-4 | |
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 | |
CHEM 1103 University Chemistry I (ACTS Equivalency = CHEM 1414 Lecture) & CHEM 1101L University Chemistry I Laboratory (ACTS Equivalency = CHEM 1414 Lab) | 4 | |
ENGL 1013 Composition I (ACTS Equivalency = ENGL 1013) (Satisfies General Education Outcome 1.1) | 3 | |
Satisfies General Education Outcome 3.4: | ||
DASC 1001 Introduction to Data Science | 1 | |
DASC 1104 Programming Languages for Data Science | 4 | |
MATH 2564 Calculus II (ACTS Equivalency = MATH 2505) | 4 | |
PHYS 2054 University Physics I (ACTS Equivalency = PHYS 2034) (Satisfies General Education Outcome 3.4) | 4 | |
ENGL 1033 Technical Composition II (ACTS Equivalency = ENGL 1023) (Satisfies General Education Outcome 1.2) | 3 | |
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 | |
STAT 3013 Introduction to Probability^{4} or INEG 2314 Statistics for Industrial Engineers I | 3-4 | |
DASC 2213 Data Visualization and Communication | 3 | |
DASC 2113 Principles and Techniques of Data Science | 3 | |
BMEG 2614 Introduction to Biomedical Engineering | 4 | |
SEVI 2053 Business Foundations (Data Science Majors-only section) | 3 | |
STAT 3003 Statistical Methods^{4} or INEG 2323 Probability and Stochastic Processes for Industrial Engineers | 3 | |
DASC 2103 Data Structures & Algorithms | 3 | |
DASC 2203 Data Management and Data Base | 3 | |
CHEM 1123 University Chemistry II (ACTS Equivalency = CHEM 1424 Lecture) | 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 | |
ECON 2143 Basic Economics: Theory and Practice (Satisfies General Education Outcome 3.3) | 3 | |
State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.2 and 3.3)^{2} | 3 | |
DASC 3203 Optimization Methods in Data Science | 3 | |
DASC 3213 Statistical Learning | 3 | |
BMEG 3801 Clinical Observations and Needs Finding | 1 | |
State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.3 and 4.1) ^{2} | 3 | |
State Minimum Core Fine Arts Elective (Satisfies General Outcome 3.1)^{2} | 3 | |
Year Total: | 15 | 13 |
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 | |
Concentration Elective Course | 1 | |
Concentration Elective Course | 3 | |
DASC 4993 Data Science Practicum II (Satisfies General Education Outcome 6.1) | 3 | |
Concentration Elective Course | 3 | |
Concentration Elective Course | 3 | |
State Minimum Core U.S. History or Government Elective (Satisfies General Education Outcome 4.2)^{2} | 3 | |
General Elective Course^{3} | 2-3 | |
Year Total: | 12 | 15 |
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} | Students must complete the State Minimum Core requirements as outlined in the Catalog of Studies. The courses that meet the state minimum core also fulfill many of the university's General Education requirements, although there are additional considerations to satisfy the general education learning outcomes. Students are encouraged to consult with their academic adviser when making course selections. |
^{3} | 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. |
^{4} | Data Science Statistics and Computational Analytics Concentration students are advised to select STAT 3013/STAT 3003 to meet the prerequisites required in the concentration. |
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
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 | 6-7 | |
Introduction to Probability and Statistical Methods (Statistical Methods) | ||
Or | ||
Statistics for Industrial Engineers I and Probability and Stochastic Processes for Industrial Engineers | ||
Data Science Concentration Courses | 20-21 | |
General Electives | 2-4 | |
Total Hours | 120 |
Required Business Data Analytics Concentration Courses
ACCT 2013 | Accounting Principles | 3 |
ACCT 2023 | Accounting Principles II | 3 |
ISYS 4193 | Business Analytics and Visualization | 3 |
ISYS 4293 | Business Intelligence | 3 |
Elective Business Data Analytics Concentration Courses (Select 9 hours) | 9 | |
Introduction to Econometrics | ||
Forecasting | ||
Financial Analysis | ||
Principles of Finance | ||
ERP Fundamentals | ||
Introduction to Marketing | ||
Marketing Research | ||
Total Hours | 21 |
Data Science B.S. with Business 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 | |
State Minimum Core Natural Science Elective with Lab (Satisfies General Education Outcome 3.4) | 4 | |
ENGL 1013 Composition I (ACTS Equivalency = ENGL 1013) (Satisfies General Education Outcome 1.1) | 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 | |
ECON 2143 Basic Economics: Theory and Practice (Satisfies General Education Outcome 3.3) | 3 | |
ENGL 1033 Technical Composition II (ACTS Equivalency = ENGL 1023) (Satisfies General Education Outcome 1.2) | 3 | |
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 | 16 |
Second Year | Units | |
Fall | Spring | |
DASC 2594 Multivariable Math for Data Scientists | 4 | |
STAT 3013 Introduction to Probability^{4} or INEG 2314 Statistics for Industrial Engineers I | 3-4 | |
DASC 2213 Data Visualization and Communication | 3 | |
DASC 2113 Principles and Techniques of Data Science | 3 | |
State Minimum Core Fine Arts Elective (Satisfies General Education Outcome 3.1)^{2} | 3 | |
SEVI 2053 Business Foundations (Data Science Majors-only section) | 3 | |
STAT 3003 Statistical Methods^{4} or INEG 2323 Probability and Stochastic Processes for Industrial Engineers | 3 | |
DASC 2103 Data Structures & Algorithms | 3 | |
DASC 2203 Data Management and Data Base | 3 | |
ACCT 2013 Accounting Principles | 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 | |
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)^{2} | 3 | |
DASC 3203 Optimization Methods in Data Science | 3 | |
DASC 3213 Statistical Learning | 3 | |
ACCT 2023 Accounting Principles II | 3 | |
State Minimum Core U.S. History or Government Elective (Satisfies General Education Outcome 4.2)^{2} | 3 | |
State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.3 and 4.1)^{2} | 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 | |
Business Data Analytics Electives | 6 | |
DASC 4993 Data Science Practicum II (Satisfies General Education Outcome 6.1) | 3 | |
ISYS 4293 Business Intelligence | 3 | |
Business Data Analytics Elective | 3 | |
General Education Elective^{3} | 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} | Students must complete the State Minimum Core requirements as outlined in the Catalog of Studies. The courses that meet the state minimum core also fulfill many of the university's General Education requirements, although there are additional considerations to satisfy the general education learning outcomes. Students are encouraged to consult with their academic adviser when making course selections. |
^{3} | 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. |
^{4} | Data Science Statistics and Computational Analytics Concentration students are advised to select STAT 3013/STAT 3003 to meet the prerequisites required in the concentration. |
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
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 | 6-7 | |
Introduction to Probability and Statistical Methods (Statistical Methods) | ||
Or | ||
Statistics for Industrial Engineers I and Probability and Stochastic Processes for Industrial Engineers | ||
Data Science Concentration Courses | 20-21 | |
General Electives | 2-4 | |
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 | |
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 | |
State Minimum Core Natural Science Elective with Lab (Satisfies General Education Outcome 3.4) | 4 | |
ENGL 1013 Composition I (ACTS Equivalency = ENGL 1013) (Satisfies General Education Outcome 1.1) | 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 | |
ECON 2143 Basic Economics: Theory and Practice (Satisfies General Education Outcome 3.3) | 3 | |
ENGL 1033 Technical Composition II (ACTS Equivalency = ENGL 1023) (Satisfies General Education Outcome 1.2) | 3 | |
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 | 16 |
Second Year | Units | |
Fall | Spring | |
DASC 2594 Multivariable Math for Data Scientists | 4 | |
STAT 3013 Introduction to Probability^{4} or INEG 2314 Statistics for Industrial Engineers I | 3-4 | |
DASC 2213 Data Visualization and Communication | 3 | |
DASC 2113 Principles and Techniques of Data Science | 3 | |
State Minimum Core Fine Arts Elective (Satisfies General Education Outcome 3.1)^{2} | 3 | |
SEVI 2053 Business Foundations (Data Science Majors-only section) | 3 | |
STAT 3003 Statistical Methods^{4} or INEG 2333 Applied Probability and Statistics for Engineers II | 3 | |
DASC 2103 Data Structures & Algorithms | 3 | |
DASC 2203 Data Management and Data Base | 3 | |
CSCE 3513 Software Engineering | 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 | |
CSCE 4143 Data Mining | 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^{2} | 3 | |
DASC 3203 Optimization Methods in Data Science | 3 | |
DASC 3213 Statistical Learning | 3 | |
CSCE 4613 Artificial Intelligence | 3 | |
State Minimum Core U.S. History or Government Elective (Satisfies General Education Outcome 4.2)^{2} | 3 | |
State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.3 and 4.1)^{2} | 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 | |
Computational Analytics Elective | 3 | |
Computational Analytics Elective | 3 | |
DASC 4993 Data Science Practicum II (Satisfies General Education Outcome 6.1) | 3 | |
Computational Analytics Elective | 3 | |
Computational Analytics Elective | 3 | |
General Education Elective^{3} | 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} | Students must complete the State Minimum Core requirements as outlined in the Catalog of Studies. The courses that meet the state minimum core also fulfill many of the university's General Education requirements, although there are additional considerations to satisfy the general education learning outcomes. Students are encouraged to consult with their academic adviser when making course selections. |
^{3} | 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. |
^{4} | Data Science Statistics and Computational Analytics Concentration students are advised to select STAT 3013/STAT 3003 to meet the prerequisites required in the concentration. |
Requirements for B.S. with Cybersecurity 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 Cybersecurity 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
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 | 6-7 | |
Introduction to Probability and Statistical Methods (Statistical Methods) | ||
Or | ||
Statistics for Industrial Engineers I and Probability and Stochastic Processes for Industrial Engineers | ||
Data Science Concentration Courses | 20-21 | |
General Electives | 2-4 | |
Total Hours | 120 |
Required Cybersecurity Data Analytics Concentration Courses
Required Courses: | 15 | |
Accounting Principles | ||
or ACCT 2023 | Accounting Principles II | |
Cyber Crime and Cyber Terrorism (Cyber Crime and Cyber Terrorism) | ||
Principles of Data and Cybersecurity | ||
Network and Data Security in a Changing World | ||
Cybersecurity, Crime and Data Privacy Law Fundamentals | ||
Elective Cybersecurity and Data Concentration Courses (Choose 2 of the following): | 6 | |
Advanced Information Security Management | ||
Advanced Cybersecurity, Crime and Privacy Law | ||
Blockchain Fundamentals | ||
Total Hours | 21 |
Data Science B.S. with Cybersecurity Data Analytics Concentration Eight-Semester Plan
First Year | Units | |
---|---|---|
Fall | Spring | |
MATH 2554 Calculus I (ACTS Equivalency = MATH 2405) (Satisfies General Education Outcome 2.1)^{1} | 4 | |
State Minimum Core Natural Science Elective with Lab (Satisfies General Education Outcome 3.4) | 4 | |
ENGL 1013 Composition I (ACTS Equivalency = ENGL 1013) (Satisfies General Education Outcome 1.1) | 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 | |
ECON 2143 Basic Economics: Theory and Practice (Satisfies General Education Outcome 3.3) | 3 | |
ENGL 1033 Technical Composition II (ACTS Equivalency = ENGL 1023) | 3 | |
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 | 16 |
Second Year | Units | |
Fall | Spring | |
DASC 2594 Multivariable Math for Data Scientists | 4 | |
STAT 3013 Introduction to Probability^{4} or INEG 2314 Statistics for Industrial Engineers I | 3-4 | |
DASC 2213 Data Visualization and Communication | 3 | |
DASC 2113 Principles and Techniques of Data Science | 3 | |
State Minimum Core Fine Arts Elective (Satisfies General Education Outcome 3.1)^{2} | 3 | |
SEVI 2053 Business Foundations (Data Science Majors-only section) | 3 | |
STAT 3003 Statistical Methods^{4} or INEG 2323 Probability and Stochastic Processes for Industrial Engineers | 3 | |
DASC 2103 Data Structures & Algorithms | 3 | |
DASC 2203 Data Management and Data Base | 3 | |
ACCT 2013 Accounting Principles or ACCT 2023 Accounting Principles II | 3 | |
Year Total: | 16 | 15 |
Third Year | Units | |
Fall | Spring | |
PHIL 3103 Ethics and the Professions | 3 | |
DASC 3103 Cloud Computing and Big Data | 3 | |
DASC 3223 Cyber Crime and Cyber Terrorism (Cyber Crime and Cyber Terrorism) | 3 | |
State Minimum Core Natural Science with Lab (Satisfies General Education Outcome 3.4) | 4 | |
State Minimum Core Social Sciences Elective (General Education Outcomes 3.2 and 3.3)^{2} | 3 | |
DASC 3203 Optimization Methods in Data Science | 3 | |
DASC 3213 Statistical Learning | 3 | |
ISYS 4013 Principles of Data and Cybersecurity | 3 | |
State Minimum Core U.S. History or Government Elective (Satisfies General Education Outcome 4.2)^{2} | 3 | |
State Minimum Core Social Sciences Elective (Satisfied General Education Outcomes 3.3 and 4.1)^{2} | 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 | |
ISYS 4023 Network and Data Security in a Changing World | 3 | |
ISYS 4043 Cybersecurity, Crime and Data Privacy Law Fundamentals | 3 | |
DASC 4993 Data Science Practicum II | 3 | |
Concentration Elective | 3 | |
Concentration Elective | 3 | |
General Education Elective^{3} | 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} | Students must complete the State Minimum Core requirements as outlined in the Catalog of Studies. The courses that meet the state minimum core also fulfill many of the university's General Education requirements, although there are additional considerations to satisfy the general education learning outcomes. Students are encouraged to consult with their academic adviser when making course selections. |
^{3} | 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. |
^{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
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 | 6-7 | |
Introduction to Probability and Statistical Methods (Statistical Methods) | ||
Or | ||
Statistics for Industrial Engineers I and Probability and Stochastic Processes for Industrial Engineers | ||
Data Science Concentration Courses | 20-21 | |
General Electives | 2-4 | |
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 Data Science 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 | |
State Minimum Core Natural Science Elective with Lab (Satisfies General Education Outcome 3.4) | 4 | |
ENGL 1013 Composition I (ACTS Equivalency = ENGL 1013) (Satisfies General Education Outcome 1.1) | 3 | |
DASC 1104 Programming Languages for Data Science | 4 | |
DASC 1001 Introduction to Data Science | 1 | |
MATH 2564 Calculus II (ACTS Equivalency = MATH 2505) | 4 | |
ECON 2143 Basic Economics: Theory and Practice (Satisfies General Education Outcome 3.3) | 3 | |
ENGL 1033 Technical Composition II (ACTS Equivalency = ENGL 1023) (Satisfies General Education Outcome 1.2) | 3 | |
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 | 16 |
Second Year | Units | |
Fall | Spring | |
DASC 2594 Multivariable Math for Data Scientists | 4 | |
STAT 3013 Introduction to Probability^{4} or INEG 2314 Statistics for Industrial Engineers I | 3-4 | |
DASC 2213 Data Visualization and Communication | 3 | |
DASC 2113 Principles and Techniques of Data Science | 3 | |
State Minimum Core Fine Arts Elective (Satisfies General Education Outcome 3.1)^{2} | 3 | |
SEVI 2053 Business Foundations (Data Science Majors-only section) | 3 | |
STAT 3003 Statistical Methods^{4} or INEG 2323 Probability and Stochastic Processes for Industrial Engineers | 3 | |
DASC 2103 Data Structures & Algorithms | 3 | |
DASC 2203 Data Management and Data Base | 3 | |
STAT 3113 Introduction to Mathematical Statistics | 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 | |
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)^{2} | 3 | |
DASC 3203 Optimization Methods in Data Science | 3 | |
DASC 3213 Statistical Learning | 3 | |
STAT 4333 Analysis of Categorical Responses | 3 | |
State Minimum Core U.S. History or Government Elective (Satisfies General Education Outcome 4.2)^{2} | 3 | |
State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.3 and 4.1)^{2} | 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 | |
Data Science Statistics Concentration Elective | 3 | |
DASC 4993 Data Science Practicum II (Satisfies General Education Outcome 6.1) | 3 | |
Data Science Statistics Concentration Elective | 3 | |
Data Science Statistics Concentration Elective | 3 | |
General Elective^{3} | 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} | Students must complete the State Minimum Core requirements as outlined in the Catalog of Studies. The courses that meet the state minimum core also fulfill many of the university's General Education requirements, although there are additional considerations to satisfy the general education learning outcomes. Students are encouraged to consult with their academic adviser when making course selections. |
^{3} | 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. |
^{4} | Data Science Statistics and Computational Analytics Concentration students are advised to select STAT 3013/STAT 3003 to meet the prerequisites required in the concentration. |
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
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 | 6-7 | |
Introduction to Probability and Statistical Methods (Statistical Methods) | ||
Or | ||
Statistics for Industrial Engineers I and Probability and Stochastic Processes for Industrial Engineers | ||
Data Science Concentration Courses | 20-21 | |
General Electives | 2-4 | |
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 | |
State Minimum Core Natural Science Elective with Lab (Satisfies General Education Outcome 3.4) | 4 | |
ENGL 1013 Composition I (ACTS Equivalency = ENGL 1013) (Satisfies General Education Outcome 1.1) | 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 | |
ECON 2143 Basic Economics: Theory and Practice (Satisfies General Education Outcome 3.3) | 3 | |
ENGL 1033 Technical Composition II (ACTS Equivalency = ENGL 1023) (Satisfies General Education Outcome 1.2) | 3 | |
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 | 16 |
Second Year | Units | |
Fall | Spring | |
DASC 2594 Multivariable Math for Data Scientists | 4 | |
STAT 3013 Introduction to Probability^{4} or INEG 2314 Statistics for Industrial Engineers I | 3-4 | |
DASC 2213 Data Visualization and Communication | 3 | |
DASC 2113 Principles and Techniques of Data Science | 3 | |
State Minimum Core Fine Arts Elective (Satisfies General Education Outcome 3.1)^{2} | 3 | |
SEVI 2053 Business Foundations (Data Science Majors-only section) | 3 | |
STAT 3003 Statistical Methods^{4} or INEG 2323 Probability and Stochastic Processes for Industrial Engineers | 3 | |
DASC 2103 Data Structures & Algorithms | 3 | |
DASC 2203 Data Management and Data Base | 3 | |
GEOS 3543 Geospatial Applications and Information Science | 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 | |
GEOS 3553 Spatial Analysis Using ArcGIS | 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)2^{2} | 3 | |
DASC 3203 Optimization Methods in Data Science | 3 | |
DASC 3213 Statistical Learning | 3 | |
GEOS 3593 Introduction to Geodatabases | 3 | |
State Minimum Core U.S. History or Government Elective (Satisfies General Education Outcome 4.2)^{2} | 3 | |
State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.3 and 4.1)^{2} | 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 | |
GEOS 3563 Geospatial Data Mining | 3 | |
GEOS 4263 Geospatial Data Science - Sources and Characteristics | 3 | |
DASC 4993 Data Science Practicum II (Satisfies General Education Outcome 6.1) | 3 | |
GEOS 4653 GIS Analysis and Modeling | 3 | |
Geospatial Data Analytics Concentration Elective | 3 | |
General Elective^{3} | 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} | Students must complete the State Minimum Core requirements as outlined in the Catalog of Studies. The courses that meet the state minimum core also fulfill many of the university's General Education requirements, although there are additional considerations to satisfy the general education learning outcomes. Students are encouraged to consult with their academic adviser when making course selections. |
^{3} | 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. |
^{4} | Data Science Statistics and Computational Analytics Concentration students are advised to select STAT 3013/STAT 3003 to meet the prerequisites required in the concentration. |
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
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 | 6-7 | |
Introduction to Probability and Statistical Methods (Statistical Methods) | ||
Or | ||
Statistics for Industrial Engineers I and Probability and Stochastic Processes for Industrial Engineers | ||
Data Science Concentration Courses | 20-21 | |
General Electives | 2-4 | |
Total Hours | 120 |
Required Operations Analytics Concentration Courses
INEG 2413 | Engineering Economic Analysis | 3 |
INEG 2613 | Introduction to Operations Research | 3 |
INEG 3624 | Simulation | 4 |
INEG 3553 | 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 | ||
Integrated Supply Chain Management | ||
Select 3 hours from the following: | ||
Global Engineering and Innovation | ||
Systems Engineering and Management | ||
Project Management | ||
Total Hours | 22 |
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 | |
State Minimum Core Natural Science Elective with Lab (Satisfies General Education Outcome 3.4) | 4 | |
ENGL 1013 Composition I (ACTS Equivalency = ENGL 1013) (Satisfies General Education Outcome 1.1) | 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 | |
ECON 2143 Basic Economics: Theory and Practice (Satisfies General Education Outcome 3.3) | 3 | |
ENGL 1033 Technical Composition II (ACTS Equivalency = ENGL 1023) (Satisfies General Education Outcome 1.2) | 3 | |
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 | 16 |
Second Year | Units | |
Fall | Spring | |
DASC 2594 Multivariable Math for Data Scientists | 4 | |
STAT 3013 Introduction to Probability^{4} or INEG 2314 Statistics for Industrial Engineers I | 3-4 | |
DASC 2213 Data Visualization and Communication | 3 | |
DASC 2113 Principles and Techniques of Data Science | 3 | |
State Minimum Core Fine Arts Elective (Satisfies General Education Outcome 3.1)^{2} | 3 | |
SEVI 2053 Business Foundations (Data Science Majors-only section) | 3 | |
STAT 3003 Statistical Methods^{4} or INEG 2323 Probability and Stochastic Processes for Industrial Engineers | 3 | |
DASC 2103 Data Structures & Algorithms | 3 | |
DASC 2203 Data Management and Data Base | 3 | |
INEG 2413 Engineering Economic Analysis | 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 3624 Simulation | 4 | |
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)^{2} | 3 | |
DASC 3203 Optimization Methods in Data Science | 3 | |
DASC 3213 Statistical Learning | 3 | |
INEG 2613 Introduction to Operations Research | 3 | |
State Minimum Core U.S. History or Government Elective (Satisfies General Education Outcome 4.2)^{2} | 3 | |
State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.3 and 4.1)^{2} | 3 | |
Year Total: | 17 | 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 | |
INEG 4553 | 3 | |
Operations Data Analytics Concentration Elective | 3 | |
DASC 4993 Data Science Practicum II (Satisfies General Education Outcome 6.1) | 3 | |
Operations Data Analytics Concentration Elective | 3 | |
Operations Data Analytics Concentration Elective | 3 | |
General Education Elective^{3} | 2-3 | |
Year Total: | 14 | 12 |
Total Units in Sequence: | 121 |
^{1} | Students have demonstrated successful completion of the learning indicators identified for learning outcome 2.1, by meeting the prerequisites for MATH 2554. |
^{2} | Students must complete the State Minimum Core requirements as outlined in the Catalog of Studies. The courses that meet the state minimum core also fulfill many of the university's General Education requirements, although there are additional considerations to satisfy the general education learning outcomes. Students are encouraged to consult with their academic adviser when making course selections. |
^{3} | 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. |
^{4} | Data Science Statistics and Computational Analytics Concentration students are advised to select STAT 3013/STAT 3003 to meet the prerequisites required in the concentration. |
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
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 | 6-7 | |
Introduction to Probability and Statistical Methods (Statistical Methods) | ||
Or | ||
Statistics for Industrial Engineers I and Probability and Stochastic Processes for Industrial Engineers | ||
Data Science Concentration Courses | 20-21 | |
General Electives | 2-4 | |
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 | ||
Environmental, Social and Governance Strategies and Operations in Supply Chains | ||
Special Topics in Supply Chain Management | ||
Supply Chain Performance Management and Analytics | ||
Any Industrial Engineering (INEG) course at the 3000 level 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 | |
State Minimum Core Natural Science Elective with Lab (Satisfies General Education Outcome 3.4) | 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 | |
MATH 2564 Calculus II (ACTS Equivalency = MATH 2505) | 4 | |
ECON 2143 Basic Economics: Theory and Practice (Satisfies General Education Outcome 3.3) | 3 | |
ENGL 1033 Technical Composition II (ACTS Equivalency = ENGL 1023) (Satisifies General Education Outcome 1.2) | 3 | |
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 | 16 |
Second Year | Units | |
Fall | Spring | |
DASC 2594 Multivariable Math for Data Scientists | 4 | |
STAT 3013 Introduction to Probability^{4} or INEG 2314 Statistics for Industrial Engineers I | 3-4 | |
DASC 2213 Data Visualization and Communication | 3 | |
DASC 2113 Principles and Techniques of Data Science | 3 | |
State Minimum Core Fine Arts Elective (Satisfies General Education Outcome 3.1)^{2} | 3 | |
SEVI 2053 Business Foundations (Data Science Majors-only section) | 3 | |
STAT 3003 Statistical Methods^{4} or INEG 2323 Probability and Stochastic Processes for Industrial Engineers | 3 | |
DASC 2103 Data Structures & Algorithms | 3 | |
DASC 2203 Data Management and Data Base | 3 | |
ACCT 2013 Accounting Principles (This pre-req to SYDA Concentration courses uses the "General Elective" to allow a full 21 hours for Concentration courses) | 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 | |
SCMT 2103 Integrated Supply Chain Management | 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)^{2} | 3 | |
DASC 3203 Optimization Methods in Data Science | 3 | |
DASC 3213 Statistical Learning | 3 | |
SCMT 3443 DELIVER: Transportation and Distribution Management | 3 | |
State Minimum Core U.S. History or Government Elective (Satisfies General Education Outcome 4.2)^{2} | 3 | |
State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.3 and 4.1)^{2} | 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 | |
SCMT 3613 SOURCE: Procurement and Supply Management | 3 | |
SCMT 3623 PLAN: Inventory and Forecasting Analytics | 3 | |
DASC 4993 Data Science Practicum II (Satisifies General Education Outcome 6.1) | 3 | |
SCMT 3643 International Logistics | 3 | |
SCMT 4653 Supply Chain Strategy and Change Management | 3 | |
Supply Chain Analytics Concentration Elective^{3} | 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} | Students must complete the State Minimum Core requirements as outlined in the Catalog of Studies. The courses that meet the state minimum core also fulfill many of the university's General Education requirements, although there are additional considerations to satisfy the general education learning outcomes. Students are encouraged to consult with their academic adviser when making course selections. |
^{3} | 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. |
^{4} | Data Science Statistics and Computational Analytics Concentration students are advised to select STAT 3013/STAT 3003 to meet the prerequisites required in the concentration. |
Faculty
Alverson, Andrew James, Ph.D. (University of Texas at Austin), M.S. (Iowa State University), B.S. (Grand Valley State University), Associate Professor, Department of Biological Sciences, 2012, 2018.
Barrett, David A., Ph.D., M.A. (University of Arkansas), B.A. (Hendrix College), Instructor, Department of Philosophy, 2006.
Cothren, Jackson David, Ph.D., M.S. (The Ohio State University), B.S. (United States Air Force Academy), Professor, Department of Geosciences, 2004, 2020.
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.
Cummings, Michael, Ph.D. (University of Minnesota), J.D. and M.P.A. (Brigham Young University), B.S. (Utah Valley), Assistant Professor, Department of Strategic, Entrepreneurship and Venture Innovation, 2017.
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.
Gruenewald, Jeffrey A., Ph.D. (Michigan State University), Associate Professor, Department of Sociology and Criminology, 2019.
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.
Pohl, Edward A., Ph.D., M.S.R.E. (University of Arizona), M.S.S.E. (Air Force Institute of Technology), M.S.E.M. (University of Dayton), B.S.E.E. (Boston University), Professor, Department of Industrial Engineering, Twenty-First Century Professorship in Engineering, 2004, 2013.
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.
Richardson, Vernon J., Ph.D. (University of Illinois-Urbana-Champaign), M.B.A., B.S. (Brigham Young University), Distinguished Professor, Department of Accounting, G. William Glezen Jr. Endowed Chair in Accounting, 2005, 2016.
Ridge, Jason, Ph.D., M.A., B.A. (Oklahoma State University), Associate Professor, Department of Strategic, Entrepreneurship and Venture Innovation, 2015, 2017.
Robinson, Samantha, Ph.D., M.S., B.S. (University of Arkansas), Teaching Assistant Professor, Department of Mathematical Sciences, 2015.
Rossetti, Manuel D., Ph.D., P.E., M.S.I.E. (The Ohio State University), B.S.I.E. (University of Cincinnati), University Professor, Department of Industrial Engineering, 1999, 2022.
Samsonraj, Rebekah M., Ph.D. (Cornell University), B.S. (University of Colorado, Boulder), Assistant Professor, Department of Biomedical Engineering, 2020.
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.
Syler, Rhonda A., Ph.D. (Auburn University), M.B.A. (Columbus State University), M.S. (Kansas State University), B.S. (Middle Tennessee State University), Teaching Assistant Professor, Department of Information Systems, 2016.
Tipton, John, Ph.D., M.S., B.S., (Colorado State University), Assistant Professor, Department of Mathematical Sciences, 2017.
Wu, Xintao, Ph.D. (George Mason University), M.E. (Chinese Academy of Space Technology), B.S. (University of Science and Technology of China), Professor, Department of Computer Science and Computer Engineering, Charles D. Morgan/Acxiom Graduate Research Chair, 2014, 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: MATH 2554 or MATH 2445. Prerequisite: Students must be a DTSCBS or DTSCFR major. (Typically offered: Fall)
DASC 1011. Success in Data Science Studies. 1 Hour.
This course provides preparation for Data Science First Year students for the Data Science program and for learning about University campus resources for students. This course is focused on students who are not MATH 2554 Calculus I or MATH 2445 Calculus I with Review ready. Prerequisite: Students must be a Data Science 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 188VH. Honors 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, have honors standing and by instructor permission only. (Typically offered: Fall, Spring and Summer) May be repeated for up to 9 hours of degree credit.
This course is equivalent to DASC 188V.
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. 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. 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. 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. 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 290V. 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: 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 290VH. Honors 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: Honors standing and 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.
This course is equivalent to DASC 290V.
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. Prerequisite: DASC 2594 and DASC 2203 and student must be a DTSCBS major. (Typically offered: Fall)
DASC 3103H. Honors 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. Prerequisite: DASC 2594, DASC 2203, honors standing and student must be a DTSCBS major. (Typically offered: Fall)
This course is equivalent to DASC 3103.
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. 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. Prerequisite: DASC 1104 and ((MATH 3013 and STAT 3003) or (INEG 2314 and INEG 2323)) and student must be a DTSCBS major. (Typically offered: Spring)
DASC 3223. Cyber Crime and Cyber Terrorism. 3 Hours.
Cyber Crime and Cyber Terrorism (CCCT) is an overview of the study of cybercrime and cyber terrorism for students of data science, criminology, and law discussing crimes committed via Internet, ranging from various white-collar financial crimes to the spread of viruses, malicious code, stalking, bullying, and web-based exploitation. Criminological, social-psychological explanations will be examined and the investigative and legal strategies employed to combat cyber-crime and cyber terrorism will be discussed. Prerequisite: DASC 2113 and must be a DTSCBS major. (Typically offered: Fall)
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 390VH. Honors 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 have honors standing, be a DTSCBS or DTSCFR major and by permission only. (Typically offered: Irregular) May be repeated for up to 9 hours of degree credit.
This course is equivalent to DASC 390V.
DASC 400VH. Honors Thesis in Data Science. 1-3 Hour.
Honors Thesis in Data Science (DASC 400VH) is a course to develop an Honors Thesis in Data Science. The Honors Thesis can be an independent thesis or can be related to the Data Science Practicum I and II Courses Project. Prerequisite: Student must be a DTSCBS major, have honors standing, and by Permission Only. (Typically offered: Fall, Spring and Summer) May be repeated for up to 3 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. Prerequisite: DASC 2103 and DASC 3203 and student must be a DTSCBS major. (Typically offered: Fall)
DASC 4113H. Honors 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. Prerequisite: DASC 2103, DASC 3203, honors standing and student must be a DTSCBS major. (Typically offered: Fall)
This course is equivalent to DASC 4113.
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. 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: Irregular)
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. Prerequisite: DASC 2113, DASC 3203 and student must be a DTSCBS major. Pre- or Corequisite: DASC 4123. (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. Prerequisite: DASC 2113, DASC 3203, and honors standing and student must be a DTSCBS major. Pre- or corequisite: DASC 4123. (Typically offered: Fall)
DASC 490V. 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: Students must be a DTSCBS major and Instructor Permission Only. (Typically offered: Fall, Spring and Summer) May be repeated for up to 9 hours of degree credit.
DASC 490VH. Honors 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: Honors standing and students must be a DTSCBS major and Instructor Permission Only. (Typically offered: Fall, Spring and Summer) May be repeated for up to 9 hours of degree credit.
This course is equivalent to DASC 490V.
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 student must be a DTSCBS major. (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, and student must be a DTSCBS major, and have honors standing. (Typically offered: Spring)
This course is equivalent to DASC 4993.