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 Probability4 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 Methods4 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 Elective3 | 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 |
