Data Science B.S. with Computational Analytics Concentration
Data Science B.S. with Computational Analytics Concentration
Eight-Semester ProgramÌý
| First Year | Units | |
|---|---|---|
| Fall | Spring | |
| ²Ñ´¡°Õ±áÌý24004 Calculus I (ACTS Equivalency = MATH 2405) (Satisfies General Education Outcome 2.1)2 | 4 | Ìý |
| ENGLÌý10103 Composition I (ACTS Equivalency = ENGL 1013) (Satisfies General Education Outcome 1.1) | 3 | Ìý |
| DASCÌý10003 Introduction to Data Science | 3 | Ìý |
| DASCÌý11004 Programming Languages for Data Science | 4 | Ìý |
| MATHÌý25004 Calculus II | Ìý | 4 |
| ECONÌý21403 Basic Economics: Theory and Practice (Satisfies General Education Outcome 3.3) | Ìý | 3 |
| ENGLÌý10303 Technical Composition II (ACTS Equivalency = ENGL 1023) (Satisfies General Education Outcome 1.2) | Ìý | 3 |
| DASCÌý12004 Introduction to Object Oriented Programming for Data Science | Ìý | 4 |
| DASCÌý12203 Role of Data Science in Today's World | Ìý | 3 |
| Year Total: | 14 | 17 |
| Ìý | ||
| Second Year | Units | |
| Fall | Spring | |
| DASCÌý25904 Multivariable Math for Data Scientists | 4 | Ìý |
| STATÌý30133 Introduction to Probability4 or INEGÌý23203 Probability and Stochastic Processes for Industrial Engineers | 3 | Ìý |
| DASCÌý22103 Data Visualization and Communication | 3 | Ìý |
| DASCÌý21103 Principles and Techniques of Data Science | 3 | Ìý |
| State Minimum Core U.S. History or Government Elective (Satisfies General Education Outcome 4.2)2 | 3 | Ìý |
| SEVIÌý20503 Business Foundations (Data Science Majors-only section) | Ìý | 3 |
| STATÌý30043 Statistical Methods5 or INEGÌý23104 Statistics for Industrial Engineers I | Ìý | 3-4 |
| DASCÌý22003 Data Management and Data Base | Ìý | 3 |
| DASCÌý21003 Data Structures & Algorithms | Ìý | 3 |
| State Minimum Core Natural Science Elective with Lab (Satisfies General Education Outcome 3.4) | Ìý | 4 |
| Year Total: | 16 | 16 |
| Ìý | ||
| Third Year | Units | |
| Fall | Spring | |
| DASCÌý21303 Data Privacy & Ethics (Satisfies General Education Outcome 5.1) | 3 | Ìý |
| DASCÌý31003 Big Data Analytics with Cloud Computing | 3 | Ìý |
| CSCEÌý41403 Data Mining | 3 | Ìý |
| State Minimum Core Natural Science Elective with Lab (Satisfies General Education Outcome 3.4) | 4 | Ìý |
| State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.2 and 3.3)3 | 3 | Ìý |
| DASCÌý32003 Optimization Methods in Data Science | Ìý | 3 |
| DASCÌý32103 Statistical Learning | Ìý | 3 |
| CSCEÌý46103 Artificial Intelligence | Ìý | 3 |
| State Minimum Core Fine Arts Elective (Satisfies General Education Outcome 3.1)3 | Ìý | 3 |
| State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.3 and 4.1)3 | Ìý | 3 |
| Year Total: | 16 | 15 |
| Ìý | ||
| Fourth Year | Units | |
| Fall | Spring | |
| DASCÌý48902 Data Science Practicum I | 2 | Ìý |
| DASCÌý41103 Machine Learning | 3 | Ìý |
| DASCÌý41203 Social Problems in Data Science and Analytics | 3 | Ìý |
| Computational Analytics Elective | 3 | Ìý |
| Computational Analytics Elective | 3 | Ìý |
| DASCÌý49903 Data Science Practicum II (Satisfies General Education Outcome 6.1) | Ìý | 3 |
| Computational Analytics Elective | Ìý | 3 |
| Computational Analytics Elective | Ìý | 3 |
| General Education Elective4 | Ìý | 2-3 |
| Year Total: | 14 | 12 |
| Ìý | ||
| Total Units in Sequence: | Ìý | 120 |
- 1
MATHÌý26103 or MATHÌý28003 is a pre-req for CSCEÌý41303ÌýandÌýCSCEÌý43203.
- 2
Students have demonstrated successful completion of the learning indicators identified for learning outcome 2.1, by meeting the prerequisites for ²Ñ´¡°Õ±áÌý24004.
- 3
Students must complete theÌýState Minimum Core requirementsÌýas outlined in the Catalog of Studies. The courses that meet the state minimum core also fulfill many of the university'sÌýGeneral 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.Ìý
- 4
Students are required to complete 40 hours of upper-division courses (3000-4000 level).Ìý It is recommended that students consult with their adviser when making course selections.
- 5
Data Science Statistics and Computational Analytics Concentration students are advised to selectÌýSTATÌý30133/STATÌý30043Ìýto meet the prerequisites required in the concentration.
- 6
CSCEÌý31903 is a pre-req for CSCEÌý41203, CSCEÌý43203, CSCEÌý45203, CSCEÌý47503, CSCEÌý48103.
- 7
Pre-req for CSCEÌý41203 is CSCEÌý31903 or CSCEÌý319H3 with a grade of C or better.