Data Science B.S. with Bioinformatics Concentration
Data Science B.S. with Bioinformatics Concentration
Eight-Semester ProgramÌý
| First Year | Units | |
|---|---|---|
| Fall | Spring | |
| ²Ñ´¡°Õ±áÌý24004 Calculus I (ACTS Equivalency = MATH 2405) (Satisfies General Education Outcome 2.1)1 | 4 | Ìý |
| DASCÌý10003 Introduction to Data Science | 3 | Ìý |
| ENGLÌý10103 Composition I (ACTS Equivalency = ENGL 1013) (Satisfies General Education Outcome 1.1) | 3 | Ìý |
| DASCÌý11004 Programming Languages for Data Science | 4 | Ìý |
| MATHÌý25004 Calculus II | Ìý | 4 |
| Satisfies General Education Outcome 3.4: | ||
| BIOLÌý10103 Principles of Biology (ACTS Equivalency = BIOL 1014 Lecture) & BIOLÌý10101 Principles of Biology Laboratory (ACTS Equivalency = BIOL 1014 Lab) | Ìý | 4 |
| 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 | 18 |
| Ìý | ||
| Second Year | Units | |
| Fall | Spring | |
| DASCÌý25904 Multivariable Math for Data Scientists | 4 | Ìý |
| Satisfies General Education Outcome 3.4: | ||
| CHEMÌý14103 ÉÁ²¥¸£Àû¿â Chemistry I (ACTS Equivalency = CHEM 1414 Lecture) & CHEMÌý14101 ÉÁ²¥¸£Àû¿â Chemistry I Laboratory (ACTS Equivalency = CHEM 1414 Lab) | 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 |
| SEVIÌý20503 Business Foundations (Data Science Majors-only section) | Ìý | 3 |
| STATÌý30043 Statistical Methods4 or INEGÌý23104 Statistics for Industrial Engineers I | Ìý | 3-4 |
| DASCÌý22003 Data Management and Data Base | Ìý | 3 |
| BIOLÌý23373 General Genetics | Ìý | 3 |
| Year Total: | 17 | 12 |
| 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 |
| ECONÌý21403 Basic Economics: Theory and Practice (Satisfies General Education Outcome 3.3) | 3 | Ìý |
| BIOLÌý25473 Cell Biology | 3 | Ìý |
| State Minimum Core U.S. History or Government Elective (Satisfies General Education Outcome 4.2)2 | 3 | Ìý |
| DASCÌý32003 Optimization Methods in Data Science | Ìý | 3 |
| DASCÌý32103 Statistical Learning | Ìý | 3 |
| State Minimum Core Fine Arts Elective (Satisfies General Education Outcome 3.1) | Ìý | 3 |
| State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.2 and 3.3)2 | Ìý | 3 |
| State Minimum Core Social Sciences Elective (Satisfies General Education Outcomes 3.3 and 4.1)2 | Ìý | 3 |
| Year Total: | 15 | 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 |
| BIOLÌý30473 Evolutionary Biology or BIOLÌý38773 General Ecology | 3 | Ìý |
| Bioinformatics Elective | 3 | Ìý |
| DASCÌý49903 Data Science Practicum II (Satisfies General Education Outcome 6.1) | Ìý | 3 |
| Bioinformatics Elective | Ìý | 3 |
| Bioinformatics Elective | Ìý | 3 |
| Bioinformatics Elective | Ìý | 3 |
| General Education Elective3 | Ìý | 2-3 |
| Year Total: | 14 | 15 |
| Ìý | ||
| Total Units in Sequence: | Ìý | 120 |
- 1
Students have demonstrated successful completion of the learning indicators identified for learning outcome 2.1, by meeting the prerequisites for ²Ñ´¡°Õ±áÌý24004.
- 2
Students must complete theÌýÌýState 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
- 3
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.
- 4
Data Science Statistics and Computational Analytics Concentration students are advised to select STATÌý30133/STATÌý30043 to meet the prerequisites required in the concentration.