Data Analytics is a new way of understanding complex systems by building computational models that are consistent with the observed data about those systems. It's used in applications such as understanding customers, making effective investment decisions, recommending shows on platforms like Netflix, detecting cyber-intrusions or financial fraud, and much more.
Program Requirements
The following are the requirements for the Data Analytics option within the Computing Major. This information is meant as a guide and is subject to change. The precise and up-to-date requirements for Computing degrees can be found online in the University's Academic Calendar. In case of discrepancies, please notify the School's Curriculum Coordinator.
A half-year/one-term course is worth 3.0 units, while a full-year/two-term course is worth 6.0 units.
1. Core
| Code | Title | Units |
|---|---|---|
| A. Complete the following: | ||
| CISC 121 | Introduction to Computing Science I | 3.0 |
| CISC 124 | Introduction to Computing Science II | 3.0 |
| B. Complete 3.0 units from the following: | 3.0 | |
| STAT 263 | Introduction to Statistics | |
| STAT 268 | Statistics and Probability I | |
| STAT 351 | Probability I | |
| STAT_Options | ||
| C. Complete the following: | ||
| CISC 203 | Discrete Mathematics for Computing II | 3.0 |
| CISC 204 | Logic for Computing Science | 3.0 |
| CISC 221 | Computer Architecture | 3.0 |
| CISC 223 | Software Specifications | 3.0 |
| CISC 235 | Data Structures | 3.0 |
| D. Complete 3.0 units from the following: | 3.0 | |
| CISC 322 | Software Architecture | |
| CISC 326 | Game Architecture | |
| E. Complete the following: | ||
| CISC 324 | Operating Systems | 3.0 |
| CISC 360 | Programming Paradigms | 3.0 |
| CISC 365 | Algorithms I | 3.0 |
| F. Complete the following: | ||
| CISC 497 | Social, Ethical and Legal Issues in Computing | 3.0 |
| G. Complete 3.0 units from the following: | 3.0 | |
| CISC 496 | Game Development Project | |
| CISC 499 | Advanced Undergraduate Project | |
| CISC 500 | Undergraduate Thesis | |
2. Option - Data Analytics
| Code | Title | Units |
|---|---|---|
| A. a) Complete the following: | ||
| CISC 271 | Linear Data Analysis | 3.00 |
| CISC 371 | Nonlinear Data Analysis | 3.00 |
| CISC 372 | Advanced Data Analytics | 3.00 |
| CISC 451 | Topics in Data Analytics | 3.00 |
| CISC 452 | Neural and Genetic Computing | 3.00 |
| B. Complete 3.0 units from the following: | 3.0 | |
| CISC, COCA, COGS, or SOFT at the 200-level or above | ||
3. Supporting
| Code | Title | Units |
|---|---|---|
| A. Complete 6.0 units from the following: | 6.0 | |
| CISC 102 & MATH 111 |
Discrete Mathematics for Computing l and Linear Algebra |
|
| CISC 102 & MATH 112 |
Discrete Mathematics for Computing l and Introduction to Linear Algebra |
|
| MATH 110 | Linear Algebra | |
| B. Complete 6.0 units from the following: | 6.0 | |
| MATH 120 | Differential and Integral Calculus | |
| MATH 121 | Differential and Integral Calculus | |
| MATH 123 | Differential and Integral Calculus I | |
| MATH 124 | Differential and Integral Calculus II | |
Elective Courses: 48.00 Units
Students should consider focussing their electives on a Minor in another topic.
Statistics Course List (STAT_Options)
| Code | Title | Units |
|---|---|---|
| BIOL 243 | Introduction to Statistics | 3.0 | CHEE 209 | Analysis Of Process Data | 3.5 |
| COMM 162 | Managerial Statistics | 3.0 |
| ECON 250 | Introduction to Statistics | 3.0 |
| GPHY 247 | Introduction to Statistics | 3.0 |
| KNPE 251 | Introduction to Statistics | 3.0 |
| NURS 323 | Introduction to Statistics | 3.0 |
| POLS 385 | Introduction to Statistics | 3.0 |
| PSYC 202 | Statistics in Psychology | 3.0 |
| SOCY 211 | Introduction to Statistics | 3.0 |
| STAM 200 | Introduction to Statistics | 3.0 |
| STAT 263 | Introduction to Statistics | 3.0 |
| STAT 367 | Engineering Data Analysis | 4.0 |
CISC Substitutions Course List (CISC_Subs)
| Code | Title | Units |
|---|---|---|
| COMM 365 | Advanced Business Decision Modeling | |
| ELEC 470 | Computer System Architecture | |
| ELEC 474 | Machine Vision | |
| MATH 272 | Applications of Numerical Methods | |
| MATH 337 | Stochastic Models in Operations Research | |
| MATH 401 | Graph Theory | |
| MATH 402 | Enumerative Combinatorics | |
| MATH 434 | Optimization Theory and Applications | |
| MATH 474 | Information Theory |