Data Science (Second Major Only)
Requirements
Data Science is open to all students as a second major; this means that the student will have some other discipline as their primary major. Students whose primary major is in Computer Science, Software Engineering or Mathematics will find the Data Science program the easiest since there is considerable overlap between those programs and the Data Science requirements Students from other disciplines are also encouraged to participate, but will have to take more courses. All students are encouraged to take the individual courses in the program, regardless of whether they wish to fulfill the second major requirements.
Data Science requires 72 course credit hours and a senior capstone experience; these include 36 hours of Fundamental data science topics, 20 hours in Advanced topics, and 16 hours in Electives. The 36 fundamental credit hours and no more than 8 of the elective credit hours may be used to satisfy any requirements for another major (primary or secondary) or minor; students with a second major in Data Science cannot earn a minor in Data Science as well. Thus, the 20 Advanced credit hours and at least 8 Elective credit hours can only be used to satisfy technical and free electives of another major program.
Furthermore, Data Science majors are expected to complete a Senior Project or Senior Thesis in either their primary major or within either the Computer Science and Software Engineering Department or the Mathematics Department, and that this work includes a data science component.
Data Science Core (56 hours)
Fundamentals (36 hours) These classes can be used to satisfy any requirements for any major. They can also be used to satisfy degree requirements for any minor, with the exception of the Mathematics Minor; for the Mathematics Minor at most two of these courses can also be used to satisfy those requirements.
- CSSE 120 Introduction to Software Development
- CSSE 220 Object-Oriented Software Development
- CSSE 230 Data Structures and Algorithm Analysis
- CSSE 333 Database Systems (pre: MA 275)
- MA 371 Linear Algebra I
or
MA 373 Applied Linear Algebra for Engineers - MA 381 Introduction to Probability with Applications to Statistics
- MA 382 Introduction to Statistics with Probability
Note: If the primary major requires MA 223 Engineering Statistics I, this would be accepted as a standard course substitution. - CSSE 286 Introduction to Machine Learning or
MA 386 Statistical Programming - PHIL H202 Business and Engineering Ethics
Advanced (20 hours) These classes can only be used to satisfy technical or free electives within the primary major, and cannot be used to satisfy any other requirements for other majors or minors.
- CSSE 413 Artificial Intelligence
- CSSE 433 Advanced Database Systems
or
CSSE 434 Introduction to the Hadoop Ecosystem - MA 384 Data Mining
- MA 485 Applied Regression Analysis
- MA 415 Machine Learning
Data Science Electives (16 hours) At most 8 of these credit hours can be used to satisfy degree requirements for any major or minor sought by the student. The remaining credit hours can only be used to satisfy technical or free electives within the primary major. The student can choose any courses from the following list of approved Data Science Elective courses (or another upper-level course approved by the Director of the Data Science program). The courses below noted by * cannot also be used to satisfy the requirements above.
- BMTH 312 Bioinformatics
- CSSE 433 Advanced Database Systems*
- CSSE 434 Introduction to Hadoop Ecosystem*
- CSSE 453 Topics in Artificial Intelligence
- CSSE 463 Image Recognition
- ECE 582 Advanced Image Processing
- MA 332 Introduction to Computational Science
- MA 335/CSSE 335 Introduction to Parallel Computing
- MA 342 Computational Modeling
- MA 416 Deep Learning
- MA 439 Mathematical Methods of Image Processing
- MA 482 Biostatistics
- MA 483 Bayesian Data Analysis
- ME 447 Visualizing Data
- ME 497 Reproducible Research
- OE 537 Advanced Image Processing
- PH 327 Thermodynamics and Statistical Mechanics
- PH 538 Introduction to Neural Networks
- ECON S451 Econometrics
Senior Capstone: Students should complete a senior project or senior thesis that includes a data science component. In order to use the senior capstone experience of another major as part of the Data Science second major, it must be approved by the Director of Data Science. Furthermore, the student may need to either include within the capstone report a description of the data science work done or submit a separate report to the Director of Data Science describing the data science component of the capstone.