Thesis\Treatise Track: Total Credit Hours Required to Finish the Degree ( 48 Credit Hours ) as Follows
Specialization Requirements
Students must pass all of the following courses plus ( 18 ) credit hours for the Thesis
|
Course Number |
Course Name |
Weekly Hours |
Cr. Hrs. |
Prerequisite |
||
|---|---|---|---|---|---|---|
|
Theoretical |
Practical |
|||||
| 152648000 | MACHINE LEARNING | 3 | - | 3 |
- |
|
| 152648010 | THEORY BUILDING IN RESEARCH | 3 | - | 3 |
- |
|
| 152648020 | QUANTITATIVE RESEARCH METHODS | 3 | - | 3 |
- |
|
| 152648030 | STATISTICAL INFERENCE AND REPRESENTATION | 3 | - | 3 |
- |
|
| 152648040 | LINEAR ALGEBRA AND OPTIMIZATION FOR MACHINE LEARNING | 3 | - | 3 |
152648000 MACHINE LEARNING |
|
| 152648050 | DEEP LEARNING AND NEURAL NETWORKS | 3 | - | 3 |
152648000 MACHINE LEARNING |
|
| 152648060 | TEXT MINING | 3 | - | 3 |
- |
|
| 152648070 | QUALITATIVE RESEARCH METHODS | 3 | - | 3 |
- |
|
Students must pass ( 6 ) credit hours from any of the following courses
|
Course Number |
Course Name |
Weekly Hours |
Cr. Hrs. |
Prerequisite |
||
|---|---|---|---|---|---|---|
|
Theoretical |
Practical |
|||||
| 152648080 | DATA ANALYTICS FOR BUSINESS | 3 | - | 3 |
- |
|
| 152648090 | COMPUTATIONAL FINANCE | 3 | - | 3 |
- |
|
| 152648100 | MACHINE LEARNING IN FINANCE | 3 | - | 3 |
152648000 MACHINE LEARNING |
|
| 152648110 | ACCOUNTING DATA ANALYTICS | 3 | - | 3 |
- |
|
| 152648120 | SEMINAR IN FINANCIAL ANALYTICS | 3 | - | 3 |
- |
|
| 152648130 | RESEARCH METHODS FOR ACCOUNTING AND FINANCE | 3 | - | 3 |
- |
|
| 152648140 | SPECIAL TOPICS FOR DATA SCIENCE RESEARCH AND DEVELOPMENT | This course aims to cover Special Topics in Data Science Development and Research that are of current interest to the Enterprise and Research Community. Some of the potential topics that can be covered in this course include Data Management, Distributed Systems, Applications of Data Science to Business and Health, Data Fusion, Anomaly Detection, and Expert Systems Research and Development. | 3 | - | 3 |
- |
| 152648150 | DATA VISUALIZATION | This course is introduction to the theory and concepts of data visualization and the techniques used to create visual representation of large amounts of data. Topics covered include data representation, data and task abstraction, validation, tables and spatial data and maps and other channels. The emphasis will be on developing data visualization from a variety of sources and effectively communicating results. | 3 | - | 3 |
- |
| 152648160 | NATURAL LANGUAGE PROCESSING | This course provides an overview of the ways that computers can process and interpret written and spoken language and the role of natural language processing in artificial learning. Topics covered include processing text, classifying text, analyzing sentence structure and meaning, information extraction speech recognition and sentiment analysis. Emphasis will be placed on recent developments in computational linguistics and machine learning. | 3 | - | 3 |
- |
| 152648170 | PROGRAMMING LANGUAGES | This course is an introduction to the systems and theory of the programming languages used in Artificial Intelligence and Data Science. Topics covered include evaluating the benefits and uses of and comparing and contrasting programming languages available to students. Special emphasis will be placed on Python and R and libraries such as TensorFlow and Scikit-learn will be introduced. This course will be updated with new languages as the field develops. | 3 | - | 3 |
- |
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