All students must complete the following courses:

• BDA 594 / GEOG 594 Big Data Science and Analytics Platforms Units: 3
• BDA 572 / LING 572 Python Scripting for Social Science Units: 3 OR GEOG582 GIS Programming with Python Units: 3
• MIS 686 Enterprise Data Management Units: 3
• BDA 602 Machine Learning Engineering Units: 3 

Prior approval of electives by the graduate adviser is required for their application towards the degree. Electives may be selected from the following list. Students should be aware that many electives require prerequisites that will not fulfill degree requirements.

• ACCTG 621 - Accounting Information Systems Units: 3
• ACCTG 673 - Accounting Information Systems (AIS) Development Units: 3
• B A 640 - Financial Reporting and Analysis Units: 2
• B A 642 - Statistical Analysis Units: 2
• B A 649 - Business Analytics Units: 3
• BDA 603 - Smart Cities and Sustainability Units: 3
• BDA 696 - Advanced Special Topics in Big Data Analytics Units: 3
• BDA 797 - Research Units: 1-3
• BDA 798 - Special Study Units: 1-3
• BIOMI 568 - Bioinformatics Units: 3
• BIOMI 600 - Methods in Bioinformatics, Medical Informatics, and Cheminformatics Units: 3
• CS 503 - Scientific Database Techniques Units: 3
• CS 514 - Database Theory and Implementation Units: 3
• CS 549 - Machine Learning Units: 3
• CS 577 - Principles and Techniques of Data Science Units: 3
• CS 581 - Computational Linguistics Units: 3; also listed as LING 581 - Computational Linguistics Units: 3
• CS 653 - Data Mining Units: 3
• CS 660 - Algorithm Analysis and Design Units: 3
• ECL 560 - Literature in the Digital Age Units: 3
• ECL 562 - Digital Methods in Literary Studies Units: 3
• GEOG 580 - Data Management for Geographic Information Systems Units: 3
• GEOG 581 - Data Visualization Units: 3
• GEOG 583 - Internet Mapping and Distributed GIServices Units: 3
• GEOG 584 - Geographic Information Systems Applications Units: 3
• GEOG 593 - GIS for Business Location Decisions Units: 3
• GEOG 780 - Seminar in Techniques of Spatial Analysis Units: 3
• LING 571 - Computational Corpus Linguistics Units: 3
• LING 583 - Statistical Methods in Text Analysis Units: 3
• MATH 524 - Linear Algebra Units: 3
• MIS 515 - Object-Oriented Programming for Business Applications Units: 3
• MIS 720 - Electronic Business and Big Data Infrastructures Units: 3
• MIS 687 - Secure Enterprise Networking and Mobile Technologies Units: 3
• MIS 691 - Decision Support Systems Units: 3
• MIS 748 - Time Series Analysis for Business Forecasting Units: 3
• MIS 649 - Business Analytics Units: 3
• P H 602 - Biostatistics Units: 3
• P H 700A - Seminar in Public Health: Epidemiology Units: 1-3
• SOC 607 - Advanced Quantitative Methods: Core Course Units: 3
• SOC 730 - Seminar in Social Institutions Units: 3
• STAT 550 - Applied Probability Units: 3
• STAT 551A - Probability and Mathematical Statistics Units: 3
• STAT 610 - Linear Regression Models Units: 3

Prior approval of electives by the graduate advisor is required for their application towards the degree. Students should be aware that many electives require prerequisites that will not fulfill degree requirements.

• BDA 797 Research Units 1-3 (Cr/NC/RP)
• BDA 798 Special Study 1-3 (Cr/NC/RP)

• Plan A: BDA 600 - Big Data Analytics Capstone Seminar Units: 3 and BDA 799A - Thesis Units: 3.
• Plan B: BDA 600 - Big Data Analytics Capstone Seminar Units: 3 and three additional units of elective graduate courses listed above (including BDA 797 or BDA 798).

A student may be advanced to candidacy after completing at least 12 units of coursework listed on the official BDA program of study with a minimum grade point average of 3.0 (B).  Students can register BDA799A (Thesis) or take Comprehensive Exam only AFTER advancing to Candicacy.

 

BDA 572 / LING 572. Python Scripting for Social Science Units: 3
Python scripting for social science data. Statements and expressions. Strings, lists, dictionaries, files. Python with unformatted data (regular expressions). Graphs and social networks. Spatial data and simple GIS scripts.

BDA 594 / GEOG 594. Big Data Science and Analytics Platforms Units: 3
Fundamental concepts, knowledge, and methods in Big Data Science, including data collection, filtering, processing, analysis, machine learning, text analysis, GIS, and visualization. Computational platforms, skills, and tools for conducting Big Data Analytics with real world case studies and practical examples.

BDA 600. Big Data Analytics Capstone Seminar Units: 3
Capstone course using Big Data Analytics skills and knowledge acquired in previous courses to work on real world research problems and business examples. Student teams will conduct group projects and deliver final presentations and reports to demonstrate their data analytics skills and knowledge.

BDA 602. Machine Learning Engineering Units: 3
The goal of this course is to teach practical machine learning techniques. The student will learn to prepare data, extract features, build machine learning models and deploy them to production.

BDA 797. Research Unit(s) 1-3 (Cr/NC/RP)
Research in one of the fields of big data analytics. Maximum Credits: six units applicable to a master’s degree.

BDA 798. Special Study Unit(s) 1-3 (Cr/NC/RP)
Prerequisite: Consent of staff; to be arranged with program director and instructor.
Individual study. Maximum Credits: six units applicable to a master’s degree.

BDA 799A. Thesis or Project Units: Units 3 (Cr/NC/RP)
Prerequisites: An officially appointed thesis committee and advancement to candidacy.
Preparation of a project or thesis for the master’s degree.

BDA 799B. Thesis or Project Extension Units 0 (Cr/NC)
Prerequisite: Prior registration in Thesis or Project 799A with an assigned grade symbol of RP.
Registration required in any semester or term following assignment of RP in Course 799A in which the student expects to use the facilities and resources of the university; also student must be registered in the course when the completed thesis or project is granted final approval.