Big Data Image

About Our Program

The SDSU Big Data Analytics (BDA) Program is a transdisciplinary program across technology, business, engineering, science, and social science domains leading to a Master of Science Degree in Big Data Analytics at San Diego State University. The two-year program is operated in a collaborative and active transdisciplinary educational environment for students and professionals who wish to advance their knowledge and skills in the fast growing fields of data science and data analytics. It is to meet the strong demand for data analytic jobs in the era of data- and knowledge-economy.

We will begin accepting student applications on October 1 each year. This program adopts rolling admissions. Due to limited capacity, we encourage students to submit their application to graduate admissions as early as possible to receive priority consideration for admission (by December 31 each year). Please go to the Graduate Admission website for steps to apply.

Big Data Analytics (BDA) is a dynamic approach to uncovering patterns, unknown correlations, and other useful insights from diverse, large-scale datasets. Although BDA is related to data science, statistics, artificial intelligence, and geospatial technologies, its focuses is on business discoveries from big data by using available software tools, such as data mining, machine learning, computational linguistics, geographic information systems (GIS), social network analysis, financial and marketing analytics, spatiotemporal data analysis, and time-frequency analysis.

SDSU BDA program is unique in the Southern California and admits students with background in social science, linguistics, arts, business, GIS, biology, and public health, in addition to those with background in engineering or computer science. Unlike most comparable programs, the SDSU BDA is a low-cost and flexible program that can meet each student’s need by customizing individual graduate Program of Studies (POS). Located in the finest city of America, SDSU continues to ascend its position as a leader in higher education. It is already ranked in the top 140 national universities and in the top 70 public universities in U.S. News & World Report's annual ranking of America's Best Colleges in 2018. FORBES magazine ranked SDSU No. 23 on its list of America's Most Entrepreneurial Universities, while U.S. News and World Report ranked SDSU’s entrepreneurship program No. 21 among the nation’s public universities.

The objective of the program is to produce technically competent students with the skills necessary to explore and identify research and business opportunities provided by Big Data across various application domains, such as information technology, geographic information systems (GIS), social and behavioral science, digital humanities, public health, business analytics, and biotechnology. The program has a dual-core design for students to learn both computational skills (programming languages and software) and analytical methods (data mining, statistics, machine learning, spatiotemporal analysis, visualization) for data models and business applications.

Upon completion of our program students will develop competencies in:

  • The management and analysis of Big Data applications with appropriate programming tools, statistical models, social theories, business concepts, and analytic software.
  • The use of the outcomes of analytics to formulate research hypotheses and to guide decision-making processes in academic or business settings.
  • Leading organizations in collecting, cleaning, organizing, analyzing, and modeling data for various applications.
  1. Articulate the basic principles of statistical inference and data analytical methods, including statistics, machine learning and spatiotemporal analysis.

  2. Apply appropriate computational skills and tools to collect, clean, summarize, analyze, and visualize Big Data in real world applications.
  3. Demonstrate proficiency in data analytic software, programming languages, and database management tools. 

  4. Present quantitative data analysis results effectively in both oral and written formats.

  5. Identify research challenges in data ethics, data privacy, and legal issues involved in the collection, storage, analysis, reporting, and distribution of Big Data.

Important Information