Data Science is an emerging field that aims to extract actionable insights from vast arrays of information. Drawing on techniques and theories from statistics, computer science and mathematics, the program focuses on the effective analysis and use of large data in the natural and social sciences.
The explosion of data in today’s world is rapidly shaping the landscape of our life. This has led to an urgent need to process massive amounts of data and obtain meaningful information. Data scientists are trained to meet such challenges. Through a structured curriculum that provides foundational knowledge as well as application skills, our students learn how to confront the most complex problems facing government and private industry.
The STEM-designated Data Science Program at GW is offered at both the Foggy Bottom and Virginia Science & Technology campuses.
Applicants should meet the following minimum requirements:
- Bachelor’s Degree from an accredited college or university with a solid academic record.
- College-Level Courses. Completion of the following courses: Multivariable Calculus (Math 2233 or equivalent) and Statistics (Stat 1051 or 1053 or higher)
- Computer Programming. There is no formal requirement for specific programming language. However, applicants are encouraged to demonstrate their capability in mastering skills dealing with programing and software in their applications.
Data Science Department Courses
- DATS 6101 Introduction to Data Science
- DATS 6102 Data Warehousing and Analytics
- DATS 6103 Introduction to Data Mining
- DATS 6201 Numerical Linear Algebra and Optimization
- DATS 6202 Machine Learning I
- DATS 6203 Machine Learning II
- DATS 6401 Visualization of Complex Data
- DATS 6402 High Performance Computing and Parallel Computing
- DATS 6450 Topics in Data Science
Examples of courses to be chosen in consultation with your advisor
- MATH 6522 Introduction to Numerical Analysis
- STAT 6207 Methods of Statistical Computing
- STAT 6214 Applied Linear Models
- STAT 6242 Regression Graphics/Nonparametric Regression
- ECON 8375 Econometrics I
- ECON 8376 Econometrics II
- ECON 8377 Econometrics III
- ECON 8378 Economic Forecasting
- GEOG 6304 Geographical Information Systems I
- GEOG 6306 Geographical Information Systems II
- GEOG 6307 Digital Image Processing
- PSC 8120 Nonlinear Models
- PSC 8132 Network Analysis
- PSC 8185 Topics in Empirical and Formal Political Analysis
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Last updated August 28, 2019
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