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MS in Data Science: Learning at the Intersection of Data and Decision-Making

Our interdisciplinary MSDS curriculum focuses on using data analysis to create knowledge and understanding from big data.

Online MS in Data Science Curriculum Overview

Our degree program offers rigorous, interdisciplinary data science online courses that prepare you with marketable skills in data-centric problem-solving and strategic decision-making, resulting in optimized outcomes. The curriculum is delivered 100% online and can be completed in less than two years.

Coursework Overview

The MS in Data Science program requires a minimum of 30 credits and a minimum of ten courses. In addition to the four required core courses, students must take courses from the three categories of electives. Students may also choose to complete a two-class capstone or choose to take an additional two electives.

Course Curriculum

Core Courses

3 Credits

Advanced analysis in probabilistic systems with strong emphasis on theoretical methods. Development of analytical tools for the modeling and analysis of random phenomena with application to problems across a range of engineering and applied science disciplines. Probability theory, sample and event spaces, discrete and continuous random variables, conditional probability, expectations and conditional expectations, and derived distributions. Sums of random variables, moment generating functions, central limit theorem, laws of large numbers. Statistical analysis methods, including hypothesis testing, confidence intervals, and nonparametric methods. Undergraduates may not take both EE 0024 and EE 0104 for degree credit. Prerequisite: Math 0042 or equivalent. Recommendation: Senior or graduate standing or consent of instructor.

4 Credits

“Big data” deals with techniques for collecting, processing, analyzing, and acting on data at internet scale: unprecedented speed, scale, and complexity. This course introduces the latest techniques and infrastructures developed for big data, including parallel and distributed database systems, map-reduce infrastructures, scalable platforms for complex data types, stream processing systems, and cloud-based computing. You’ll learn to apply common statistical and machine learning techniques to large data sets. Course content will be a blend of theory, algorithms, and practical, hands-on work.

3 Credits

This course provides an overview of methods by which computers can learn from data or experience and make decisions accordingly. Topics include supervised learning, unsupervised learning, reinforcement learning, and knowledge extraction from large databases with applications to science, engineering, and medicine. You’ll learn to recognize a problem as being appropriate for a machine learning solution and take steps to solve that problem with an applicable technique.

4 Credits

A course on mathematical statistics. The emphasis is on theory, though there will also be many computations. Students will analyze problems of estimating, predicting, and inferring given limited data. The major topics include parameter estimation, convergence of random variables, properties of estimators, statistical tests and confidence intervals, and non-parametric statistics.

Four to Six Electives

Category A:

3 Credits

Explore the fundamental concepts of database management systems, including data models, SQL query language, implementation techniques, the management of unstructured and semi-structured data, and scientific data collections.

3 Credits

Delve into the fundamentals of cybersecurity, including attacking and defending networks, searching for vulnerabilities, cryptography, reverse engineering, web security, static and dynamic analysis, malware, and forensics. Hands-on labs and projects are included.

3 Credits

This course will discuss the limits of current web technologies, the similarities and differences between web and software engineering, design, information and service architectures, content management, and testing disciplines. Frameworks such as Rails, Spring, and Symfony will be emphasized and used. Projects will involve search, cloud computing, location-based services, and mobile web development.

Category B:

3 Credits

This course will focus on agents that must learn, plan, and act in complex, non-deterministic environments. We will cover the main theory and approaches of reinforcement learning (RL), along with common software libraries and packages used to implement and test RL algorithms. The course is a graduate seminar with assigned readings and discussions. The content of the course will be guided in part by the interests of the students. It will cover at least the first several chapters of the course textbook. Beyond that, we will move to more advanced and recent readings from the field (e.g., transfer learning and deep RL), with an aim towards focusing on the practical successes and challenges relating to reinforcement learning.

Category C:

3 Credits

This course focuses on the history, theory, and computational methods of artificial intelligence. Basic concepts covered include representation of knowledge and computational methods for reasoning. One or two application areas will be selected and studied from among these topics: expert systems, robotics, computer vision, natural language understanding, and planning.

4 Credits

In this introduction to the study of algorithms, explore strategies that include divide-and-conquer, greedy methods, and dynamic programming. Delve into graph algorithms, sorting, searching, integer arithmetic, hashing, and NP-complete problems.

Capstone Project

6 Credits

A two-course, hands-on, and project-based culmination to the program, in which students apply data science and analytic principles to the solution of a real-world problem. In the first course, students will perform requirements analysis, review available data sources, and propose a solution strategy to the problem, beginning their analysis. The second course completes the analysis process, culminating in a final report summarizing data gathered, analytic results, lessons learned, and opportunities for future study.

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