## Curriculum Overview

Our online MS in Data Science program offers a rigorous, interdisciplinary curriculum designed to put you at the center of data-centric problem-solving that can help organizations make strategic decisions and optimize outcomes. The curriculum is delivered 100% online and can be completed in less than 2 years.

## Courses

### COMP 205 Principles of Data Science in Python

#### 4 Credits

In this course, you’ll learn the fundamentals of python programming for data analysis, including common python data structures and algorithms, design of python programs, coding standards and practices, and the use and creation of software libraries. You’ll also complete lab work utilizing iPython and the Jupyter data analysis workflow framework. Examples used in the course are drawn from data preparation and transformation, statistical data analysis, machine learning, deep learning, and deep data science including recommendation systems and trend analysis.

### EE 104 Probabilistic Systems Analysis

#### 3 Credits

The goal of this class is the development of basic analytical tools for the modeling and analysis of random phenomena and the application of these tools to a range of problems arising in engineering, manufacturing, and operations research. You’ll learn to utilize probability to analyze problems and to assess the risk inherent in a specific problem solution. The first portion of this class will cover introductory probability theory including sample spaces and probability, discrete and continuous random variables, conditional probability, expectations and conditional expectations, and derived distributions. The balance of the class will be concerned with statistical analysis methods including hypothesis testing, confidence intervals and nonparametric methods.

### COMP 135 Introduction to Machine Learning

#### 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.

### MATH 166 Statistics

#### 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.

### COMP 138 Reinforcement Learning

#### 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.

### EE 140 Stochastic Processes, Detection, and Estimation

#### 3 Credits

The goal of this course is the development of basic analytical tools for the modeling and analysis of random phenomena and the application of these tools to a range of problems arising in engineering, manufacturing, and operations research. The first portion of this course will cover introductory probability theory including sample spaces and probability, discrete and continuous random variables, conditional probability, expectations and conditional expectations, and derived distributions. The balance of the class will be concerned with statistical analysis methods including hypothesis testing, confidence intervals, and nonparametric methods.

### MATH 123 Aspects of Data Analysis

#### 3 Credits

A course in mathematical data science with an emphasis on theory. The course will also highlight important applications and students will have the opportunity to program some standard algorithms. The topics to be covered include principle component analysis, algorithms in numerical linear algebra, unsupervised clustering and density methods, nearest neighbor classifiers, supervised methods such as support vector machines and neural networks, and spectral graph theory, with applications in areas like image processing and network analysis.

### COMP 119 Big Data

#### 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.

### DSO 293/294

#### 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.

### COMP 131 Artificial Intelligence

#### 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.