Master in
Data Science

Introduction to the tools and techniques used in data science. Hands-on activities will include how to manipulate and represent data, and methods to analyze data. Prerequisite: undergraduate course in probability or statistics; prior programming experience preferred.

An introduction to different types of quantitative research methods and statistical techniques for analyzing data, with a focus on application to real-world data problems. The course begins with a focus on measurement, inferential statistics, and causal inference. Further topics in quantitative techniques include: descriptive and inferential statistics, sampling, experimental design, parametric and non-parametric tests of difference, ordinary least squares regression, and logistic regression. Prerequisite: undergraduate course in statistics.

An examination of strategies for visualizing data. This course focuses on the design of visual representations of data in order to discover patterns, answer questions, convey findings, and drive decisions. Exercises throughout the course provide a hands-on experience using relevant, state-of-the-art programming libraries and software tools to apply concepts learned.

An exploration of ethical issues in computing, particularly regarding data analytics, and considers the consequences of increased artificial intelligence and data analysis integration into our world. Students will define systems for decision support and predictive analysis, and their effect on privacy and other ethical issues.

Provides a broad introduction to key concepts in machine learning. Emphasis will be on intuition and practical examples, though some experience with probability, statistics, and linear algebra will be important. Students will learn how to apply powerful machine learning techniques to new problems, run evaluations and interpret results, and think about scaling up from thousands of data points to billions.

Introduces students to analytical technologies and the fundamental methods, techniques, and software used to design and develop artificially intelligent systems. This course is focused on Artificial Intelligence (AI) as it relates to data science models and predictability.

Presents the breadth of data storage solutions. The content spans from traditional databases and business warehouse architectures to streaming analytics solutions and graph processing. Students will consider both small and large datasets as both are equally important and justify different trade-offs.

Presents methods and tools for working with large datasets. Consideration of the core concepts behind Big Data problems, applications, and systems and introduces Big Data frameworks, including Hadoop, Spark, and Amazon Web Services (AWS). Topics may also include GPU programming, feature hashing, and the map-reduce framework.

Application of skills learned throughout the data science program to specific industries (financial, business, healthcare, etc.). Students will work on several large projects throughout the course focusing on one industry at a time.

Explains how blockchain technologies work and how they can be applied to various industries to better understand their implications and innovative potential. The relationship between blockchain technology, AI, and Internet of Things will be critically analyzed.

Introduces linguistic phenomena and our attempts to analyze them with machine learning. The course will cover a wide range of concepts with a focus on practical applications such as information extraction, machine translation, sentiment analysis, and summarization.

Finalizes the data science experience with one large, term-long group project. Students will work in small groups to complete a data analysis project incorporating skills learned throughout the program.