Master of Science in Data Science

This course explores the expertise, perspectives and the statistical foundation that data scientists apply to projects during four phases of data science: problem foundation, data acquisition, statistical modeling and analysis, and presentation of results. This course intends to provide students with a comprehensive introduction to all the major aspects of data science. Credits: 4

This course offers an introduction to SQL and Python object-oriented programming. Students will learn data programming concepts, reproducible research and version control that serve as a foundation for professional programming practice within Data Science. Emphasis is on both practical applications with industry standard software, but also on core concepts as preparation for life-long learning. Credits: 4

This course introduces students to the fundamentals of exploratory data analyses and data visualization, allowing students to turn data into insights, pictures, and stories.. The following topics will be covered:

• Transforming data into information and subsequent actionable insights and knowledge
• Exploring the basics of graphic design and making a “good” graph
• Undertaking data due diligence
• Descriptive and inferential data analytical framework
• Examples and practice of exploratory data analysis and using data for segmentation and classification
• Exploring why some data visualizations present information effectively and others do not,
• Considering visualization as a component of the data analytics systems

The course will address data exploration utilizing classical statistical techniques, as well as topics of statistical inference, statistical significance, and outcome validity and reliability. Additionally, this course will introduce students to the foundations of constructing plots with the grammar of graphics and utilizing packages such as ggplot2 and Shiny. Credits: 4

Prerequisites: DSE 5001, DSE 5002

This course introduces students to the fundamental concepts capturing legal rights and responsibilities associated with data capture, storage and leveraging data for decision-making. Given the very diverse mix of topics falling under this broad umbrella, the aim of the course is to provide a general overview of the applicable aspects of the US regulatory and legislative framework, and then to offer more topically-focused overview of the key notions falling within the following areas: operational and experiential aspects of data governance and differential privacy, data-capture related rights and responsibilities, data governance design and management, data security and privacy, information quality, and the ethical aspects of data access, usage, and sharing. Credits: 4

Prerequisites: DSE 5001 and DSE 5002, or CSC 6003

This course offers students an introduction to the basic concepts and applications of predictive analytics, which is a broad domain of data analytical practice focused on developing forward-looking statistical estimates. The course begins with an overview of predictive and prescriptive analytics, followed by immersion in statistical prediction related multivariate statistical concepts, including an in-depth discussion of commonly used types of multivariate predictive models, all focused on strengthening students’ comprehension of core statistical notions, including variable types, statistical learning, statistical regression and classification, non-linear modeling, and tree based estimation. The second part of the course focuses on hands-on applications of model fitting and evaluation using programming applications in R or Python. Credits: 4

Prerequisites: Grade of B or higher in DSE 5001 and DSE 5002

This course offers students an introduction to the fundamentals conceptual, operational and experiential aspects of machine learning, or broadly defined algorithmic capability to manipulate, process, amend, and analyze data using appropriate applications. This is an introductory course, designed to endow students with the foundational theoretical and experiential knowledge of automated pattern detection approaches focused on four key outcomes of categorization, prediction, identification and detection, and further framed within the confines of supervised and unsupervised learning. The course is meant to offer an overview of this highly complex and rapidly evolving field; as such, it focuses on established approaches, key developmental trends, and hands-on applications of select techniques. Credits: 4

Prerequisites: DSE 6111

This course provides an introduction to machine learning on unstructured data sets, focusing on text and image data. More specifically, students learn the proper machine learning workflows and techniques for extracting critical insights and then apply their knowledge to real-world text and image data sets. The course includes an overview of these highly complex topics, focusing on the key intuition behind established approaches and hands-on application of techniques. Credits: 4

This course covers cloud-based AI systems using state-of-the-art, industry-standard platforms. The course also includes an overview of the process of creating Deep Learning AI systems on cloud systems and the data engineering aspects necessary to implement such systems. Students build an online, trained AI system as part of the course project. Credits: 4

This course introduces students to the fundamentals of exploratory data analyses and data visualization, allowing students to turn data into insights, pictures, and stories.. The following topics will be covered:

• Transforming data into information and subsequent actionable insights and knowledge
• Exploring the basics of graphic design and making a “good” graph
• Undertaking data due diligence
• Descriptive and inferential data analytical framework
• Examples and practice of exploratory data analysis and using data for segmentation and classification
• Exploring why some data visualizations present information effectively and others do not,
• Considering visualization as a component of the data analytics systems

The course will address data exploration utilizing classical statistical techniques, as well as topics of statistical inference, statistical significance, and outcome validity and reliability. Additionally, this course will introduce students to the foundations of constructing plots with the grammar of graphics and utilizing packages such as ggplot2 and Shiny. Credits: 4

Prerequisites: DSE 5001, DSE 5002

Use analytical techniques to convert social media data into marketing insights, benefits and limitations of social media listening, creation of monitors, discussion of standard social media metrics, market structure and consumers’ perception of brand. Credits: 4

Apply analytical techniques including multiple regression and discriminant analysis to predict player performance and team outcomes as well as business models for sports franchises. Credits: 4

This course is hands-on analytics of real-world healthcare datasets. Students apply their modeling skills to real-life applications in the healthcare environment. Credits: 4

Students learn to write and read SQL and no-SQL queries. Students also develop an understanding of the design and function of relational and non-relational databases. Credits: 4

This course provides students with a comprehensive understanding of the big data processing foundation and techniques. Students gain an understanding of basic concepts of parallel computing, big data, Hadoop, MapReduce and Spark. They also develop skills to solve big data processing problems. Credits: 4

This course introduces students to machine learning on unstructured data sets, focusing on text and image data. More specifically, students learn the proper machine learning workflows and techniques for extracting critical insights and then apply their knowledge to real-world text and image data sets. The course includes an overview of these highly complex topics, focusing on the key intuition behind established approaches and hands-on applications of techniques. Credits: 4

Businesses are increasingly turning to data to evaluate and improve the efficacy of decisions. The ability to use data to inform the decision-making process is a critical skill for successful business professionals. This course introduces students to the growing role of evidence-based management, and the manner in which data is used to answer high-level business questions. Students are exposed to real world applications of analytics to solve problems in a variety of industries. Credits: 4

The objective of this course is to introduce students to the basic principles and fundamental techniques in the context of financial analysis. The course introduces students to the basic principles of finance, critical financial terminology, and key financial management indicators. Emphasis is placed on building a foundational knowledge of financial statement analysis, and assessing your organization’s financial health with a focus on practical application and real-world situational analyses. It is a great overview for students to familiarize themselves with relevant finance concepts and provides a structured approach to facilitating the identification of future financing needs and measuring the impact and sustainability of growth. Credits: 4

Leaders today face highly dynamic business environments characterized by numerous disruptive forces and complex challenges. To be an effective leader in this environment necessitates a new set of skills. In this highly interactive course, students will explore leadership theories and will practice applying various leadership frameworks, approaches, and tools to develop a holistic understanding of leadership at the individual, team, and organizational levels. Students will also gain valuable insights into their own personal leadership style, how to collaborate with internal and external constituents in a manner that benefits all stakeholders, and learn communication strategies, principles, and methods that are essential for business success. Credits: 4

In this capstone experience, students take a problem through the full data science lifecycle using data provided by the instructor or a data set from an employer or internship. The instructor provides data and requirements. Students must complete six courses from the MSDS program before taking this capstone course. Credits: 4