Master of Science in Analytics

DATA 500: Analytical Programming with Python (3 credits)

This course introduces the essential general programming concepts and techniques to a data analytics audience without prior programming experience. The goal is to equip the students with the necessary programming skill to be successful in the other courses in the analytics program. Topics covered include: boolean, numbers, loops, function, debugging, Python’s specifics (such as NumPy, Pandas, Jupyter notebook), version control, and docker. Examples are drawn from the problems and programming patterns often encountered in analytics.

Prerequisite: None

DATA 501: Introduction to Analytics and Decision Making (3 credits)

Problem solving with descriptive, predictive and prescriptive analytics in a business context using spreadsheets and other analytic tools. Techniques include forecasting, optimization, location analysis, decision analysis, inventory management, among others.

Prerequisite: None

DATA 502: Visualization & Communication (3 credits)

The goal of this course is to introduce students to principles and techniques of representing data visually. Students will communicate data in a variety of ways using industry standard software and programming techniques to communicate an effective narrative. We’ll explore how to design and create data visualizations based on data available and tasks to be achieved. This process includes data modeling, data processing (such as aggregation and filtering), and mapping data attributes to graphical attributes. Students will create their own data visualizations, and learn to use two of the most used dashboard tools in the industry: tableau and Power BI. This course will focus on communication of data driven results; with a focus on understanding how to share insights in relevant ways for a variety of stakeholders.

Prerequisite: None

DATA 503: Data Leadership & Quantitative Communication (3 credits)

In this course, students will develop the oral and written presentation skills demanded in data-driven environments. Students will learn to identify and articulate business questions and then translate data into compelling and effective narratives for decision-making. This course reviews quantitative communication research methods, with an emphasis on statistical analysis, and explores vocational/professional applications of communication research. In addition, this course will focus on how to lead technology teams focusing on technology systems, procedures, information risk, data integrity, ethics, information system (IS) policies, strategies, cloud computing, and budget.

Prerequisite: None

DATA 504: AI for everyone (3 credits)

AI is not only for data scientist, analytics professional or engineers. “AI for Everyone” is a non-technical course that will help you understand AI technologies and spot opportunities to apply AI to problems in your own organization. You will see examples of what today’s AI can and cannot do. By the end of this course, you will understand how AI is impacting society and how to navigate through this technological change.

Prerequisite: None

DATA 505: Data Management (3 credits)

This course introduces the principles of data management and analysis. It covers database, data warehousing, big data, and predictive analytics concepts so students can perform data management and analysis in organizations. Foundational data management concepts and models are used to describe the creation, organization, distribution, storage, access, retrieval, management, use, and preservation of data throughout the data lifecycle. The course includes a review of data management best practices; governance, quality controls, data integrity, guidelines, and policies.

Prerequisite: DATA 500

DATA 510: Predictive Analytics (3 credits)

A deep dive in applications of predictive modeling and machine learning techniques such as Logistic Regression, Decision Trees, Neural Net, Ensemble Models, KNN, Market Basket Analysis, Text Analytics and Sentiment mining. A quick overview of applications of deep learning models such as CNN and RNN.

Prerequisites: DATA 500 & DATA 501

DATA 511: Prescriptive Analytics (3 credits)

Prescriptive analytics applied to resource allocation and operational problems encountered in accounting, economics, finance, management and marketing. Linear programming, goal programming, integer programming, and network models.

Prerequisites: DATA 500 & DATA 501

DATA 515: Analytics Capstone (3 credits)

This capstone course provides an opportunity for students in the analytics program to integrate and apply the analytics skills and knowledge learned in the previous courses. The analytics capstone course is a project based course, in which students will use analytics methodology to solve real-life problems. At the end of this Capstone, you’ll be able to ask the right questions of the data, and know how to use data effectively to address business challenges of your own. You’ll understand how cutting-edge businesses use data to optimize marketing, maximize revenue, make operations efficient, and make hiring and management decisions so that you can apply these strategies to your own company or business.

Prerequisites: DATA 510 & DATA 511

DATA 533: Finance Analytics (3 credits)

In today’s environment business, finance, and accounting professionals need to analyze an increasing volume of data in a meaningful way. This course covers the main quantitative analysis methods of finance. The emphasis is on rigorous and in-depth development of the key techniques and their application to practical problems in order to make sustainable strategic decisions. Good decisions depend on accurate and well-presented information drawn from both domestic and international sources and more importantly the ability to synthesize and draw conclusions from that data. This course will help individuals develop, interpret and analyze both internal and external financial information.

Prerequisites: DATA 500 & DATA 501. Notice that this course is cross-listed with DATA 433. However, graduate students will have extra assignments in comparison to undergraduate students.

DATA 535: Marketing Analytics (3 credits)

This course will focus on developing marketing strategies and resource allocation decisions driven by quantitative analysis. Marketing activities provide critical economic functions for the success of organizations. Companies of all sizes must develop effective marketing analysis to reach customers. The course will draw on and extend students’ understanding of issues related to integrated marketing communications, pricing, digital marketing, and quantitative analysis.

Prerequisites: DATA 500 & DATA 501. Notice that this course is cross-listed with DATA 435. However, graduate students will have extra assignments in comparison to undergraduate students.

DATA 537: Sports Analytics (3 credits)

Students will learn the analytical techniques that, when properly applied, can provide a competitive advantage to teams and players. Sports analytics helps facilitate decision-making both on and off the field. Students will learn how to apply methods and principles in a wide range of applications such as evaluating team and player performance; developing tactics and team strategies; improving sales and reducing expenses across an organization; and identifying opportunities to increase brand engagement.

Prerequisites: DATA 500 & DATA 501. Notice that this course is cross-listed with DATA 437. However, graduate students will have extra assignments in comparison to undergraduate students.

DATA 539: Healthcare Analytics (3 credits)

Professionals in today’s competitive healthcare environment need an understanding of how analytics impacts and informs healthcare decisions and outcomes. Explores the critical role information technologies and systems play in healthcare organizations. A variety of health care data will be analyzed in this course. Analysis of different kinds of health care data, such as patient electronic medical records, public health data, biomedical publications, social media pertaining to health, and ontologies in health care; students will read papers exploring different kinds of research and application development involving such data.

Prerequisites: DATA 500 & DATA 501

DATA 547: Applied Deep Learning I (3 credits)

Deep Learning is a subset of machine learning where artificial neural networks, algorithms based on the structure and functioning of the human brain, learn from large amounts of data to create patterns for decision-making. Neural networks with various (deep) layers enable learning through performing tasks repeatedly and tweaking them a little to improve the outcome. In this course, students will learn the foundational concept of neural networks and deep learning. By the end of this course, students will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications.

Prerequisite: DATA 510

DATA 548: Applied Deep Learning II (3 credits)

In the second course of applied deep learning, students will understand how computer vision works has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more. By the end of this course, students will be able to build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data.

Prerequisite: DATA 547

DATA 549: Intro to Natural Language Processes (3 credits)

Natural language processing is a subfield of linguistics, computer science, and artificial intelligence that uses algorithms to interpret and manipulate human language. In this course, students will introduce to the design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages, summarize text, and even build chatbots. NLP is one of the most broadly applied areas of machine learning and is critical in effectively analyzing massive quantities of unstructured, text-heavy data.

Prerequisites: DATA 510 & DATA 547

DATA 550: Intro to MLOps (3 credits)

Machine learning engineering for production refers to the tools, techniques, and practical experiences that transform theoretical ML knowledge into a production-ready skillset. Students will learn how to conceptualize, build, and maintain integrated systems that continuously operate in production. In striking contrast with standard machine learning modeling, production systems need to handle relentless evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance. In this course, you will learn how to use well-established tools and methodologies for doing all of this effectively and efficiently.

Prerequisites: DATA 510 & DATA 547