MSDS core and elective courses are taught by faculty members in the School of Computer and Information Sciences. Some electives are taught by well-credentialed adjunct faculty with connections to local industry. The selection of electives varies every program year, with frequent new additions.
The MSDS degree requires the completion of five core classes and three elective courses.
This foundational course offers a full-spectrum introduction to data science and data science workflows, emphasizing data as a source of value creation in the enterprise. The R programming environment serves as the implementation vehicle in support of essential data science activities – data exploration and visualization, data wrangling, predictive modeling, model deployment, and communication. The R programming environment, along with Python, is among the most important tools in the data scientist’s toolbox and this course will feature tools and a style of programming inspired by the popular tidyverse ecosystem – ggplot2 for data visualization, dplyr and tidyr for data wrangling. Students will master elements of the data science workflow through a series of short R programming exercises reinforced by a full-spectrum, integrative final project. Presentation skills are an ever-present theme as students are challenged, through every stage of analysis, to communicate managerial relevance and value to the enterprise.
It’s one thing to conduct an analysis, it’s another to convince someone to change their behavior based on this analysis. In this course, students will study theories of visualization, communication and presentation with the purpose of translating technical results into actionable insight. Using a mix of case studies and code, the course begins with an examination of how to ask good research questions. It then covers the psychology of communication and the construction of compelling visualizations. Finally, students are tasked with writing and presenting their work in a manner suited to a non-technical audience.
This course explores the legal, policy, and ethical implications of data. These types of issue arise at each stage of the data science workflow including data collection, storage, processing, analysis and use. Armed with legal and ethical guidelines, students are then confronted with topics including privacy, surveillance, security, classification, discrimination, decisional-autonomy, and duties to warn or act. Using case studies and a lecture-discussion format, the course will address real-world problems in areas like criminal justice, national security, health, marketing and politics.
Data management is core to both applied computer science and data science. This includes storing, managing, and processing datasets of varying sizes and types. This course introduces students to the various ways in which data is stored and processed including relational databases, file-based databases, cloud-based storage and data streaming. A key component of the course is learning which architectures fit which types of data science problem (and the strengths and weaknesses of each). Students will learn to work with data that is both clean and structured, and dirty and unstructured.
Machine learning is becoming a core component of many modern organizational processes. It is a growing field at the intersection of computer science and statistics focused on finding patterns in data. Prominent applications include personalized recommendations, image processing and speech recognition. This course will focus on the application of existing machine learning libraries to practical problems faced by organizations. Through lectures, cases and programming projects, students will learn how to use machine learning to solve real world problems, run evaluations and interpret their results.
Over the course of the semester, students will propose, plan and execute an actual data science project. Run as an independent or small-group project during the student’s last term, the project provides an opportunity to integrate all of the core skills learned throughout the program, and to develop a portfolio piece that can help with students’ career aspirations. Projects must be consequential in nature—i.e., have a real (or potential) impact on some organization, or the world. Grades will be based on assessments by both the faculty advisor and those (potentially) impacted by the project’s results. Data sets must be selected by the student either from a public repository or from the company for which they work and approved by the course instructor within the first two weeks of the term.
Offering: Cybersecurity can be understood as a mindset or approach rather than a subfield of computer science, such as secure mobile computing, network and operating system security, secure data bases, and secure cryptography algorithms. This course prepares a general audience to incorporate security concepts and ethics into their daily lives and offers some basic familiarity with writing security oriented code.
Offering: As “big data” and the role of data scientist enters into mainstream life, the way science is done is also evolving. Natural scientists are frequently turning to trained data scientists to collaborate and assist with data reduction, data storage, and machine-learning. This course is intended to help bridge the gap across disciplines: to instruct data scientists in the analyses, formats, requirements, and approaches commonly used in the natural sciences. Particular emphasis will be placed on understanding what sorts of information scientists are interested in, and in facilitating communication between data scientists and natural scientists. Additionally, as science can frequently use a slightly different technology stack than commercial or industrial groups, some time will be devoted to learning how to navigate a Linux environment and work with a variety of open-source frameworks.
Offering: Survival analysis methods consist of statistical modeling techniques that predict the time to an event. The event of interest often depends on the application area. Survival analysis techniques have been used in a wide variety of areas—medical professionals working to predict the onset of a disease; HR representatives studying trends in workforce attrition; mechanical engineers investigating reliability of post-released products; and many other applications. In this course, students will be exposed to practice survival analysis techniques through exploration of real data sets. Students can expect to develop a deeper understanding of concepts including: observation censoring, accelerated life testing and experimental design, recurrence analysis, and implementation of survival analysis techniques in statistical software as well as communicating statistical results to different audiences.
Offering: The advanced machine learning course is designed for students with a practical data science background in machine learning who seek to deepen their understanding and proficiency in advanced concepts and techniques. The course surveys cutting-edge topics and applications, including the following: Ensemble learning, neural network and deep learning, reinforcement learning, and an overview of AutoML and MLops. Students will gain a deep understanding of the machine learning lifecycle, from data preparation and model development to deployment, monitoring and maintenance. Practical hands-on projects and case studies will provide students with experience tackling real life problems.
At the end of the course, students are expected to demonstrate proficiency in implementing and optimizing advanced machine learning models, applying cutting-edge concepts to solve real-world problems and exploring their applications in various domains. Design, develop and evaluate complex machine learning models with an emphasis on interoperability, ethical consideration and practical feasibility. Students will have the opportunity to contribute to the course and to develop team final projects. Proficiency in Python programming, practical experience with scikit-learn and foundations in machine learning concepts and algorithms are required.
Prerequisite: DATA 505
Offering: This course introduces cloud computing as the solution to the problem of data-intensive programming at scale. We will survey the existing techniques in computing, their algorithmic basis, and explore them in practice. With the motivating example of the MapReduce programming model, will examine Microsoft Azure, Google Cloud, Amazon Web Services, three common commercial platforms for cloud computing, and Hadoop, an open-source alternative. The course will be supplemented with discussions of virtualization and cloud security. Experience coding in Python is recommended.
Offering: Power BI professionals should be comfortable assessing data quality, understanding data security, and comprehending data sensitivity. We will delve into these core topics, building student confidence in foundational modeling, report building, and extracting actionable insights from data in Power BI. As we navigate through foundational learning and hands-on experience, we will refer to domain areas covered in the PL-300 - Microsoft Power BI Data Analyst certification.
Offering: This course prepares students with technical backgrounds for sales roles in software and services, focusing on a consultative selling approach. The curriculum covers sales cycle basics, CRM and marketing automation tools, solution architecture, and advanced strategies like requirement analysis, proof of concept development, negotiation, and Challenger Sale tactics. Students will emerge with a strong foundation in sales strategies that emphasize empathy, identifying client needs, and solution-based selling. This course is ideal for newcomers considering a career in sales while enhancing their skills in the competitive tech market. Nothing happens in the technology industry until it’s sold!
(Cross-listed with Atkinson MBA)