Core Courses

The core course material continually builds upon the Data Science lifecycle theme.

Course work is hands-on, presenting students with increasingly complex data curation as they continue to learn concepts relevant to each particular course.

Additionally, students are continually performing exploratory data analysis and preliminary statistical modeling. Statistical modeling and machine learning are thematic throughout the program.

Finally, the program continually emphasizes the goal of the data science lifecycle, namely achieving business intelligence for the stakeholders (end consumer of the analytics). This storytelling of the data and the analytical processes is also thematic as part of our process to continually develop and refine the students’ soft skills.

An introductory course in data science and analytics. The objective of the course is to give students a broad overview of the various aspects of data analytics such as accessing, cleansing, modeling, visualizing, and interpreting data. Students will perform hands-on learning of data analytic topics, using technologies such as Python, R, and open source analytic tools.

An intermediate statistics class designed to build the mathematical foundation for students dealing with Big Data phenomena. Topics include discussions of probability, data sampling, data summarization, sampling distributions, statistical inference, statistical pattern analysis, hypothesis testing, regression, and nonparametric inference over multidimensional data collections. Students will engage in Big Data projects using various publicly available data sets and leveraging modern Data Science tools, techniques, and cyberinfrastructure.

Covers the Fundamental concepts of current database systems and query methods with emphasis on relational model and non-relational techniques in Big Data environments. Topics include entity-relationship model, relational algebra, indexing, query optimization, normal forms, tuning, security, NoSQL, and data analytics skills in both relational and non-relational environments. Project work involves modern relational DBMS systems and NoSQL environments.

This course will cover visualization techniques and methods for a broad range of data types prevalent in engineering disciplines, life sciences, media, and business. Theoretical and practical aspects of information visualization and exploratory data visualization will be taught with a hands-on approach to give students experience in handling data with a set of tools and programming environments. Topics will include visual perception and distortions, color theory, pre-attentive processing, data types and models, visual variables, efficient visualizations, design principles, grammar of graphics, spatial visualization, maps, graph theory network visualization, data storytelling including hands-on programming to create plots, charts, heatmaps, spatial and network visualizations using R and Python libraries.

This course leverages the foundations in statistics and modeling to teach applied concepts in machine learning. Participants will learn various classes of machine learning and modeling techniques, and gain an in-depth understanding how to select appropriate techniques for various data science tasks. Topics cover a spectrum from simple Bayesian modeling to more advanced algorithms such as support vector machines, decision trees/forests, and neural networks. Students learn to incorporate machine learning workflows into data-intensive analytical processes.

This course provides an overview of state-of-the-art topics in Big Data Security, looking at data collection (smartphones, sensors, the Web), data storage and processing (scalable relational databases, Hadoop, Spark, etc.), extracting structured data from unstructured data, systems issues (exploiting multicore, security). Securing sensitive data, personal data and behavioral data while ensuring a respect for privacy will be a focus point in the course.

Introduces the ethics related to Big Data in industry, business, academia, and research settings. Students will learn the social, ethical, legal and policy issues that underpin the big data phenomenon. Discussions and case studies will help guard against the repetition of known mistakes and inadequate preparation. The course content will follow the guidelines to be developed by the Council for Big Data, Ethics, and Society.

Advanced Courses

Case studies and capstone allow students to specialize in one or a couple of particular domains.

Interdisciplinary faculty from other MU colleges and schools help lead domain-specific learning case study and capstone mentoring. On-Campus students may replace Case Study and Capstone with Thesis Research.

Using a case-study approach, students will engage in discussions on a variety of big data topics relevant to their emphasis area and the realm of Big Data. This course will help students generate ideas and prepare them for the Big Data Capstone. Course work will be performed in small teams, mentored by faculty and/or industry advisors. Teams will research, cultivate, curate, and leverage large data sets. Students will gain hands-on experience applying relevant data science and analytical technology and techniques to gain insight and information from these real-world data sets.

This course provides an opportunity for participants to tackle a real-world data science project, delivered as a problem-based exercise. Participants will perform the full data science lifecycle methodology on a relevant challenge problem as final learning activity that draws upon all the foundational data science concepts and technologies, as well as specialized technologies and concepts relative to a particular concentration area.

Investigation and research of a data science thesis topic, including exploratory data analysis, statistical modeling, and machine learning. Outcomes will include data-driven insights that advance science, society, or intelligent automation.

Emphasis Area Courses

Emphasis courses represent the final stage in the further refinement of learning with domain-specific data and challenges.

Interdisciplinary faculty from other MU colleges and schools help lead domain-specific learning through emphasis area courses.

BioHealth Analytics

This course will introduce the foundational concepts of genomics and bioinformatics. Genomics is a combination of biological and computational methods that explore the roles of DNA, genes, and proteins on a very large scale. However, understanding how to interpret and understand the results depends (at least) on a basic understanding of biology. The course does not assume a student has a biological background and it will cover the concepts necessary to implement genomics methods.

The integration of multiple types of omics data set such as genomics, epigenomics, transcriptomic, proteomic and metabolomics are very important to understand the pathophysiology of human complex diseases. This course will describe the basic concepts of Multiple types of Omics datasets and databases. This course will also focus on various tools and its application in knowledge discovery from multi-omics data set and its challenges related to preprocessing, analysis and visualization. Hands-on computer experience will be provided through web resources and Jupyter notebook environment.

Credit Hours: 3

This course covers the basic concepts surrounding the analysis of health data. Topics include ethics and regulations of protected health data, healthcare data standards, and statistical analysis and dissemination techniques suitable for health care settings. Project work involves accessing and analyzing real (de-identified) health care data. This course focuses on health data analysis that is done in industry, insurance, hospitals and research. Practical, hands-on course with focus on fundamental data science skillsets such as programming in Python and data carpentry.

This course covers advanced topics in health data analysis. Students will learn about research informatics and clinical trials, and advanced statistical methods used in health data analysis. Additionally, students will be exposed to new forms of health data processing such as free text data, image data, and longitudinal data. Students will explore the use of machine learning and AI in health care settings, and applied clinical informatics in the form of decision support. Project work involves accessing and analyzing real (de-identified) health care data.

Geospatial Analytics

This course provides an overview of theoretical and practical issues encountered when working with geospatial data for both the vector and raster data models with a focus on incorporating geospatial data into the data science lifecycle. Data access, indexing, retrieval, and other technical concepts are investigated. Important data storage paradigms such as enterprise geospatial databases and desktop GIS systems are explored along with scalable computational tools beyond desktop computing for Geospatial Big Data. Core issues in geospatial data storage, management, exploitation, and multi-data set entity resolution / correlation are examined.

This course will provide a practical overview of key issues encountered when working with and analyzing spatial data as well as an overview of major spatial analysis approaches. Discussions and laboratory work will focus on implementation, analysis, and interpretive issues given constraining factors that commonly arise in practice.

Introduction to the principles of remote sensing of the environment leading to information extraction from remote sensing geospatial raster data sets. Examines theoretical and practical issues associated with digital imagery from spacecraft and airborne systems, thermal imaging, and microwave remote sensing. Covers standard processing techniques, including preprocessing and normalization, pixel-level feature extraction, information extraction, and structural/object extraction.

Human-Centered Science Design

Covers the fundamental concepts of current visualization concepts and technologies, adding in Infographic and Interactive Visualization Design. Unlike many data visualization courses, this one focuses on principles of visualization design and the grammar of graphics as they can be applied to combining art and technology to tell data stories. These principles are then implemented in popular contemporary visualization technologies. Students will develop an advanced knowledge of the appropriate selection, modeling, and evaluation of data visualizations.

Usability is concerned with how well a person can use a designed system to accomplish the goals for which that system is designed. This course provides an overview of methods for usability testing of data science applications through readings, examples and discussions. Students will work in groups to develop and present a usability test plan for a data science application or system.

Data Journalism/Strategic Communication

This course offers an introductory exploration of Natural Language Processing (NLP) techniques in the context of generative AI, with a focus on NLP for Computational Social Science and Large Language Models (LLM). Designed for students with no prior experience in NLP and machine learning principles, this course provides a comprehensive introduction to NLP, applications of basic and advanced NLP techniques, capabilities, and workings of generative AI models. Discussion of NLP methods will be particularly geared towards their application to computational social science research.

This course provides hands-on experience using several digital platforms such as Facebook Insights, Google AdWords, Google Analytics, Adobe Analytics, Clarabridge and Topsy. In this course, you’ll learn digital advertising terminology and jargon, the importance of digital analytics, the role of analysts, qualities of effective analysts, the digital optimization process, web metrics, and key performance indicators, as well as the essentials of collaboration and generating support and buy-in while gaining your executive’s attention.

This is a planned future course and not on current schedules.

This course is intended to review theoretical, conceptual, and analytic issues associated with network perspectives on communicating and organizing. The course will review scholarship on the science of networks in communication across a wide array of disciplines in order to take an in-depth look at theories, methods, and tools to examine the structure and dynamics of networks.

An intermediate data wrangling and analysis class designed to provide students with an in-depth overview of collecting and analyzing Twitter data. Computational topics include composing, sending, and receiving Hypertext Transfer Protocol (HTTP) messages. Data wrangling topics include parsing json files, navigating recursively nested structures, and processing textual data. Analysis methods include machine learning, network analysis, topic modeling, time series, etc.

High-Performance Computing

he course introduces the main concepts and techniques of data mining and information retrieval. It covers a variety of data mining topics and methods to extract hidden and predictive patterns from large data collections. Furthermore, theory and techniques for the modeling, indexing, and retrieval of relational, non­relational, text­based and multimedia databases is covered. Topics include introduction to data mining process, mining frequent patterns, and pattern analysis, as well as different information retrieval models and evaluation, query languages and operations, and indexing/searching methods.

This course introduces students to cluster and cloud computing big data ecosystems. Topics include a survey of cloud computing platforms, architectures, and use-cases. Students will examine scaling data science techniques and algorithms using a variety of cluster and cloud paradigms, such as those built atop Hadoop (Map-Reduce) concepts, and others.

This course will provide in-depth treatment of the evolution of high performance, parallel computing architectures and how these architectures and computational ecosystems support data science. We will cover topics such as: parallel algorithms for numerical processing, parallel data search, and other parallel computing algorithms which facilitate advanced analytics. To reinforce lecture topics, learning activities will be completed using parallel computing techniques for modern multicore and multi-node systems. Parallel algorithms will be investigated, selected, and then developed for various scientific data analytics problems. Programming projects will be completed using Python and R, leveraging various parallel and distributed computing infrastructure such as AWS Elastic Map Reduce and Google Big Query. Students will research emerging parallel and scalable architectures for data analytics.

Sample Course Path for MS Online

Students move through 8-week modules completing core courses and then progressing through emphasis area courses directly applicable to their area of study.

Sample Course Path for MS On Campus

Students move through 8-week or 16-week modules completing core courses and then progressing through emphasis area courses directly applicable to their area of study. Case Study and Capstone may be replaced with Thesis Research.