Data Analytics & Applications
Data is an essential component of many industries, and creating meaningful insights from the information pulled from that data can lead to better processes and outcomes. If a professional is looking to advance their career in this field, the Master of Data Analytics & Applications will provide the knowledge base and expertise they need to construct data systems that improve efficiencies and profit margins for organizations.
Offered in collaboration between the McKelvey School of Engineering’s Sever Institute and the School of Continuing & Professional Studies (CAPS), this flexible online program will help working professionals acquire the skills they need to advance in a data-driven environment.
In this program, modern learners will acquire relevant and practical knowledge of the data analytics and applications field, improve their critical thinking and communication skills, and develop the leadership acumen needed to be successful in demanding senior management roles.
This program can be completed entirely online.
Contact Info
| Contact: | Dorris Scott |
| Phone: | 314-935-5498 |
| Email: | d.scott@wustl.edu |
| Website: | https://caps.wustl.edu/items/mdaa/ |
The Master of Data Analytics & Applications program requires the successful completion of 30 units of graduate course work, including 21 units of required core courses and 9 units of electives. New students without evidence of math and programming proficiency will be required to take 6 units of bridge courses.*
Required Courses: 21 units
- Enterprise Data Management
- Analytics Applications
- Applied Data Analytics for Practitioners
- Data Engineering Foundations of Data Analytics
- Data Visualization and Storytelling
- Introduction to Relational Databases and SQL Programming
- Applied Machine Learning
Elective Courses: 9 units
Choose from options such as the following:
- Applied Natural Language Processing
- Applications of Deep Neural Networks
- Applied Simulation Modeling
- Architectural Data Analytics Applications
- Special Topics in Data Analytics and Applications
- Applied Research Study
This program is offered either mostly or fully online. Students entering the U.S. on an F-1 or J-1 Visa must enroll in a program full time. F-1 students are only permitted to enroll in one online course per semester and J-1 students may only enroll in non-credit online courses that do not count toward their degree program. The School of Continuing & Professional Studies (CAPS) cannot guarantee face-to-face enrollment options each semester of full time enrollment, therefore cannot issue an I-20 or DS 2019 to F-1 and J-1 students for this program. If you are an F-1 or J-1 student and wish to enroll in a CAPS program while here on a Visa, please contact our recruitment team to discuss your options for face-to-face program enrollment. F-1 and J-1 students should not enroll in online courses or programs without first consulting the university’s Office for International Students and Scholars (OISS).
- *
Proficiency in 1) introductory statistics and linear algebra and 2) basic programming is required for admittance into the program. There are two introductory bridge courses covering these subject areas. Students who have earned a 3.0 GPA or better in introductory statistics and linear algebra and a programming course can have the two bridge courses waived.
- Foundations of Programming for Data Analytics & Applications (U71 DATA 5001)
- Students with proficiency in Python may have the requirement to take Foundations of Programming for Data Analytics & Applications waived. Proficiency is established with a B or better in an introductory Python programming course or relevant work experience (as evaluated by the program director or delegated evaluator).
- Foundations of Mathematics for Data Analytics & Applications (U71 DATA 5002)
- Students with proficiency in introductory statistics and linear algebra may have the requirement to take Foundations of Mathematics for Data Analytics and Applications waived. Proficiency is demonstrated with a B or better in Introduction to Statistics and Linear Algebra courses.
- Foundations of Programming for Data Analytics & Applications (U71 DATA 5001)
Visit online course listings to view semester offerings for U71 DATA.
U71 DATA 5001 Foundations of Programming for Data Analytics & Applications
Programming is an increasingly important skill, whether you aspire to a career in software development or in other fields. This course introduces core programming concepts and problem-solving using Python. Students will learn the principles of software development, style, and testing. Topics include an operational model of Python execution, procedures and functions, iteration, recursion, lists, strings, algorithms, exceptions, object-oriented programming, and GUIs (graphical user interfaces). As the course progresses, students will learn to work with packages, data structures, object-oriented programming, and tools for data science and cybersecurity.
Credit 3 units. UColl: OLI
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U71 DATA 5002 Foundations of Mathematics for Data Analytics & Applications
This course introduces the fundamental concepts, theorems, and tools used in data science and machine learning, including probability, optimization and calculus, linear algebra, discrete mathematics, and statistics. Applications of the theory to data science and machine learning will be developed with mathematical concepts being applied in Python. Prerequisites: None
Credit 3 units. UColl: OLI
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U71 DATA 5013 Data Visualization and Story Telling
This course begins with a review of human perception and cognition, drawing upon psychological studies of perceptual accuracy and preferences. The course reviews principles of computational graphic design, what makes for a good graph, and why some data visualizations effectively present information and others do not. It considers visualization as a component of systems for data analytics and applications and presents examples of exploratory data analysis, visualizing time, networks, and maps. Students learn methods for static and interactive graphing and become familiar with tools for building web-browser-based presentations. Prerequisites: None
Credit 3 units. UColl: OLI
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U71 DATA 5025 Enterprise Data Management
Organizations have begun generating, collecting, and accumulating more data at a faster pace than ever before. The advent of "Big Data" has proven to be both opportunity and challenge for contemporary organizations who are awash-even drowning-in data but starved for knowledge. Unfortunately, organizations have not developed comprehensive enterprise data management (EDM) practices that treat data as a strategic imperative. EDM is a comprehensive approach to defining, governing, securing, and maintaining the quality of all data involved in the business processes of an organization. EDM enables data-driven applications and decision-making by establishing policies and ownership of key data types and sources. The ultimate goals are to create a strategic context for the technology underpinnings of data life cycle management and ensure good stewardship of an organization's data. This course will cover the critical components of building an enterprise data management practice including, but not limited to, data strategy, data governance, data security, data architecture, data quality, data ownership, and metadata management. This course includes case studies, lectures, and group activities to enhance the textbook material.
Credit 3 units. UColl: OLI
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U71 DATA 5030 Analytics Applications
This course focuses on the strategic, operational, tactical, and practical use of data analytics to inform decisions within an organization across a range of industry and government sectors as well as within organizational functions. Students will be introduced to specific analytics techniques that are used currently by practitioners in areas of diagnostic, descriptive, predictive, and prescriptive analytics. Students will learn the critical phases of analytics including data preparation, model development, evaluation, validation, selection, and deployment. In so doing, students will learn to apply data analytics in order to optimize organizational processes, improve performance, and inform decision-making.
Credit 3 units. UColl: OLI
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U71 DATA 5300 Introduction to Relational Databases and SQL Programming
The purpose of this course is to introduce the essential concepts behind relational databases, and help students acquire and apply foundational knowledge of the SQL language and Relational Database Management Systems (RDBMS). Students will study relational data models and discover how they are created and what benefits they bring, plus how to apply them to their own data. Additionally, students are exposed to other types of datastores like NoSQL and graph databases, and how to work with them. The emphasis in this course is on practical and hands-on learning. Through a series of labs, students will practice building and running SQL queries. Prerequisites: None
Credit 3 units. UColl: OLI
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U71 DATA 5740 Foundations of Data Analytics
Organizations are rapidly transforming the way they ingest, integrate, store, and serve data and perform analytics. In this course, students will learn the steps involved with designing and implementing data analytics and applications projects. Topics addressed include ingesting and parsing data from various sources, dealing with messy and missing data, transforming and engineering features, building and evaluating models, and visualizing results. Using Python, as well as other tools, students will complete assignments learning the process of building a data model as well as a variety of analytics techniques and under what situations they are best employed. Through lectures and practical exercises, students will become familiar with the computational mathematics that underpin analytics; the elements of statistical modeling and machine learning; model interpretation and assessment; and structured and unstructured data analysis. Prerequisites: None
Credit 3 units. UColl: OLI
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