Program Overview

The applied data science minor program, based in the Case School of Engineering, includes faculty from schools across the university and provides courses in applied data science for undergraduates and graduate students from across the schools of the university.  The Applied Data Science program is directed to undergraduate and graduate students studying in the domains of Engineering and Physical Sciences (including Engineering, Energy and Manufacturing, Astronomy, Linguistics, Geology, Physics, and Chemistry), Health (including Translational and Clinical), Business (including Finance, Marketing, and Economics), and Social Sciences.

Successful completion of the Undergraduate Minor in Applied Data Science requirements leads to a "minor in applied data science" for the graduating student. The minor represents that the students have developed knowledge of the essential elements of Data Science and Analytics in the area of their major (their domain of expertise). 

Additionally, the Applied Data Science courses are offered as DSCI 4xx graduate level classes, in which graduate students additional work on a semester project related to their domain area or thesis research topic. 

Undergraduate Policies

For undergraduate policies and procedures, please review the Undergraduate Academics section of the General Bulletin.

Program Requirements

Elements of the Minor

The undergraduate minor in applied data science is structured so that the students who qualify for the minor have a working understanding of the basic ADS tools and their application in their domain area. This includes: 

• Formulate Data Science analyses of real-world datasets, to answer critical questions in various domain and application areas;

• Data Management: datastores, sources, streams; 

• High Performance and Distributed Computing: local computer, high performance computing clusters, distributed computing (such as Hadoop), or other cloud computing environments;

• Informatics, Ontology, Query: including search, data assembly, annotation; 

• Statistical Analytics: tools such as R statistics and high-level scripting languages (such as Python3); and

• Machine Learning and Deep Learning: Machine learning approaches such as support vector machines, or neural networks, and deep learning frameworks such as Keras and TensorFlow2.

The data types found in these domains are diverse. They include time series and spectral data for Energy, Physics, Chemistry and Astronomy, and sensor and production data and image and volumetric data for Manufacturing. In Health, Translational ADS includes Genomic, Proteomic, and other Omics data, while Clinical ADS includes patient data, medical data, physiological time series, and mobile data. And in Social Sciences natural language datasets, both written and oral. Business data types include stock and other financial market data for Finance, time series and cross-section data for Economics, and operations and consumer behavior data for Marketing.

Students will develop comprehensive experience in the steps of data analysis. 

• Define the Applied Data Science questions.

• Identify, locate, and/or generate the necessary data, including defining the ideal data set and variables of interest, determining and obtaining accessible data and cleaning the data in preparation for analysis.

• Exploratory data analysis to start identifying the significant characteristics of the data and information it contains.

• Statistical modeling, inference and prediction, including interpretation of results, challenging results, and developing insights and actions.

• Machine learning, deep learning and approaches to data visualization, images, natural language and artificial intelligence implementations.

• Synthesizing the results in the context of the domain and the initial questions, and writing this up.

• The creation of reproducible research, including code, datasets, documentation, and reports, which are easily transferable and verifiable.

• Communicating data science results in context, with consideration of privacy, openness, security, ethics, and value considerations. 

The ADS Minor Curriculum

The undergraduate minor curriculum is based on five 3 credit hour courses, with one class chosen from each of Levels 1 through Level 5, which cover the spectrum of learning needed to achieve domain area expertise in data science and analytics. The courses are chosen to be both cross-cutting, i.e., intermixing students from across the university in the fundamental concepts such as scripting and statistics (Levels 1, 2, and 4), and domain-focused (Levels 3 and 5). For the Level 5 advanced topics course, the research topic will be either a semester research project approved by the minor advisor, and will also be a 3-credit project, or an advanced data science topic class. This will provide minor students both the domain focused learning they need, and a broadening perspective on applications, methods, and uses of ADS in other domains.  

Courses Counted Toward Minor Requirements

Established courses included in the Minor are found in Case School of Engineering (Materials Science, Electrical Engineering and Computer Science, Manufacturing), College of Arts & Sciences (Mathematics, Astronomy, Philosophy, Cognitive Science); School of Medicine, Frances Payne Bolton School of Nursing, and Weatherhead School of Management (Marketing, Finance, Operations, and Economics) and the Jack, Joseph and Morton Mandel School for Applied Social Sciences.

The courses that meet the requirements for the Minor can also be taken by students to meet requirements in Major programs, and therefore serve a dual purpose in our academic offerings. However, each program, department, and school may have its own criteria on whether a given course could be "double counted" towards major and minor requirements. 

Level 5

Level 4

Level 3

Level 2

Level 1

The minor in Applied Data Science is based in the Case School of Engineering, and includes coursework from schools across the university.