"Leaders in business, education and government must take action to foster a new generation of talent with the technical expertise and unique ideas to make the most of this tsunami of Big Data."

-Richard Rodts, Manager of Global Academic Programs, IBM

Undergraduate Minor in Applied Data Science

To help bring the application of data science to a variety of fields, Case Western Reserve has developed a unique Applied Data Science undergraduate minor that can be paired with any undergraduate major at Case Western Reserve. The sequence launches in the fall of 2014.

This minor, based in the Case School of Engineering, is open to all undergraduate majors: engineering, arts, sciences, nursing and management.

Students can choose from eight subdomains in which to concentrate their minor, all of which include a core curriculum that includes five 3-credit courses.  

Domain areas available for minor concentration are:

Engineering and Physical Sciences

  • Energy
  • Manufacturing
  • Astronomy

Health

  • Translational
  • Clinical

Business

  • Finance
  • Marketing
  • Economics

The pathway towards earning the Applied Data Science minor is organized into five levels:

Level 1: Data Science Programming

Level 2: Inferential Statistics

Level 3: Exploratory Applied Data Science

Level 4: Undergraduate Applied Data Science Research

Level 5: Modeling & Prognosticshere 

Click here to download a general overview of the ADS Minor

Click here to download a list of the approved courses

 

Harnessing the resources of faculty expertise in materials science, electrical engineering and computer science, mechanical and aerospace engineering, systems biology, design and innovation, economics, finance and astronomy, the Applied Data Science minor teaches essential tools and applications within each domain area. This includes:

  • data management: Datastores, sources, streams
  • distributed computing: local and distributed computing (including Hadoop and other cloud computing)
  • informatics, ontology, query: including search, data assembly and annotation
  • statistical analytics: including tools such as high-level scripting languages such as R statistics, Python and Ruby

Students receiving the Applied Data Science minor will become fully proficient in all steps of data analysis and will be able to:

  • define the applied data science question
  • identify, locate and/or generate the data (including defining the ideal data set and variables; determining and obtaining accessible data; and cleaning the data in preparation for analysis)
  • exploratory data analysis (start identifying the significant characteristics of the data and information it contains)
  • statistical modeling and prediction (including interpretation of results, challenging results and developing insights and actions)
  • synthesizing the results (in context of the domain)
  • creation of reproducible research (including code and datasets, documentation and reports that are easily transferable and verifiable)