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Data Analytics

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The Data Analytics Concentration
The Data Analytics (DA) concentration prepares students from a wide range of disciplines to use data to address problems in their chosen disciplines and in multidisciplinary settings. The concentration provides understanding of and computational skills necessary to do data analysis, modeling and simulation, data visualization, and more generally to understand the concept of data-driven decision making and predictions about the future. Students learn various tools that can be used to make sense of data and how to identify the ways in which data are used to manipulate the message conveyed by data. The concentration also addresses issues of algorithmic bias, data ethics, and the power exercised by those who control data and make decisions about its use.

Requirements

Additional courses that will be added to this new concentration in the coming year will address topics such as modeling and simulation, data visualization, algorithmic bias, and machine learning.

  • Moderation Requirements
    Courses needed to moderate:
    • CMSC 121, Introduction to Data Analytics and R Programming
    • One of the below statistics-oriented courses:
      • CMSC 275, Statistics for Computing
      • BIO 244, Biostatistics
      • PSY 202, Design and Analysis in Psychology II
      • ECON 229, Introduction to Econometrics
      • Introduction to Statistics (new course)
      • PHYS 221 or 222, Mathematical Methods I + II
      • ES/EUS 240, Statistics and Econometrics
  • Graduation Requirements
    Courses needed to graduate:
    • CMSC 121, Introduction to Data Analytics and R Programming
    • One of:
      • CMSC 275, Statistics for Computing
      • BIO 244, Biostatistics
      • PSY 202, Design and Analysis in Psychology II
      • ECON 229, Introduction to Econometrics
      • Introduction to Statistics (new course)
      • PHYS 221 or 222, Mathematical Methods I + II
      • ES/EUS 240, Statistics and Econometrics
    • At least two of:
      • Modeling and Simulation (new course)
      • Data Visualization (new course)
      • Data Mining (new course)
      • ES/EUS 321, GIS for Environmental Justice
      • Spatial Analysis (new course)
    • At least one course outside of the Division of Science, Mathematics, and Computing that involves significant data analysis, chosen with approval of the DA concentration director. Examples are:
      • HR 278, A Human Right to Homes or Homelessness
      • Common Courses, Data and Democracy (new course)
      • SOC 138, Introduction to Urban Sociology
      • ES/EUS 113, Introduction to Geography and Geosciences
      • GIS 269/Sociology 269, Global Inequality and Development
    • Senior Project in the major discipline that includes a significant data analysis component, likely involving the combination of multiple data sources and development of an analytical approach in order to address a question or problem rooted in the major field.

Faculty

Valerie Barr – Computer Science (director)
Sven Anderson – Computer Science
Jordan Ayala – Data Analytics; Environmental Studies
Beate Liepert – Environmental Studies; Physics
Allison Stanger – Technology and Human Values; Hannah Arendt Center