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Academics

Requirements & Courses

  • Minor Requirements

    Twenty-six credits which must include:

    1. DAT 115: Introduction to Data Analytics (4 cr)
    2. DAT 315: Machine Learning (4 cr)
    3. Introduction to programming: DAT 116 or CSC 120 (4 cr)
    4. Upper level statistics: MTH 242 or  MTH 342 (4 cr)
    5. Data analytics project: DAT 385 or DAT 399 (2 cr)
    6. Data intensive electives: 8-cr from the following courses, at least 4 cr completed at the upper level: CSC 345, CSC 410, ECN 217, ECN 317, HCA 405, IPH 330, MTH 116, MTH 118, MTH 336, MTH 341, PHY 221, POL 316, PSY 220.
    7. Additional electives may be approved in consultation with the math and computer science faculty.

Courses

  • DAT
    115
    .
    Introduction to Data Analytics
    4 credits
    Introduction to graphs, calculations, and models for summarizing data, gaining insights from data, and making predictions. Discusses variation in data and how to ensure conclusions are justified. Example data sources include business, economics, medical studies, and sports statistics. Uses both a spreadsheet program, such as Microsoft Excel, and a statistics-oriented computing platform, such as R.
  • DAT
    116
    .
    Programming With Data
    4 credits
    Introduction to programming techniques for the manipulation and analysis of digital data. Programming topics include: digital representations of data, types of data, programming decision and repetition, functions and libraries for storing and manipulation data in the language of instruction (e.g. the pandas library of Python). Data topics include: common formats (e.g. CSV, JSON, XML, database), missing data, cleaning data, exploratory data analysis. Visualizing and presenting data to support an argument. Cross-listed with CSC-116.
  • DAT
    315
    .
    Machine Learning
    4 credits
    Prerequisite: DAT 116 or CSC 121, and MTH 242, or Permission
    Principles and techniques for machine-based decision and prediction from large datasets. Algorithms for and applications of classification, regression, and unsupervised learning. Introduction to neural networks and deep learning. Use of machine learning libraries in languages such as Python and R.
  • DAT
    385
    .
    Data Analytics Project
    2 credits
    Practicum in the field of Data Analytics.