ENGRD 2720

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ENGRD 2720

Course information provided by the Courses of Study 2023-2024.

An introduction to data science for engineers. The data science workflow: acquisition and cleansing, exploration and modeling, prediction and decision making, visualization and presentation. Tools for data science including numerical optimization, the Discrete Fourier Transform, Principal Component Analysis, and probability with a focus on statistical inference and correlation methods. Techniques for different steps in the workflow including outlier detection, filtering, regression, classification, and techniques for avoiding overfitting. Methods for combining domain-agnostic data analysis tools with the types of domain-specific knowledge that are common in engineering. Ethical considerations. Optional topics include classification via neural networks, outlier detection, and Markov chains. Programming projects in Python.

When Offered Fall, Spring.

Prerequisites/Corequisites Prerequisite: MATH 1920 and either CS 1110 or CS 1112.

View Enrollment Information

Syllabi: none
  •   Regular Academic Session.  Choose one lecture and one discussion. Combined with: ECE 2720

  • 4 Credits Graded

  •  9526 ENGRD 2720   LEC 001

  •  9527 ENGRD 2720   DIS 201

    • F Phillips Hall 213
    • Jan 22 - May 7, 2024
    • Krishnamurthy, V

  •  9528 ENGRD 2720   DIS 202

    • F Phillips Hall 307
    • Jan 22 - May 7, 2024
    • Krishnamurthy, V

  •  9529 ENGRD 2720   DIS 203

    • F Phillips Hall 213
    • Jan 22 - May 7, 2024
    • Krishnamurthy, V

  •  9682 ENGRD 2720   DIS 204

    • R Phillips Hall 213
    • Jan 22 - May 7, 2024
    • Krishnamurthy, V