ECE 2720

ECE 2720

Course information provided by the Courses of Study 2024-2025.

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. Corequisite: MATH 2940.

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

  • 4 Credits Graded

  •  7159 ECE 2720   LEC 001

    • MW Olin Hall 165
    • Aug 26 - Dec 9, 2024
    • Acharya, J

  • Instruction Mode: In Person
    Corequisite: MATH 2940.

  •  7160 ECE 2720   DIS 201

  • Instruction Mode: In Person

  •  7224 ECE 2720   DIS 202

  • Instruction Mode: In Person

  •  7349 ECE 2720   DIS 203

  • Instruction Mode: In Person