ECE 29595 - Introduction to Data Science (Now runs as ECE 20875)

Lecture Hours: 1 Credits: 1

Counts as:
EE Elective

Experimental Course Offered: Spring 2018, Spring 2019

Requisites:
(ENGR 132, 142, or 162, C- or better) and (MA 16600, 16200, or 17300, C- or better) and (CS 15900 or 18000 or CNIT 10500 or 15501 or 17500), and College of Engineering student. THIS COURSE IS NOT SUITABLE FOR STUDENTS WHO HAVE COMPLETED ECE 30200 AND 36400.

Requisites by Topic:
Two semesters of calculus; complex numbers; computer literacy and experience with MatLab, Python, or similar programming language; some familiarity with vectors and matrices

Catalog Description:
This course provides a broad introduction to data analysis and modeling for Engineering majors. The course will be problem focused, and cover how to use data analysis and modeling algorithms, such as clustering, regression, hypothesis testing, etc., to solve interesting engineering problems. The course will use Python to teach how to write these analyses. There will be bi-weekly programming assignments (in Python) during the first part of the course to explore concepts, followed by an end-of-semester mini-project where the students will tackle a larger analysis and modeling problem. THIS COURSE IS NOT SUITABLE FOR STUDENTS WHO HAVE COMPLETED ECE 30200 AND 36400.

Required Text(s): None.

Recommended Text(s): None.

Learning Outcomes:

A student who successfully fulfills the course requirements will have demonstrated:
  1. an ability to write data analyses in Python.. [1,2]
  2. an ability to build statistical models and use them for prediction.. [1,6]
  3. an ability to design and apply analyses/models to solve engineering problems.. [1,2,3,4,5,6,7]

Lecture Outline:

Week(s) Lecture Topics
4 Python: NumPy, basic data structures, sorting, searching
2 Models, histograms, distributions
2 Regression
2 Estimation: sample mean/variance
2 Clustering: k-means, knn
1 Hypothesis testing
2 Using models: classification, detection, forecasting, prediction

Engineering Design Content:

Analysis
Testing
Evaluation

Engineering Design Consideration(s):

Social