ECE 20875 - Python for Data ScienceLecture Hours: 3 Credits: 3
Normally Offered: Each Fall, Spring
CS 15900 (minimum grade of C-)
Requisites by Topic:
This course will introduce Python programming to students through data science problems. Students will learn Python concepts as well as introductory data science topics, and will use their knowledge of Python (and prior programming experience) to implement data analyses.
For EE students, this course is an EE Elective for catalog terms prior to Fall 2019, and is a core course for Fall 2019 and later. For CMPE students, this course is an alternative to ECE 36400 for catalog terms prior to Fall 2019, and is a EE core course for 2019 and later.
- Python Essential Reference, 4th Edition, David M. Beazley, MacMillian, 2009, ISBN No. 978-0672329784.
- There will also be class notes distributed to cover the data science material for the course..
Learning Outcomes:A student who successfully fulfills the course requirements will have demonstrated:
- an understanding of regular expressions. [1,2]
- an ability to use Python to write data analyses. [1,2,6]
- an ability to explain when data analyses are appropriate. [1,2,3,6]
- an ability to explain the results of data analyses. [2,3,5]
- an ability to incorporate classes in their Python programs. [1,2,6]
- an ability to incorporate associative arrays in their programs. [1,2,6]
|1||Bash scripting, basic Python|
|2-3||Higher order functions, map/reduce, regular expressions, basic text processing|
|4-5||Regular expressions, N-gram analysis, probability distributions|
|6-7||Data structures: lists, arrays, associative arrays, sampling and estimation|
|8||Iterators and generators, regression|
|9||Objects, Clustering (k-means)|
|10||Classifiers (kNN, naive bayes)|
|11-12||Perceptrons, neural nets, PyTorch|
Engineering Design Content:
Engineering Design Consideration(s):