Information Technology 375

ITEC 375: Data Science

Prerequisite: ITEC 120 (with a grade of "C" or better), either STAT 200 or STAT 301, and Junior Standing

Credit Hours: (3) Lecture

Serves as an introduction to the scientific processes of transforming data sets into actionable insight.

Note(s): Scientific and Quantitative Reasoning designated course.

 

Detailed Description of Course

Topics include:

1) Introduction to Data Science
        a. Data Science, analytics, and statistics
        b. Required skills: statistics, programming, problem domain

2) Available Tools
        a. R
        b. Statistical Analysis tools
        c. Watson, SPSS, Others

3) Data Cleaning
        a. Categorical vs continuous data
        b. Bad data

4) Exploratory Data Analysis
        a. Plots
        b. Correlations
        c. Factor Analysis

5) Introduction to Machine Learning
        a. Regression
        b. K-nearest neighbor (categorical), k-means (continuous)
        c. Naive Bayes

6) Feature Generation
        a. Decision Trees
        b. Decision Forests

7) Dimensionality Reduction
        a. Principle Component Analysis
        b. Support Vector Machines

8) Social Analysis
        a. Network Analysis
        b. Cluster Analysis
        c. Sentiment Analysis

9) Data Visualization
        a. Principles of visualization
       
Detailed Description of Conduct of Course

This course will be taught primarily in lecture mode, but as a workshop format. The content will be introduced as students will be encouraged to work through the content as it is introduced.


Goals and Objectives of the Course

Students who complete this course will be able to:

1) Explain data science to a non-user
2) Identify the best software platform for a data problem
3) Explain a problem domain for which data science can provide a solution
4) Identify the best analysis technique for any data set
5) Explain how to clean data sets to generate insight
6) Demonstrate how to take a data set and identify the potential insight
7) Generate high quality visualizations of data sets

Assessment Measures

Assessment of students achievement is measured by written tests and through projects and or homework assignments completed outside of class.


Other Course Information

None

 

Review and Approval

April 6, 2017

March 01, 2021