Catalog Course Search Details

 Department:    IMDS

 Course #:    240

 Credits:    5

 Variable:     No

 IUs:    5

 CIP:    30.7101

 EPC:    573

 REV:    2021

 Course Description  

Learn to build and derive insights from data science using modern approaches. Introduction to machine learning concepts and how to use machine learning to prepare data for modeling including key concepts in data acquisition, preparation, exploration and visualization, and how to build a cloud data science solution using cloud computing services, R, and Python.

 Prerequisite  

Prerequisites: IMDS 101 and IMDS 105 and IMDS 120 all with a grade of C or higher.

Contact Hours (based on 11 week quarter)

Lecture: 55

Lab: 0

Other: 0

Systems: 0

Clinical: 0

Intent: Distribution Requirement(s) Status:  

Vocational Preparatory Required for ATA degree  

Equivalencies At Other Institutions

Learning Outcomes

After completing this course, the student will be able to:

1. Describe key concepts of data science and machine learning.
2. Prepare data for predictive modeling.
3. Visualize and explore data.
4. Create supervised and unsupervised machine learning models.
5. Publish machine learning web services.

General Education Learning Values & Outcomes

Revised August 2008 and affects outlines for 2008 year 1 and later.

Course Contents

1. Getting started with data science and machine learning.
2. Acquiring data, transforming data, data cleansing and transformation.
3. R and Python for data science.
4. Data exploration and visualization, building and evaluating models.
5. Working with regression, classification, and unsupervised learning models.
6. Working with recommendation models.
7. Publishing models using web services.