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    SUNY Broome Community College
   
 
  Jan 19, 2018
 
 
    
Website Catalog
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BIT 278 - Data Analytics II


This course provides an overview of data mining and predictive analytics, including large scale, enterprise level analytics.  The course structure follows the stages of a typical data mining project, from reading data, to data exploration, data transformation, modeling, and effective interpretation of results.  This course demonstrates how to develop models to predict categorical and continuous outcomes, using such techniques as neural networks and decision trees.  Students will have the opportunity to apply techniques learned to other fields of study through individualized projects in social, political, scientific, engineering or health information analytics.

Prerequisite- Corequisite
Prerequisite:  BIT 276 Data Analytics I

Credits: 3
Hours
3 Class Hours
Course Profile
Course Objectives:

1.  Provides an overview of data mining and predictive analytics, including large scale, enterprise level analytics.
2.  Teach students the stages of a typical data mining project, from reading data, to data exploration, data transformation, modeling, and effective interpretation of results.
3.  Teach students how to develop models to predict categorical and continuous outcomes, using such techniques as neural networks and decision trees.
4.  Demonstrate the application of techniques learned to other fields of study in social, political, scientific, engineering or health information analytics.

Learning Outcomes of the Course:

Upon successful completion of this course the student will be able to:

1.  Define the core terminology associated with data mining and predictive analytics.
2.  Apply data mining techniques and processes to unstructured data through the use of enterprise level tools such as SPSS-Modeler in order to solve business related problems through data modeling and the effective interpretation of results.
3.  Define and solve problems using predictive analysis techniques such as decision trees and neural networks.
4.  Apply techniques learned to an individualized project in business, social, political, scientific, engineering or health information analytics.



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