2014-2015 Official General Catalog [Archived Catalog]
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BIT 276 - Data Analytics I
This course will introduce students to data analysis and its applications in business analytics and business intelligence. Upon successful completion of this course students will be able to find, interpret, and convert raw unstructured data for use within spreadsheet, statistical, and database tools. An emphasis will be placed on using statistical tools useful for decision making, data modeling, and data visualization. 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
Prerequisites: BIT 200 Spreadsheets for Business Applications, BIT 260 Introduction to Database Management, MAT 181 Calculus or MAT 146 Applied Calculus, BUS 115 Business Statistics or MAT 124 Intro to Statistics
Credits: 3
Hours
3 Class Hours
Course Profile
Objectives of the Course:
1. Introduce students to data analysis and its applications in business analytics and business intelligence.
2. Teach processes, tools and techniques to convert raw unstructured data for use within spreadsheet, statistical, and database tools.
3. Teach how to use statistical tools and software packages for decision making, data modeling, and data visualization.
4. Demonstrate how techniques learned can apply to other fields of study such as 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 analysis.
2. Find, interpret, and convert raw unstructured data for use within spreadsheet, statistical, and database tools.
3. Use statistical tools for decision making, data modeling, and data visualization.
4. Apply data and text mining techniques and programs to unstructured data through the use of tools such as Google Chart API, R, Mathematica, Excel.
5. Use predictive analysis tools to forecast solutions to business opportunities.
6. Apply techniques learned to an individualized project in business, social, political, scientific, engineering or health information analytics.
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