ADMN 898 (7ON) - Topics/Data Visualization

Durham   Paul College of Business&Econ :: Administration
Online Course Delivery Method: Online (no campus visits), EUNH
Credits: 3.0
Term: Fall 2018 - E-term I (08/13/2018 - 10/05/2018)
Grade Mode: Letter Grading
Class Size:   40  
CRN: 16979
Special topics; may be repeated. Prereq: consent of adviser and instructor.
Only listed majors in section: ANALYT DS CERT, ANALYTICS, ANALYTICS CERT, ANALYTICS PP, BUSINESS ADM M, BUSINESS ADM OL, BUSINESS ADM PT, BUSINESS ADMIN
Instructors: STAFF

Times & Locations

Start Date End Date Days Time Location
8/13/2018 10/5/2018 Hours Arranged ONLINE
Additional Course Details: 

With improvements in computing technology and the ability to generate/collect vast amounts of data, many organizations are quickly finding themselves data rich yet information poor. The goal of this course is to expose students to techniques and technologies that will enable them to become key players in helping organizations transform unstructured and structured data from various sources including, social media, the web, databases and archival data, into meaningful and insightful information that facilitates effective decision making.

COURSE OBJECTIVES

By the end of this course students should have an in depth understanding of the following:

1) Data extraction and cleansing:

a) Exploring and evaluating potential sources of data

b) Extracting and cleansing unstructured and structured data

c) Preparing data for storage or analysis

2) Data visualization and presentation:

a) Creating dashboards

b) Creating standard visualizations

c) Creating interactive visualizations

3) Data modeling and storage:

a) Modeling data (conceptual, logical and physical data modeling)

b) Designing and storing data in relational databases

This is a project-driven, hands-on course that is designed to give students exposure to real world data management challenges. Students taking the course will learn and demonstrate their skills in using the following technologies: MS Excel, Tableau, R and SQL.