Syllabus for DAA-703

DATA ANALYTICS & VISUALIZATION WITH CAPSTONE

COURSE DESCRIPTION

This course prepares students to access, analyze, manage, and present data to an organization’s decision makers. The focus of this course is to prepare students to effectively and efficiently use tools for data mining and data visualization. An essential skill within Business Intelligence (BI) is the ability to effectively communicate analysis, which includes providing a recommendation to decision makers. This course provides students the ability to do this in a test environment. The capstone project integrates all concepts learned with the use of a BI application.

COURSE TOPICS

Introduction to Information Technology (IT) and Business Intelligence (BI)
Data mining defined
Improving BI using effective BI systems
Successful steps for data mining
Essential approaches to data mining
Marketing, advertising, promotions, and pricing policies using econometric based modeling
Improving the web experience
Development and implementation of successful BI systems
Agile architecture framework for BI
Constructing an Enterprise Business Intelligence Maturity Model (EBI2M)
Strategic intelligence in corporate planning
Tactical intelligence in marketing
Operational intelligence in manufacturing
Financial intelligence in accounting
Future trends for data mining
Strategic intelligence in corporate planning

COURSE OBJECTIVES

After completing this course, you should be able to:

Evaluate data visualization fundamentals and apply them with data mining techniques.
Assess BI fundamentals and apply them with data mining techniques.
Explain the application data visualization software applications and justify how they may be used in industry.
Facilitate the application of BI software applications.
Analyze problem solving using data mining tool, and techniques.
Construct visualization data and assess BI in a real world scenario.

COURSE MATERIALS

You will need the following materials to complete your coursework. Some course materials may be free, open source, or available from other providers. You can access free or open-source materials by clicking the links provided below or in the module details documents. To purchase course materials, please visit the University's textbook supplier.

Required Textbook

Loshin, D. (2012). Business intelligence. Elsevier. ISBN-13: 978-0123858894

Mircea, M., Ghilic-Micu, B., & Stoica, M. (2012). An agile architecture framework that leverages the strengths of business intelligence, decision management and service orientation. In Marinela Mircea (ed.). Business Intelligence - Solution for Business Development. InTech. Available from: http://www.intechopen.com/books/business-intelligence-solution-for-business-development/an-agile-architecture-framework-that-leverages-the-strengths-of-business-intelligence-decision-manag

Min-Hooi Chuah and Kee-Luen Wong (2012). Construct an enterprise business intelligence maturity model (EBI2M) using an integration approach: A conceptual framework. In Marinela Mircea (ed.). Business Intelligence - Solution for Business Development. InTech. Available from: http://www.intechopen.com/books/business-intelligence-solution-for-business-development/construct-an-enterprise-business-intelligence-maturity-model-ebi2m-using-an-integration-approach-a-c

Guides, tutorials, and examples

A list of links to these is available at https://docs.google.com/a/tesc.edu/document/d/1Tl8-uDqxLKbkHpUFVTbbzOmLlfU2hE236U0bbNTua40/edit?usp=sharing

BI Software

Jaspersoft, available at http://www.jaspersoft.com/
Spago, available at http://www.spagoworld.org/xwiki/bin/view/SpagoBI/
Pentaho, available at http://www.pentaho.com/
Google Public Data at http://www.google.com/publicdata/directory
Datavisualization.ch Selected Tools at http://selection.datavisualization.ch/Data Visualization Software
Software: Predictive Model Markup Language (PMML) at http://www.dmg.org/

Supporting software can be found at http://www.dmg.org/products.html

Software: R Project for Statistical Computing at http://www.r-project.org/
Software: Hadoop at http://hadoop.apache.org/
Software: Augustus at https://code.google.com/p/augustus/

Installation manual: Augustus installation guide at http://augustus-docs.s3-website-us-east-1.amazonaws.com/
Modeling primer at: http://augustus-docs.s3-website-us-east-1.amazonaws.com/Primer/html/

Software: insider-R package reference at http://www.inside-r.org/packages
Software: Revolution R Enterprise for Academia at http://www.revolutionanalytics.com/downloads/
Software: R Programming Studio at http://rprogramming.net/download-and-install-rstudio/

Datavisualization.ch Selected Tools, available at http://selection.datavisualization.ch/

Datavisualization.ch is the premier news and knowledge resource for data visualization and infographics. This website provides a number of resources that are available for multiple OSs.

Software Title	Trial Package	Open Source Software	Supports Windows	Supports Mac	Supports Linux	Information About Software
R Language		X	X	X	X	R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.
Revolution R Enterprise for Academia		X	X		X	Revolution R Enterprise Academic edition, free to students and educators. Get the power of R language for data mining, predictive analytics.
R Programmming Studio		X	X	X	X	RStudio is a free and open source integrated development environment for R. You can run it on your desktop (Windows, Mac, or Linux)
Rapid Miner		X	X	X	X	RapidMiner, formerly YALE (Yet Another Learning Environment), is an environment for machine learning, data mining, text mining, predictive analytics, and business analytics. It is used for research, education, training, rapid prototyping, application development, and industrial application
Datavisualization.ch		X	X	X	X	News, people, event listings, tools and data sets, focusing on the domain of information visualization.
Jaspersoft	X		X	X	X	The JasperSoft Business Intelligence Suite provides integrated reporting, analysis, and data integration to make faster, better decisions
SpagoBI		X	X		X	SpagoBI is an Open Source Business Intelligence suite, belonging to the free/open source SpagoWorld initiative, founded and supported by Engineering Group. t offers a large range of analytical functions, a highly functional semantic layer often absent in other open source platforms and projects, and a respectable set of advanced data visualization features including geospatial analytics.
Pentaho	X		X		X	Pentaho is the business analytics company providing power for technologists and rapid insight for users.

COURSE STRUCTURE

Data Analytics & Visualization is a three-credit online course, consisting of six modules. Modules include an overview, topics, learning objectives, study materials, and activities. Module titles are listed below.

Module 1: Introduction and Defining BI
Course objectives covered in this module: 1, 2, 3, 4
Module 2: Steps to Data Mining
Course objectives covered in this module: 2, 3, 4, 5
Module 3: Business Applications for Data Mining and Visualization
Course objectives covered in this module: 1, 2, 3, 4, 5, 6
Module 4: Steps to Data Mining
Course objectives covered in this module: 1, 2, 3, 4
Module 5: Integrating BI in Multiple Disciplines
Course objectives covered in this module: 3, 4, 5
Module 6: Capstone Project & Future of BI and Data Mining
Course objectives covered in this module: 3, 4, 5, 6

BEFORE YOU START YOUR RESEARCH

One or more of the assignments in this course may involve original research. Research on persons other than yourself may require approval by the Institutional Review Board (IRB) of Thomas Edison State University prior to beginning your research. Examples of research types that may need IRB review are questionnaires, surveys, passive observation of individuals, interviews, and experimental procedures. Research involving vulnerable populations will always need IRB review. An IRB review is designed to protect research subjects from potential harm.

The following links fully explain the purpose of the Institutional Research Board as well as how to determine if your research requires IRB review. If you are in doubt, always ask for guidance from the University.

ASSESSMENT METHODS

For your formal work in the course, you are required to participate in online discussion forums, complete written assignments, take a proctored midterm examination, and complete a final project. See below for details.

Consult the Course Calendar for due dates.

Promoting Originality

One or more of your course activities may utilize a tool designed to promote original work and evaluate your submissions for plagiarism. More information about this tool is available in this document.

Discussion Forums

You are required to complete six graded discussion forums. For each discussion forum you are

required to make and initial post and then respond to posts made by your classmates.

Synchronous Event

One synchronous event will be held during module 5 (See course Calendar). During the live event, students will discuss a topic specified in the module details. To access the event, click the Collaboration Space link in the Edison Live! section of the course site a few minutes before the designated time. Use the following link for directions and helpful videos about how to use the Edison Live! tool in Moodle. Your mentor will work with the class to propose a time that works best and accommodates the majority.

Written Assignments

You will be required to submit two, three-page APA formatted written assignments. The first written assignment requires that you put forth the model, solutions, and process improvement for a given scenario. The second written assignment relates to data analytic software and will also make up a part of your final project.

Midterm Paper

You will prepare a 5-page APA formatted paper providing the larger picture of data mining and visualization. This report will include detailing a process of collecting data, mining data, and presenting data. Using existing data, you will be required to find and collect at least 2 statistical data sets and place them into a .csv file.

See additional information regarding the Midterm Paper in Module 3.

Capstone Project

You will create a data analytics and prepare a 7-10 page APA style report with the data used. The report will be a comprehensive marketing plan on the data collected from the midterm assignment. This plan will be targeted towards market intelligence, competitive intelligence, and other associated competitive marketing strategies that can be detailed with the use of data sets. Student will include a copy of the backend code or attach the file used to obtain visualizations so they can be rerun to ensure completeness.

See additional information regarding the Capstone Project in Module 6.

GRADING AND EVALUATION

Your grade in the course will be determined as follows:

Discussion forums (5)—30%
Written Assignments (2)—10%
Synchronous Event (1)—10%
Midterm paper—20%
Final project—30%

All activities will receive a numerical grade of 0–100. You will receive a score of 0 for any work not submitted. Your final grade in the course will be a letter grade. Letter grade equivalents for numerical grades are as follows:

A	=	93–100	B	=	83–87
A–	=	90–92	C	=	73–82
B+	=	88–89	F	=	Below 73

To receive credit for the course, you must earn a letter grade of C or higher on the weighted average of all assigned course work (e.g., assignments, discussion postings, projects, etc.). Graduate students must maintain a B average overall to remain in good academic standing.

STRATEGIES FOR SUCCESS

First Steps to Success

To succeed in this course, take the following first steps:

Read carefully the entire Syllabus, making sure that all aspects of the course are clear to you and that you have all the materials required for the course.
Take time to read the entire Online Student Handbook. The Handbook answers many questions about how to proceed through the course and how to get the most from your educational experience at Thomas Edison State University.
Familiarize yourself with the learning management systems environment—how to navigate it and what the various course areas contain. If you know what to expect as you navigate the course, you can better pace yourself and complete the work on time.
If you are not familiar with Web-based learning be sure to review the processes for posting responses online and submitting assignments before class begins.

Study Tips

Consider the following study tips for success:

To stay on track throughout the course, begin each week by consulting the Course Calendar. The Calendar provides an overview of the course and indicates due dates for submitting assignments, posting discussions, and scheduling and taking examinations.
Check Announcements regularly for new course information.

ACADEMIC POLICIES

To ensure success in all your academic endeavors and coursework at Thomas Edison State University, familiarize yourself with all administrative and academic policies including those related to academic integrity, course late submissions, course extensions, and grading policies.

For more, see: