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

  1. Introduction to Information Technology (IT) and Business Intelligence (BI)
  2. Data mining defined
  3. Improving BI using effective BI systems
  4. Successful steps for data mining
  5. Essential approaches to data mining
  6. Marketing, advertising, promotions, and pricing policies using econometric based modeling
  7. Improving the web experience
  8. Development and implementation of successful BI systems
  9. Agile architecture framework for BI
  10. Constructing an Enterprise Business Intelligence Maturity Model (EBI2M)
  11. Strategic intelligence in corporate planning
  12. Tactical intelligence in marketing
  13. Operational intelligence in manufacturing
  14. Financial intelligence in accounting
  15. Future trends for data mining
  16. Strategic intelligence in corporate planning

COURSE OBJECTIVES

After completing this course, you should be able to:

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

COURSE MATERIALS

You will need the following materials to do the work of the course. The required textbook is available from the College’s textbook supplier, MBS Direct.

Required Textbook

Guides, tutorials, and examples

BI Software

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 (6) modules. Modules include an overview, topics, learning objectives, study materials, and activities. Module titles are listed below.

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.

Discussion Forums

You are required to complete six (6) graded discussion forums. For each discussion forum you are

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

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:

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–

=

80–82

A–

=

90–92

C+

=

78–79

B+

=

88–89

C

=

73–77

B

=

83–87

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:

Study Tips

Consider the following study tips for success:

ACADEMIC INTEGRITY

Students at Thomas Edison State College are expected to exhibit the highest level of academic citizenship. In particular, students are expected to read and follow all policies, procedures, and program information guidelines contained in publications; pursue their learning goals with honesty and integrity; demonstrate that they are progressing satisfactorily and in a timely fashion by meeting course deadlines and following outlined procedures; observe a code of mutual respect in dealing with mentors, staff, and other students; behave in a manner consistent with the standards and codes of the profession in which they are practicing; keep official records updated regarding changes in name, address, telephone number, or e-mail address; and meet financial obligations in a timely manner. Students not practicing good academic citizenship may be subject to disciplinary action including suspension, dismissal, or financial holds on records.

 

Academic Dishonesty

Thomas Edison State College expects all of its students to approach their education with academic integrity—the pursuit of scholarly activity free from fraud and deception. All mentors and administrative staff members at the College insist on strict standards of academic honesty in all courses. Academic dishonesty undermines this objective. Academic dishonesty can take the following forms:

Please refer to the Academic Code of Conduct Policy in the College Catalog and online at www.tesc.edu.

 

 

Plagiarism

Using someone else’s work as your own is plagiarism. Thomas Edison State College takes a strong stance against plagiarism, and students found to be plagiarizing will be severely penalized. If you copy phrases, sentences, paragraphs, or whole documents word-for-word—or if you paraphrase by changing a word here and there—without identifying the author, or without identifying it as a direct quote, then you are plagiarizing. Please keep in mind that this type of identification applies to Internet sources as well as to print-based sources. Copying and pasting from the Internet, without using quotation marks and without acknowledging sources, constitutes plagiarism. (For information about how to cite Internet sources, see Online Student Handbook > Academic Standards > “Citing Sources.”)

Accidentally copying the words and ideas of another writer does not excuse the charge of plagiarism. It is easy to jot down notes and ideas from many sources and then write your own paper without knowing which words are your own and which are someone else’s. It is more difficult to keep track of each and every source. However, the conscientious writer who wishes to avoid plagiarizing never fails to keep careful track of sources.

Always be aware that if you write without acknowledging the sources of your ideas, you run the risk of being charged with plagiarism.

Clearly, plagiarism, no matter the degree of intent to deceive, defeats the purpose of education. If you plagiarize deliberately, you are not educating yourself, and you are wasting your time on courses meant to improve your skills. If you plagiarize through carelessness, you are deceiving yourself.

For examples of unintentional plagiarism, advice on when to quote and when to paraphrase, and information about writing assistance and originality report checking, click the links provided below.

Examples of Unintentional Plagiarism 

When to Quote and When to Paraphrase

Writing Assistance at Smarthinking

Originality Report Checking at Turnitin

 

Disciplinary Process

First-time incidents of academic dishonesty concerning plagiarism may reflect ignorance of appropriate citation requirements. Mentors will make a good faith effort to address all first-time offenses that occur in courses. In these cases, the mentor may impose sanctions that serve as a learning exercise for the offender. These may include the completion of tutorials, assignment rewrites, or any other reasonable learning tool including a lower grade when appropriate. The mentor will notify the student by e-mail. Decisions about the sanctions applied for subsequent plagiarism offenses or other violations will be made by the appropriate dean’s office, with the advice of the mentor or staff person who reported the violation. The student will be notified via certified mail of the decision. Options for sanctions include:

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