Syllabus for DAM-702
Predictive Analytics for Business Intelligence
This course is intended for business students with these goals: 1) To provide the key methods of predictive analytics and advanced BI concepts; 2) To provide business decision-making context for these methods; 3) Using real business cases, to illustrate the application and interpretation of these methods. The course will cover R Programming, trends in predictive analytics, and understanding available application programs that can be deployed within the business enterprise.
After completing this course, you should be able to:
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.
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. |
Predictive Analytics for Business Intelligence is a three-credit online course, consisting of six modules. Modules include an overview, topics, study materials, and activities. Module titles are listed below.
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.
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.
You are required to complete five graded discussion forums. For each discussion forum you are
required to make and initial post and then respond to posts made by your classmates.
One synchronous event will be held during module 3 (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.
You will be required to submit one written assignment, which is designed to aid you in preparing for your midterm and final papers.
Using existing data, you will be asked to find and collect at least 2 substantial different statistical data sets and place them into a.csv file. You will then write a 5-page APA formatted paper on the importance of data science and predictive modeling in business today.
You will be asked to create a data analytics using Revolution R Enterprise for Academia or R Programming Studio. Additionally, you will prepare a 7-10 page APA formatted paper with the data used with Revolution R Enterprise for Academia. Report will cover the tool and interpretation of the data sets.
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 | = | 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.
To succeed in this course, take the following first steps:
Consider the following study tips for success:
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:
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