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ANLY620 - Predictive Analytics

Course Details

Course Code: ANLY620 Course ID: 4877 Credit Hours: 3 Level: Graduate

This course gives emphasis to understanding how the predictive analytic approach flows, as well as the process of analysis starting with a problem, and through effective analytics approach that is cohesive and integrating of various statistical analysis tools for predicting behavior of variables in a modeled relationship. (PREREQUISITE: BUSN662)





Prerequisites

Course Schedule

Registration Dates Course Dates Session Weeks
04/29/19 - 10/04/19 10/07/19 - 12/01/19 Fall 2019 Session B 8 Week session
06/24/19 - 11/29/19 12/02/19 - 01/26/20 Fall 2019 Session D 8 Week session
09/30/19 - 02/28/20 03/02/20 - 04/26/20 Winter 2020 Session D 8 Week session

Current Syllabi

  1. Describe the factors in big data and its analytics lifecycles.
  2. Discuss the reviewing process of the basic data analytic methods within the Regression process.
  3. Explain the importance of the advanced analytical theory for products and services.
  4. Describe the analytical methods when using clusters, association rules, regression, and classifications.
  5. Explain how the forecasting techniques for time series throughout the predictive analysis process.
  6. Discuss the role of the analysts when applying MapReduce and Hadoop methods within a predictive development process.
  7. Discuss how predictive technology tools are used in communicating and operationalizing of the analytical project.
  8. Encourage students to apply what they have learned in Endgame plan format or putting it all together by analyzing data visually from project deliverables that are based on predictive analysis.

LESSONS AND READING

Students are required to actively participate reading and studying of the chapter materials so that they can analyze meaningful data and information by using predictive analytics methods and applications.

FORUMS

There are eight Forum Topics, in which are designed to promote interaction amongst fellow participants and to motivate or provoke other thoughts on the matter. This discussion format allows you to post and respond to other students within the convenient time frame of the weekly schedule. The study subject is graded in accordance with the assigned paragraph length requirements and required responses to at least two of your colleagues’ postings. These postings must add value and expand the conversion on the topic. Correspondent must interact with other participants throughout the Forum exercise to receive full participation credit.

1. The Main Response to the Discussion Question(s) must be written in a substantive manner with no less than 250 words that are relevant to the discussion topic(s). You must also include at least one scholarly source in your researched response.

2. Your interactive post should be at least 150-200 words that expand the conversation forward.

3. Please do not attach your responses, but make sure that you write within the body of the forum.

ASSIGNMENTS

There is one written assignment per week which is due at the end of the week. Your grades are based on the completion of the assigned assignment in accordance with the instructor’s lesson task requirements, and the use of the APA style guidelines. All the assignments must be uploaded into the Assignment Folder with your Turnitin.com submission results for the grading purpose.

ASSESSMENTS

Tests/Quizzes – These assessments will challenge the understanding of the class textbook material by the students. There may be questions from Predictive Analytics: Microsoft Excel’s topics. Assessments are configured as Problem Sets that will contain multiple-choice questions; or true and false, and essay or short answer format.

NameGrade %
Discussion Forum & Participations 20.00 %
Week 1 Discussion Forum 2.86 %
Week 2 Discussion Forum 2.86 %
Week 3 Discussion Forum 2.86 %
Week 4 Discussion Forum 2.86 %
Week 5 Discussion Forum 2.86 %
Week 6 Discussion Forum 2.86 %
Week 7 Discussion Forum 2.86 %
Case Study Analysis 20.00 %
Assignment 3 Week 3-Initializing Forecasting 6.67 %
Assignment 4 Week 4 - Predictive Regression and Classification Attributes 6.67 %
Assignment 7 Week 7- In-Database Analytics 6.67 %
Assignments 20.00 %
Week 8 Final Assignment 20.00 %
Problem Sets 7.50 %
Problem Set 1 1.88 %
Problem Set 2 1.88 %
Problem Set 5 1.88 %
Problem Set 6 1.88 %
Quizzes 7.50 %
Week 1 Quiz 1.88 %
Week 2 Quiz 2 1.88 %
Week 5 Quiz 5 1.88 %
Week 6 Quiz 6 1.88 %
Final Exam 25.00 %
Final Exam 25.00 %

Textbooks

  • Data Science & Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data (2015-Edition 1) by David Dietrich, Barry Heller and Beibei Yang i.: ISBN: 978-1-118-87613-8 ISBN: 978-1-118-87622-0 (ebk) ISBN: 978-1-118-87605-3 (ebk) https://www.vitalsource.com/referral?term=9781118876220.
  • Predictive Analytics: Microsoft Excel (2013-Edition 1) by Conrad Carlberg, Que Publishing PTG: eText ISBN: 9780132967259, 0132967251: https://www.vitalsource.com/referral?term=9780132967259.

These e-textbooks that are located inside of the Lessons section, which is set up as your weekly reading assignments. There are twelve chapters of this text material that is assigned as reading and posted on the Assignments and Forums sections. Additional reading materials will be assigned by the instructor, which includes but not limited to external periodical research Web sites that not are listed on the course syllabus.

Software Requirements

Book Title:Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data (ebook available through the APUS Online Library)
ISBN:9781118876138
Publication Info:Wiley
Author:EMC Education Services
Unit Cost:$49.40
Book Title:Predictive Analytics: Microsoft Excel
ISBN:9780789749413
Publication Info:QUE Publishing
Author:Conrad Carlberg
Unit Cost:$41.39

Previous Syllabi

Not current for future courses.