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Mauricio Featherman Marketing Analytics



featherman2012 Instructor Name: Mauricio Featherman, Ph.D.
Office Location: Todd Hall 440E
Contact Information: – 509.335.4445

Class time: Monday/Wednesday 9:10 am – 10:25 am, TODD 203

Office Hours: TBD and TODD 440E by appointment

Teaching Assistant  

Lab hours: TBD

Catalog Description: Advanced decision-making concepts utilizing relevant datasets for data-driven problem-solving and formulating decision analyses to evaluate and recommend management action (3 credits, no pre-requisite).


Building a Digital Analytics Organization:
Create Value by Integrating Analytical Processes, Technology,
and People into Business Operations 

by Judah Phillips 2013
ISBN-13: 978-0133372786  

Additionally, select online book chapters are utilized - 
see course schedule.
Use | Skillport | Books 24x7 for access 
to the textbooks. Locate the book chapter by searching 
for title or author.

Course Overview

This course is designed to equip students with knowledge, technical skills, and industry-perspective necessary to perform data-driven decision making. Good decision makers should be able to recognize and formulate decision problems, represent the essential structure of the decision situation, and analyze the problem using appropriate tools and techniques in order to recommend various courses of action. In this course you will learn to conduct data-based analysis using a variety of analytical tools and techniques. More specifically we will use Microsoft database, reporting, analysis, and visualization tools. The course is application oriented where students learn the key decision making concepts in common scenarios (production, inventory, sales, etc.) by analyzing datasets to solve business problems in a data-driven and iterative manner.

Course Objectives – Student Learning Outcomes

At the end of the class, students should be able to:

1. Define and structure a decision problem and its scope.
2. Identify external and internal organizational sources of data available for decision making.
3. Transform data into decisions using spreadsheet engineering, modeling, and analysis skills.
4. Explore, describe, and summarize data using statistical and visualization techniques.
5. Apply analytical tools and techniques to generate, evaluate and support courses of action based on data-driven analysis.
6. Analyze uncertainty and risk in business decisions and model assumptions and perform adequate sensitivity analyses.

Additional skills based learning outcomes include:

7. Become proficient in designing tabular data models using PowerPivot, and/or SSAS tabular data models
8. Become proficient in reporting and data visualization using Powerview, PowerPivot, SQLServer Reporting Services, Performance Point dashboards, Sharepoint, and Tableau.
9. Become proficient in using Excel-based data mining
10. Gain a perspective on the BI tools available and their appropriateness for Management support.
11. Gain a industry perspective by performing case based analysis and researching use-cases.

This course supports the learning goals of the Pullman MBA (specifically learning goal #2) as follows:

MBA Learning Objectives

Goal 1: Graduates of of the WSU MBA program will be able to formulate an actionable business strategy that is grounded in theory and practice from multiple business disciplines.
Goal 2: Graduates of the WSU MBA program will be able to conduct data-driven analyses to identify significant business problems, recommend feasible solutions to the problem, and justify a course of action.
Goal 3: Graduates of the WSU MBA program will be able to apply leadership theory to analyze business situations and develop theory-based recommendations.
Goal 4: Graduates of the WSU MBA program will be able to identify and evaluate the ethical, global, and societal implications of doing business as an organization.

Assessment of Student learning outcomes – the course requirements below provide the detailed grading rubric. Specifying the level of student success and mastery of the learning objectives an exam, case studies, hands-on technology-based projects, and submission of written reports.

Technology utilized  – In addition to providing a BI overview and deployment perspective, a review of BI systems planning and implementation best practices, and a grounding in descriptive and inferential statistics,  the course will provide a guided dive deep into Microsoft’s current state-of-the-art BI technology stack.

1) We will review SQLServer databases and database management systems, views and T-SQL using SQLServer Management Studio (SSMS)
2) We will review SQLServer Analysis Services (SSAS) using it to create tabular data models of data mart content
3) We will displaying the SSAS tabular results using Pivot tables, and PowerView in Excel
4) We will create interactive management reports using SQLServer Reporting Services (SSRS), Report Builder 3.0, and PowerView
5) We will create Dashboards and management scorecards using Sharepoint’s PerformancePoint or SSRS
6) We will use the MSFT-Excel tools for statistical analysis to run descriptive and inferential statistics.
7) We will use SSAS data mining (via an Excel add-in) to to employ advanced statistical techniques (regression, canonical correlation, etc.) to perform predictive analytics

Classroom Activities – classes are a mixture of discussion, hands-on demonstrations, student hands-on activities, student presentations, and guest speakers. The 75 minutes immediately following class are reserved for hands-on practice and completion of HW assignments, hosted by the instructor and/or class TA. You can help to make the class time epic by contributing your ideas, your insights, and your questions.

Course Requirements

Video Reviews 10% of grade
Hands-on Assignments 35% of grade
Case study review, Chapter Review 15% of grade
Exam 20% of grade
Participation & Attendance 5% of grade
Final project 15% of grade

For all out of class assignments that require a turn-in, be sure to upload your response to the correct dropbox. Email submission are not accepted or wise. Emails can get misplaced. If you cannot identify the correct dropbox to submit your grade, kindly notify the class teaching assistant.

Video Reviews  – Mixed in with the hands-on assignments, and book reading is another form of pedagogy and learning. Watching videos and taking notes of the presented material. Many BI & BA experts have posted excellent tutorials on Youtube. You are asked to watch a series of videos (each assignment are approximately 1 hour) and turn in your review of the content. Three video review assignment are each worth 3.33% of the total grade. Please provide a 2 or 3 page summary, interpretation and personal reaction to the video’s.

Hands-on assignments – To better understand BI systems, business analytics and the usage of statistics to solve business problems, a series of hands-on projects are assigned using Microsoft and Tableau BI technologies. You will be given software accounts, a friendly and knowledgeable professor and TA and lab time to work on these assignments. You may work alone, or in groups of 2 or 3. Either each student should upload to the CMS or when you upload, include each student name in the heading and submission information.  More information is found on the course schedule

Case reviews – Case studies will also be used to teach managerial aspects of defining, designing, and developing BI systems. Choose 3 mini use-cases from, or similar. You can also choose a case from Business Source Complete You can also search ‘IBM Analytics Case Studies’ or ‘Business Intelligence Case Studies’. You are asked to provide a verbal presentation of these cases, with slides for a total of 8 or 9 minutes.

For each company case, present a) the company name and problem, b) the IT strategy implemented and why it was chosen, c) the results and outcomes of the implemented technology strategy, d) lessons you learned (takeaways). So you just need 4 or 5 slides per mini use case. (no formal write-up is needed, the content will be graded live.) This is an individual assignment.

In addition, groups will be asked to present portions of the textbook chapters. This is a group-based assignment.

Exam – A tremendous amount of content will be presented in class, you are provided with one in-class exam that has two components a) short essay style exam that asks essay questions to assess your mastery of the database, data warehouse, visualization, T-SQL, etc. concepts covered in the first section of the class, b) a hands-on exam covering technologies covered to date.

Participation & Attendance – attendance will be taken daily. While hands-on night time labs are optional in-class attendance is mandatory. Notes will also be gathered that assess student preparedness, vocal participation and helpfulness in class.

Final project – Students are requested to complete an end of semester final project You may work alone or in groups of two. For groups of two the project will be graded after taking into account that twice the capacity is available, meaning that final projects should be twice the quality of a single person group. Each student group will conduct the following activities using the analytical tools covered in this course: Explore the data, analyze, summarize and visualize the problem to generate plausible solutions; provide recommendations regarding the various courses of action.

Your professor will provide the project and data. Currently the project examines Seattle crime using a large dataset.The projects are due the last day of finals week. Please submit all materials both by uploading to relevant servers (as designated) and provide a written report version that includes cropped screenshots of your analysis. Your project will be graded primarily using the written report which has the screenshots and full analysis. If any statistics are included, you can summarize the results in a sentence or two.

Letter grade equivalencies and Distribution:

Letter Grade Final Total %
A 93% – 100%
A- 90% – 92.9%
B+ 87% – 89.9%
B 83% – 86.9%
B- 80% – 82.9%
C+ 77% – 79.9%
C 73% – 76.9%
C- 70% – 72.9%
D+ 65% – 69.9%
D 60% – 64.9%
F below 60%

Grading Policy – Incompletes
Assigning Incompletes: University policy (Acad. Reg. #90) states that Incompletes may only be awarded if the student is unable to complete their work on time due to circumstances beyond their control. An incomplete may be granted only if less than 20% of the course content needs to be completed.

Attendance Policy
: There will be considerable amount of hands on work in class therefore students are expected to attend all the classes. Two class absences are excused, thereafter each class absence harms the student’s participation grade.

WSU Reasonable Accomodation Statement: Students with Disabilities: Reasonable accommodations are available for students with a documented disability. If you have a disability and need accommodations to fully participate in this class, please either visit or call the Access Center (Washington Building 217; 509-335-3417) to schedule an appointment with an Access Advisor. All accommodations MUST be approved through the Access Center. For more information contact a Disability Specialist on your home campus: 509-335-3417

WSU Academic Integrity Statement: Academic integrity will be strongly enforced in this course. Any student caught cheating on any assignment will be given an F grade for the course and will be reported to the Office Student Standards and Accountability. Cheating is defined in the Standards for Student Conduct WAC 504-26-010 (3). It is strongly suggested that you read and understand these definitions.

Safety and Emergency Notification: Washington State University is committed to enhancing the safety of the students, faculty, staff, and visitors. It is highly recommended that you review the Campus Safety Plan ( and visit the Office of Emergency Management web site ( for a comprehensive listing of university policies, procedures, statistics, and information related to campus safety, emergency management, and the health and welfare of the campus community.

Weather policy: For emergency weather closure policy, see: