GSBA 581 Information Management: Reading List



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University of Southern California

Marshall School of Business

IOM 599 Business Analytics

Fall 2013

Units: 3.0

Prerequisites: None

Schedule: Wednesday, 6:30-9:30pm

Office Hours: Wednesday, 9:30-10:00pm or by appointment

Professor Richard W. Selby

Bridge Hall 401

rselby@marshall.usc.edu

949-400-8941, cell

Course Introduction

Business analytics helps organizations achieve broad and deep understanding of and insights into markets, customers, operations, and suppliers. Business analytics provides benefits throughout all major functional areas of an organization including strategy, product development, marketing, operations, customer service, and finance. Business analytics is defined as the study, integration, and application of knowledge, skills, and methods for using data, statistical analysis, quantitative approaches, and predictive modeling to enable data-driven decision making and innovation in organizations. Organizations ranging from entrepreneurial start-ups to large global companies can innovate using business analytics to accelerate communication, enhance products, grow relationships, and operate in efficient, effective, and scalable manners. Business analytics aligns with and enables the “big data” initiatives that numerous organizations are undertaking.

Understanding the current and future ideas, strategies, and approaches for business analytics is essential and empowering for any student. This course investigates leading companies using business analytics and summarizes the foundational knowledge, skills, methods, tools, and resources that any student needs to know about business analytics. This course explains how to manage using analytics and provides an overall business analytics framework using an eight-point methodology for successfully implementing analytics-driven management and rapidly creating value. This course applies the framework to examine the use of business analytics by leading companies in eight major functional areas of an organization:

□ Strategy

□ Product development

□ Marketing

□ Social media

□ Mobile

□ Operations

□ Customer service

□ Financial reporting

In order to support the overall business analytics framework and methodology, this course teaches students how to use the SAS statistical data analysis package (), including hands-on skills for using SAS to implement strategies and approaches for defining, performing, and presenting business analytics.

This course does not assume prior knowledge of topics for business analytics, and there are no prerequisites. This course is open to all USC graduate students from all schools and all disciplines.

Business Analytics Framework

This course explains how to manage using analytics and provides an overall business analytics framework using an eight-point methodology for successfully implementing analytics-driven management and rapidly creating value. This course describes the applicability of the methodology to multiple major functional areas of an organization and illustrates how it facilitates “big data” initiatives. The following table summarizes the methodology and emphasizes its focus on:

□ Goal definition

□ Data collection

□ Data analysis and modeling

□ Interpretation, action, and feedback

|Analytics Methodology Feature |Example Techniques and Tools |Applicability to Functional |

| | |Areas of an Organization |

|Goal definition |Value propositions | |

|(1a) Define business-driven goals |Capabilities analysis | |

|(1b) Specify questions that support goals and |Productivity analysis |Strategy |

|focus on targeted opportunities and challenges |Profitability analysis | |

| |Prioritization and ranking | |

| |Question templates |Product development |

| | | |

| | | |

| | |Marketing |

| | | |

| | | |

| | |Social media |

| | | |

| | | |

| | |Mobile |

| | | |

| | | |

| | |Operations |

| | | |

| | | |

| | |Customer service |

| | | |

| | | |

| | |Financial reporting |

|Data collection |Metric definition and normalization | |

|(2a) Identify business metrics that provide data |Metric templates | |

|needed to answer questions and yield insights |Automated and manual data collection | |

|(2b) Execute data collection and validation |Data collection tools | |

|methods and tools |Process instrumentation | |

| |Data brokerages | |

| |Delphi techniques | |

| |Real-time data | |

|Data analysis and modeling |Statistical analysis techniques | |

|(3a) Analyze data, develop models, and assess |Model building and calibration | |

|limitations |Parametric modeling | |

|(3b) Integrate models into approaches for |Data visualization techniques | |

|visualizations, characterizations, predictions, |Dashboards | |

|and prescriptions |Control charts and limits | |

| |Collaborative filtering | |

| |SAS, Excel, and PowerPoint | |

|Interpretation, action, and feedback |Decision frameworks and criteria | |

|(4a) Interpret results and integrate actions in |Tradeoff and sensitivity analysis | |

|context of business goals |Data-driven decision making | |

|(4b) Feedback ideas for improvement and learning |Decision flow and workflow | |

| |Integration of data and processes | |

| |Process improvement | |

| |Organizational change | |

| |Closed-loop feedback | |

Business Analytics Tools and Skills

In order to support the overall business analytics framework and methodology, this course teaches students how to use the SAS statistical data analysis package (). This course explains hands-on skills for using SAS to implement strategies and approaches for defining, performing, and presenting business analytics in each of the framework methodology areas:

□ Goal definition

□ Data collection

□ Data analysis and modeling

□ Interpretation, action, and feedback

In particular, this course teaches the SAS skills needed for the following techniques:

□ Data manipulation

□ Data validation

□ Data visualization

□ Model building

□ Decision trees and networks

□ Regression

□ Classification analysis

□ Parametric and non-parametric tests

□ Control charts

□ Dashboards

□ Collaborative filtering

□ Design of experiments

□ Analysis of variance

This course integrates the use of SAS with other tools including Excel and PowerPoint.

Students apply the business analytics knowledge and skills they learn in class in a project using SAS. Students present their business analytics project in class to showcase their data-driven decision making and innovation. This project has five phases:

□ Phase 1: Define business analytics proposal, data required, data analysis approach, and decision making and innovation framework; In-class presentation

□ Phase 2: Define detailed plan for data collection and data analysis; In-class presentation and/or demonstration

□ Phase 3: Present and discuss data collected, data analysis, insights revealed, and actions taken; In-class presentation and/or demonstration

□ Phase 4: Present and discuss updates to data collected, data analysis, insights revealed, and actions taken; In-class presentation and/or demonstration

□ Phase 5: Finalize your business analytics project and data-driven decision making and innovation; In-class presentation and/or demonstration

Course Definition (for Course Catalog)

Foundational knowledge for business analytics, including strategies, methods, and tools integrated with hands-on skills for defining business analytics for data-driven decision making and innovation.

Learning Objectives

In this course students will learn:

□ Foundational knowledge, skills, methods, tools, and resources for business analytics

□ Understanding of ideas, strategies, and approaches for how leading companies use business analytics in multiple major functional areas of an organization

□ How to use the SAS statistical data analysis package () to implement strategies and approaches for business analytics

□ Hands-on skills for defining, performing, and presenting business analytics for data-driven decision making and innovation

Course Format

This course meets for 15 class sessions (except for any conflicts with holidays), and there is an additional session at the end for the final exam. Classes include a mixture of lectures, demonstrations, and discussions. Students are expected to read the materials in advance of class, come to class prepared to discuss the readings, apply the business analytics skills they learn in class in a project that uses the SAS statistical data analysis package () to define and perform business analytics, and present their business analytics project in class to showcase their data-driven decision making and innovation. Students are encouraged to collaborate with others on their project to foster ideas and get feedback for improvements.

Prerequisites

This course does not assume prior knowledge of topics for business analytics, and there are no prerequisites. This course is open to all USC graduate students from all schools and all disciplines.

Required Materials

Students are required to purchase a course reader from the USC Bookstore.

Course Reading Materials

The course reader provides the course reading materials. The reading materials describe business analytics topics, and the materials are as follows.

Class Session #1

Pre-class preparation

Case reading, analysis, and discussion: Strategy

□ Thomas H. Davenport, Marco Iansiti, and Alain Serels, “Managing with Analytics at Procter & Gamble”, Harvard Business School case, Article Product Number 9-613-045, April 3, 2013, 20 pages.

Reading and discussion

□ Thomas H. Davenport, Leandro Dalle Mule, and John Lucker, “Know What Your Customers Want Before They Do”, Harvard Business Review, Article Product Number R1112E, December 1, 2011, 8 pages.

Online e-learning

□ None

Project for business analytics using SAS

Hands-on project for learning SAS programming, business analytics, and “big data” applications

□ In Phase 1 of the project during class sessions #1-3, students will learn the SAS statistical data analysis package and focus on using SAS for data manipulation, data validation, and data visualization. Students will develop SAS programs that use SAS functions and capabilities to understand and benefit from “big data” applications using actual data from social media, markets, customers, products, and operations. Students will develop SAS programs to analyze structured and unstructured data from data-intensive industries, and they will explore the three V’s of “big data” -- volume, variety, and velocity of data.

Example data set application areas for using SAS

□ Strategy, product development, marketing, social media, mobile, operations, customer service, financial reporting

Skills for using SAS for business analytics and “big data”

□ Introducing the SAS statistical data analysis package (). Using SAS for data manipulation, data validation, and data visualization, including SAS programming, SAS functions, example data, and actual business data

Class Session #2

Pre-class preparation

Case reading, analysis, and discussion: Strategy

□ Christopher Malloy, “Recorded Future: Searching the Web for Alpha”, Harvard Business School case, Article Product Number 9-212-057, March 27, 2012, 16 pages.

Reading and discussion

□ David Kiron, Pamela Kirk Prentice, and Renee Boucher Ferguson, “Innovating with Analytics”, MIT Sloan Management Review, Article Product Number SMR433, October 1, 2012, 8 pages.

Online e-learning

□ Course: “Applied Analytics Using SAS Enterprise Miner”. Sub-course: “Introduction to Using SAS Enterprise Miner”.

Project for business analytics using SAS

Skills for using SAS for business analytics and “big data”

□ Using SAS for data manipulation, data validation, and data visualization, including SAS programming, SAS functions, example data, and actual business data

Class Session #3

Pre-class preparation

Case reading, analysis, and discussion: Product Development

□ Willy Shih, “Building Watson: Not So Elementary, My Dear!”, Harvard Business School case, Article Product Number 9-612-017, July 6, 2012, 19 pages.

Reading and discussion

□ Andrew McAfee and Erik Brynjolfsson, “Big Data: The Management Revolution”, Harvard Business Review, Article Product Number R1210C, October 1, 2012, 9 pages.

Online e-learning

□ Course: “Applied Analytics Using SAS Enterprise Miner”. Sub-course: “Pattern Discovery Using SAS Enterprise Miner”.

Project for business analytics using SAS

Skills for using SAS for business analytics and “big data”

□ Using SAS for data manipulation, data validation, and data visualization, including SAS programming, SAS functions, example data, and actual business data

Class Session #4

Pre-class preparation

Case reading, analysis, and discussion: Product Development

□ Alan MacCormack, Brian Kimball Dunn, and Chris F. Kemerer, “Barnes & Noble: Managing the E-Book Revolution”, Harvard Business School case, Article Product Number 9-613-073, March 5, 2013, 21 pages.

Reading and discussion

□ Thomas H. Davenport, Paul Barth, and Randy Bean, “How Big Data is Different”, MIT Sloan Management Review, Article Product Number SMR428, October 1, 2012, 6 pages.

Online e-learning

□ Course: “Applied Analytics Using SAS Enterprise Miner”. Sub-course: “Introduction to Predictive Modeling Using SAS Enterprise Miner”.

Project for business analytics using SAS

Hands-on project for learning SAS programming, business analytics, and “big data” applications

□ In Phase 2 of the project during class sessions #4-6, students will focus on using SAS for model building, decision trees and networks, and regression. Students will develop SAS programs that use SAS functions and capabilities to formulate models, customize them, and evaluate their descriptive and predictive effectiveness. The models will use actual data from “big data” applications, and the students will learn how to build, iterate, and improve models. Students will understand how their models developed using SAS create evidence-based answers that drive data-driven decision making and innovation in organizations.

Example data set application areas for using SAS

□ Strategy, product development, marketing, social media, mobile, operations, customer service, financial reporting

Skills for using SAS for business analytics and “big data”

□ Using SAS for model building, decision trees and networks, and regression, including SAS programming, SAS functions, example data, and actual business data

Class Session #5

Pre-class preparation

Case reading, analysis, and discussion: Marketing

□ Mikolaj Jan Piskorski and David Chen, “Social Strategy at American Express”, Harvard Business School case, Article Product Number 9-712-447, April 12, 2012, 24 pages.

Reading and discussion

□ Manish Goyal, Maryanne Q. Hancock, and Homayoun Hatami, “Selling into Micromarkets”, Harvard Business Review, Article Product Number R1207F, July 1, 2012, 9 pages.

Online e-learning

□ Course: “Applied Analytics Using SAS Enterprise Miner”. Sub-course: “Decision Tree Models Using SAS Enterprise Miner”.

Project for business analytics using SAS

Skills for using SAS for business analytics and “big data”

□ Using SAS for model building, decision trees and networks, and regression, including SAS programming, SAS functions, example data, and actual business data

Class Session #6

Pre-class preparation

Case reading, analysis, and discussion: Marketing

□ John Deighton and Leora Kornfeld, “Coca-Cola on Facebook”, Harvard Business School case, Article Product Number 9-511-110, May 6, 2011, 11 pages.

Reading and discussion

□ Emma K. Macdonald, Hugh N. Wilson, and Umut Konus, “Better Customer Insight - in Real Time”, Harvard Business Review, Article Product Number R1209H, September 1, 2012, 8 pages.

Online e-learning

□ Course: “Applied Analytics Using SAS Enterprise Miner”. Sub-course: “Regression Models Using SAS Enterprise Miner”.

Project for business analytics using SAS

Skills for using SAS for business analytics and “big data”

□ Using SAS for model building, decision trees and networks, and regression, including SAS programming, SAS functions, example data, and actual business data

Class Session #7

Pre-class preparation

Case reading, analysis, and discussion: Social Media

□ Francois Brochet and James Weber, “LinkedIn Corporation”, Harvard Business School case, Article Product Number 9-112-006, January 6, 2012, 37 pages.

Reading and discussion

□ Sinan Aral and Dylan Walker, “Forget Viral Marketing -- Make the Product Itself Viral”, Harvard Business Review, Article Product Number F1106Z, June 1, 2011, 3 pages.

Online e-learning

□ Course: “Applied Analytics Using SAS Enterprise Miner”. Sub-course: “Neural Networks and Other Models Using SAS Enterprise Miner”.

Project for business analytics using SAS

Hands-on project for learning SAS programming, business analytics, and “big data” applications

□ In Phase 3 of the project during class sessions #7-9, students will focus on using SAS for classification analysis, model building, and parametric and non-parametric tests. Students will develop SAS programs that use SAS functions and capabilities to implement classification and decision-making techniques including model definition, model training, model testing, prediction, and prescription. The students will develop these models using actual data from “big data” applications. Students will understand how to develop these models using SAS so they can monitor key business metrics and enable fact-based decision making by getting the right information to the right people at the right time.

Example data set application areas for using SAS

□ Strategy, product development, marketing, social media, mobile, operations, customer service, financial reporting

Skills for using SAS for business analytics and “big data”

□ Using SAS for classification analysis, model building, and parametric and non-parametric tests, including SAS programming, SAS functions, example data, and actual business data

Class Session #8

Pre-class preparation

Case reading, analysis, and discussion: Social Media

□ Mikolaj Jan Piskorski, David Chen, and Bill Heil, “Twitter”, Harvard Business School case, Article Product Number 9-710-455, November 15, 2011, 28 pages.

Reading and discussion

□ Wes Nichols, “Advertising Analytics 2.0”, Harvard Business Review, Article Product Number R1303C, March 1, 2013, 10 pages.

Online e-learning

□ Course: “Applied Analytics Using SAS Enterprise Miner”. Sub-course: “Model Assessment Using SAS Enterprise Miner”.

Project for business analytics using SAS

Skills for using SAS for business analytics and “big data”

□ Using SAS for classification analysis, model building, and parametric and non-parametric tests, including SAS programming, SAS functions, example data, and actual business data

Class Session #9

Pre-class preparation

Case reading, analysis, and discussion: Mobile

□ Mikolaj Jan Piskorski, Thomas Eisenmann, Jeffrey Bussgang, and David Chen, “foursquare”, Harvard Business School case, Article Product Number 9-711-418, November 2, 2011, 16 pages.

Reading and discussion

□ H. James Wilson, “You, By the Numbers”, Harvard Business Review, Article Product Number R1209K, September 1, 2012, 5 pages.

Online e-learning

□ Course: “Applied Analytics Using SAS Enterprise Miner”. Sub-course: “Model Implementation Using SAS Enterprise Miner”.

Project for business analytics using SAS

Skills for using SAS for business analytics and “big data”

□ Using SAS for classification analysis, model building, and parametric and non-parametric tests, including SAS programming, SAS functions, example data, and actual business data

Class Session #10

Pre-class preparation

Case reading, analysis, and discussion: Mobile

□ Lynda M. Applegate, Ramiro Montealegre, and Jeffrey Sweeney, “Riding the Wave of Technological Change at RE/MAX, LLC”, Harvard Business School case, Article Product Number 9-813-054, October 1, 2012, 21 pages.

Reading and discussion

□ Thomas H. Davenport, “Keep Up with Your Quants”, Harvard Business Review, Article Product Number R1307L, July 1, 2013, 5 pages.

Online e-learning

□ Course: “Applied Analytics Using SAS Enterprise Miner”. Sub-course: “Special Topics Using SAS Enterprise Miner”.

Project for business analytics using SAS

Hands-on project for learning SAS programming, business analytics, and “big data” applications

□ In Phase 4 of the project during class sessions #10-12, students will focus on using SAS for control charts, dashboards, and model building. Students will develop SAS programs that use SAS functions and capabilities to enable effective and efficient visualizations and interpretations of their data analyses. The students will apply these visualization and interpretation techniques to enable the integration of business data and business processes, and therefore, ensure that “big data” applications help drive business opportunities, innovations, and decisions.

Example data set application areas for using SAS

□ Strategy, product development, marketing, social media, mobile, operations, customer service, financial reporting

Skills for using SAS for business analytics and “big data”

□ Using SAS for control charts, dashboards, and model building, including SAS programming, SAS functions, example data, and actual business data

Class Session #11

Pre-class preparation

Case reading, analysis, and discussion: Operations

□ Willy Shih, Jyun-Cheng Wang, and Karen Robinson, “AmTran Technology Ltd.”, Harvard Business School case, Article Product Number 9-613-069, February 15, 2013, 18 pages.

Reading and discussion

□ Dominic Barton and David Court, “Making Advanced Analytics Work for You”, Harvard Business Review, Article Product Number R1210E, October 1, 2012, 7 pages.

Online e-learning

□ Course: “Applied Analytics Using SAS Enterprise Miner”. Sub-course: “Case Studies Using SAS Enterprise Miner”.

Project for business analytics using SAS

Skills for using SAS for business analytics and “big data”

□ Using SAS for control charts, dashboards, and model building, including SAS programming, SAS functions, example data, and actual business data

Class Session #12

Pre-class preparation

Case reading, analysis, and discussion: Operations

□ Juan Alcacer and Kerry Herman, “Intel: Strategic Decisions in Locating a New Assembly and Test Plant (A)”, Harvard Business School case, Article Product Number 9-713-406, April 25, 2013, 20 pages.

□ Juan Alcacer and Kerry Herman, “Intel: Strategic Decisions in Locating a New Assembly and Test Plant (B)”, Harvard Business School case, Article Product Number 9-713-419, September 19, 2012, 2 pages.

Reading and discussion

□ Shvetank Shah, Andrew Horne, and Jaime Capella, “Good Data Won't Guarantee Good Decisions”, Harvard Business Review, Article Product Number F1204A, April 1, 2012, 4 pages.

Online e-learning

□ Course: “Rapid Predictive Modeling for Business Analysts (EM 7.1)”.

Project for business analytics using SAS

Skills for using SAS for business analytics and “big data”

□ Using SAS for control charts, dashboards, and model building, including SAS programming, SAS functions, example data, and actual business data

Class Session #13

Pre-class preparation

Case reading, analysis, and discussion: Customer Service

□ Robert Simons and Natalie Kindred, “Agero: Enhancing Capabilities for Customers”, Harvard Business School case, Article Product Number 9-113-001, March 26, 2013, 22 pages.

Reading and discussion

□ Donald A. Marchand and Joe Peppard, “Why IT Fumbles Analytics”, Harvard Business Review, Article Product Number R1301H, January 1, 2013, 9 pages.

Online e-learning

□ Will be assigned in class

Project for business analytics using SAS

Hands-on project for learning SAS programming, business analytics, and “big data” applications

□ In Phase 5 of the project during class sessions #13-15, students will focus on using SAS for collaborative filtering, design of experiments, and analysis of variance. Students will develop SAS programs that use SAS functions and capabilities to define, compare, and quantify alternatives in a tradespace of possible outcomes for decisions, variations, or opportunities. The students will organize these tradespaces into experimentation approaches and use analysis of variance techniques on actual data from “big data” applications. Students will understand how to develop SAS programs that measure what matters most, reveal best actions, expose threats, and gain predictive insights that compel the right actions.

Example data set application areas for using SAS

□ Strategy, product development, marketing, social media, mobile, operations, customer service, financial reporting

Skills for using SAS for business analytics and “big data”

□ Using SAS for collaborative filtering, design of experiments, and analysis of variance, including SAS programming, SAS functions, example data, and actual business data

Class Session #14

Pre-class preparation

Case reading, analysis, and discussion: Customer Service

□ Robert F. Higgins, Penrose O'Donnell, and Mehul Bhatt, “Kyruus: Big Data's Search for the Killer App”, Harvard Business School case, Article Product Number 9-813-060, December 5, 2012, 29 pages.

Reading and discussion

□ Eric T. Anderson and Duncan Simester, “A Step-by-Step Guide to Smart Business Experiments”, Harvard Business Review, Article Product Number R1103H, March 1, 2011, 9 pages.

Online e-learning

□ Will be assigned in class

Project for business analytics using SAS

Skills for using SAS for business analytics and “big data”

□ Using SAS for collaborative filtering, design of experiments, and analysis of variance, including SAS programming, SAS functions, example data, and actual business data

Class Session #15

Pre-class preparation

Case reading, analysis, and discussion: Financial Reporting

□ Jaclyn Foroughi, Anne Casscells, and Maureen McNichols, “Tesla Motors - Evaluating a Growth Company”, Stanford Graduate School of Business case, Article Product Number A-209, May 17, 2013, 21 pages.

Reading and discussion

□ Arar Han and Ilya A. Strebulaev, “Sand Hill Angels: To Fund or Not To Fund”, Stanford Graduate School of Business case, Article Product Number E-442, September 5, 2012, 23 pages.

□ Arar Han and Ilya A. Strebulaev, “Sand Hill Angels Supplement to Pitches”, Stanford Graduate School of Business case, Article Product Number E-442T Supplement, September 5, 2012, 30 pages.

Online e-learning

□ Will be assigned in class

Project for business analytics using SAS

Skills for using SAS for business analytics and “big data”

□ Using SAS for collaborative filtering, design of experiments, and analysis of variance, including SAS programming, SAS functions, example data, and actual business data

Class Session #16

Final exam (cumulative)

Course Grading

Several dimensions of performance factor into the grades for students:

□ Business case analysis. Students will analyze two of the course’s business cases and create a PowerPoint summary for each case that captures the key issues and describes the ideas that can be transferred successfully to other companies. These analyses will provide students the opportunity to reveal insights about business analytics.

□ Project (using SAS). Students will apply the business analytics knowledge and skills they learn in class in a project that uses the SAS statistical data analysis package () to define and perform business analytics. Students will present their business analytics project in class to showcase their data-driven decision making and innovation. Students will be encouraged to collaborate with others on their project to foster ideas and get feedback for improvements. Guidance, instructions, demonstrations, and resources will be provided.

□ Quiz. In order to highlight key concepts as well as provide evaluative feedback to students, there will be a quiz. This quiz serves many purposes including facilitating focus on key principles, providing students an opportunity to share their understanding of the material, and identifying areas where further explanations are needed.

□ Exam. The final exam will be comprehensive and cover all materials presented in all classes. This exam will provide students the opportunity to demonstrate their knowledge of business analytics.

□ Class participation. Class participation will be assessed subjectively. All students will be expected to contribute to the class discussions.

The following table defines the detailed breakdown of the course grading.

|Grading Type |Description |Grade Breakdown |Grade Total |

|Business case analysis | | | |

| |Analysis of first business case |10% | |

| |Analysis of second business case |10% | |

| |Subtotal | |20% |

|Project (using SAS) | | | |

| |Phase 1: Define business analytics proposal, data required, data |5% | |

| |analysis approach, and decision making and innovation framework; | | |

| |In-class presentation | | |

| |Phase 2: Define detailed plan for data collection and data analysis; |5% | |

| |In-class presentation and/or demonstration | | |

| |Phase 3: Present and discuss data collected, data analysis, insights |5% | |

| |revealed, and actions taken; In-class presentation and/or demonstration| | |

| |Phase 4: Present and discuss updates to data collected, data analysis, |5% | |

| |insights revealed, and actions taken; In-class presentation and/or | | |

| |demonstration | | |

| |Phase 5: Finalize your business analytics project and data-driven |5% | |

| |decision making and innovation; In-class presentation and/or | | |

| |demonstration | | |

| |Subtotal | |25% |

|Quiz | | | |

| |Quiz |10% | |

| |Subtotal | |10% |

|Exam | | | |

| |Final exam |35% | |

| |Subtotal | |35% |

|Class participation | | | |

| |Class participation |10% | |

| |Subtotal | |10% |

|Total | |100% |100% |

Course Communication

Course communication occurs through the posting of class materials into Blackboard ( ), email, and announcements in class. All of the presentation materials will be posted into Blackboard, and class announcements will be sent via email using Blackboard. Therefore, all students are required to have an active Blackboard account that they use regularly and this account needs to define a correct email address.

Course Outline and Schedule

|Class Session |Topic: Readings and |Learning Objectives |Project Phase and SAS Skills |Project Assignments |Business Case |

| |Class Discussions | |for Business Analytics and | |Analysis, Quiz, |

| | | |“Big Data” Applications | |and Exam |

|Session #1. |Strategy |Understand foundational |In Phase 1 of the project |Define business | |

|Week of | |knowledge, skills, methods, |during class sessions #1-3, |analytics proposal, | |

|8/26/13. | |tools, and resources for business|students will learn the SAS |data required, data | |

| | |analytics for an organization’s |statistical data analysis |analysis approach, and| |

| | |strategy |package and focus on using |decision making and | |

| | |Understand ideas, strategies, and|SAS for data manipulation, |innovation framework | |

| | |approaches for how leading |data validation, and data | | |

| | |companies use business analytics |visualization. Students will| | |

| | |for an organization’s strategy |develop SAS programs that use| | |

| | |Understand how to define business|SAS functions and | | |

| | |analytics proposal, data |capabilities to understand | | |

| | |required, data analysis approach,|and benefit from “big data” | | |

| | |and decision making and |applications using actual | | |

| | |innovation framework |data from social media, | | |

| | |Understand introduction to the |markets, customers, products,| | |

| | |SAS statistical data analysis |and operations. Students | | |

| | |package and understand how to use|will develop SAS programs to | | |

| | |SAS for data manipulation, data |analyze structured and | | |

| | |validation, and data |unstructured data from | | |

| | |visualization |data-intensive industries, | | |

| | | |and they will explore the | | |

| | | |three V’s of “big data” -- | | |

| | | |volume, variety, and velocity| | |

| | | |of data. | | |

| | | |Phase 1, Session #1: | | |

| | | |Introducing the SAS | | |

| | | |statistical data analysis | | |

| | | |package (). Using| | |

| | | |SAS for data manipulation, | | |

| | | |data validation, and data | | |

| | | |visualization, including SAS | | |

| | | |programming, SAS functions, | | |

| | | |example data, and actual | | |

| | | |business data | | |

|Session #2. |(continued) |Understand foundational |Phase 1, Session #2: Using | | |

|Week of 9/2/13.| |knowledge, skills, methods, |SAS for data manipulation, | | |

| | |tools, and resources for business|data validation, and data | | |

| | |analytics for an organization’s |visualization, including SAS | | |

| | |strategy |programming, SAS functions, | | |

| | |Understand ideas, strategies, and|example data, and actual | | |

| | |approaches for how leading |business data | | |

| | |companies use business analytics | | | |

| | |for an organization’s strategy | | | |

| | |Understand how to use SAS for | | | |

| | |data manipulation, data | | | |

| | |validation, and data | | | |

| | |visualization | | | |

|Session #3. |Product development |Understand foundational |Phase 1, Session #3: Using |In-class presentation | |

|Week of 9/9/13.| |knowledge, skills, methods, |SAS for data manipulation, |of business analytics | |

| | |tools, and resources for business|data validation, and data |proposal, data | |

| | |analytics for an organization’s |visualization, including SAS |required, data | |

| | |product development |programming, SAS functions, |analysis approach, and| |

| | |Understand ideas, strategies, and|example data, and actual |decision making and | |

| | |approaches for how leading |business data |innovation framework | |

| | |companies use business analytics | | | |

| | |for an organization’s product | | | |

| | |development | | | |

| | |Understand how to use SAS for | | | |

| | |data manipulation, data | | | |

| | |validation, and data | | | |

| | |visualization | | | |

|Session #4. |(continued) |Understand foundational |In Phase 2 of the project |Define detailed plan | |

|Week of | |knowledge, skills, methods, |during class sessions #4-6, |for data collection | |

|9/16/13. | |tools, and resources for business|students will focus on using |and data analysis | |

| | |analytics for an organization’s |SAS for model building, | | |

| | |product development |decision trees and networks, | | |

| | |Understand ideas, strategies, and|and regression. Students | | |

| | |approaches for how leading |will develop SAS programs | | |

| | |companies use business analytics |that use SAS functions and | | |

| | |for an organization’s product |capabilities to formulate | | |

| | |development |models, customize them, and | | |

| | |Understand how to define detailed|evaluate their descriptive | | |

| | |plan for data collection and data|and predictive effectiveness.| | |

| | |analysis |The models will use actual | | |

| | |Understand how to use SAS for |data from “big data” | | |

| | |model building, decision trees |applications, and the | | |

| | |and networks, and regression |students will learn how to | | |

| | | |build, iterate, and improve | | |

| | | |models. Students will | | |

| | | |understand how their models | | |

| | | |developed using SAS create | | |

| | | |evidence-based answers that | | |

| | | |drive data-driven decision | | |

| | | |making and innovation in | | |

| | | |organizations. | | |

| | | |Phase 2, Session #4: Using | | |

| | | |SAS for model building, | | |

| | | |decision trees and networks, | | |

| | | |and regression, including SAS| | |

| | | |programming, SAS functions, | | |

| | | |example data, and actual | | |

| | | |business data | | |

|Session #5. |Marketing |Understand foundational |Phase 2, Session #5: Using | | |

|Week of | |knowledge, skills, methods, |SAS for model building, | | |

|9/23/13. | |tools, and resources for business|decision trees and networks, | | |

| | |analytics for an organization’s |and regression, including SAS| | |

| | |marketing |programming, SAS functions, | | |

| | |Understand ideas, strategies, and|example data, and actual | | |

| | |approaches for how leading |business data | | |

| | |companies use business analytics | | | |

| | |for an organization’s marketing | | | |

| | |Understand how to use SAS for | | | |

| | |model building, decision trees | | | |

| | |and networks, and regression | | | |

|Session #6. |(continued) |Understand foundational |Phase 2, Session #6: Using |In-class presentation | |

|Week of | |knowledge, skills, methods, |SAS for model building, |and/or demonstration | |

|9/30/13. | |tools, and resources for business|decision trees and networks, |of detailed plan for | |

| | |analytics for an organization’s |and regression, including SAS|data collection and | |

| | |marketing |programming, SAS functions, |data analysis | |

| | |Understand ideas, strategies, and|example data, and actual | | |

| | |approaches for how leading |business data | | |

| | |companies use business analytics | | | |

| | |for an organization’s marketing | | | |

| | |Understand how to use SAS for | | | |

| | |model building, decision trees | | | |

| | |and networks, and regression | | | |

|Session #7. |Social media |Understand foundational |In Phase 3 of the project |Present and discuss |Analysis of first|

|Week of | |knowledge, skills, methods, |during class sessions #7-9, |data collected, data |business case due|

|10/7/13. | |tools, and resources for business|students will focus on using |analysis, insights | |

| | |analytics for an organization’s |SAS for classification |revealed, and actions | |

| | |social media |analysis, model building, and|taken | |

| | |Understand ideas, strategies, and|parametric and non-parametric| | |

| | |approaches for how leading |tests. Students will develop| | |

| | |companies use business analytics |SAS programs that use SAS | | |

| | |for an organization’s social |functions and capabilities to| | |

| | |media |implement classification and | | |

| | |Understand how to present and |decision-making techniques | | |

| | |discuss data collected, data |including model definition, | | |

| | |analysis, insights revealed, and |model training, model | | |

| | |actions taken |testing, prediction, and | | |

| | |Understand how to use SAS for |prescription. The students | | |

| | |classification analysis, model |will develop these models | | |

| | |building, and parametric and |using actual data from “big | | |

| | |non-parametric tests |data” applications. Students| | |

| | | |will understand how to | | |

| | | |develop these models using | | |

| | | |SAS so they can monitor key | | |

| | | |business metrics and enable | | |

| | | |fact-based decision making by| | |

| | | |getting the right information| | |

| | | |to the right people at the | | |

| | | |right time. | | |

| | | |Phase 3, Session #7: Using | | |

| | | |SAS for classification | | |

| | | |analysis, model building, and| | |

| | | |parametric and non-parametric| | |

| | | |tests, including SAS | | |

| | | |programming, SAS functions, | | |

| | | |example data, and actual | | |

| | | |business data | | |

|Session #8. |(continued) |Understand foundational |Phase 3, Session #8: Using | |Quiz (covers |

|Week of | |knowledge, skills, methods, |SAS for classification | |class sessions |

|10/14/13. | |tools, and resources for business|analysis, model building, and| |1-7) |

| | |analytics for an organization’s |parametric and non-parametric| | |

| | |social media |tests, including SAS | | |

| | |Understand ideas, strategies, and|programming, SAS functions, | | |

| | |approaches for how leading |example data, and actual | | |

| | |companies use business analytics |business data | | |

| | |for an organization’s social | | | |

| | |media | | | |

| | |Understand how to use SAS for | | | |

| | |classification analysis, model | | | |

| | |building, and parametric and | | | |

| | |non-parametric tests | | | |

|Session #9. |Mobile |Understand foundational |Phase 3, Session #9: Using |In-class presentation | |

|Week of | |knowledge, skills, methods, |SAS for classification |and/or demonstration | |

|10/21/13. | |tools, and resources for business|analysis, model building, and|of data collected, | |

| | |analytics for an organization’s |parametric and non-parametric|data analysis, | |

| | |mobile presence |tests, including SAS |insights revealed, and| |

| | |Understand ideas, strategies, and|programming, SAS functions, |actions taken | |

| | |approaches for how leading |example data, and actual | | |

| | |companies use business analytics |business data | | |

| | |for an organization’s mobile | | | |

| | |presence | | | |

| | |Understand how to use SAS for | | | |

| | |classification analysis, model | | | |

| | |building, and parametric and | | | |

| | |non-parametric tests | | | |

|Session #10. |(continued) |Understand foundational |In Phase 4 of the project |Present and discuss | |

|Week of | |knowledge, skills, methods, |during class sessions #10-12,|updates to data | |

|10/28/13. | |tools, and resources for business|students will focus on using |collected, data | |

| | |analytics for an organization’s |SAS for control charts, |analysis, insights | |

| | |mobile presence |dashboards, and model |revealed, and actions | |

| | |Understand ideas, strategies, and|building. Students will |taken | |

| | |approaches for how leading |develop SAS programs that use| | |

| | |companies use business analytics |SAS functions and | | |

| | |for an organization’s mobile |capabilities to enable | | |

| | |presence |effective and efficient | | |

| | |Understand how to present and |visualizations and | | |

| | |discuss updates to data |interpretations of their data| | |

| | |collected, data analysis, |analyses. The students will | | |

| | |insights revealed, and actions |apply these visualization and| | |

| | |taken |interpretation techniques to | | |

| | |Understand how to use SAS for |enable the integration of | | |

| | |control charts, dashboards, and |business data and business | | |

| | |model building |processes, and therefore, | | |

| | | |ensure that “big data” | | |

| | | |applications help drive | | |

| | | |business opportunities, | | |

| | | |innovations, and decisions. | | |

| | | |Phase 4, Session #10: Using | | |

| | | |SAS for control charts, | | |

| | | |dashboards, and model | | |

| | | |building, including SAS | | |

| | | |programming, SAS functions, | | |

| | | |example data, and actual | | |

| | | |business data | | |

|Session #11. |Operations |Understand foundational |Phase 4, Session #11: Using | | |

|Week of | |knowledge, skills, methods, |SAS for control charts, | | |

|11/4/13. | |tools, and resources for business|dashboards, and model | | |

| | |analytics for an organization’s |building, including SAS | | |

| | |operations |programming, SAS functions, | | |

| | |Understand ideas, strategies, and|example data, and actual | | |

| | |approaches for how leading |business data | | |

| | |companies use business analytics | | | |

| | |for an organization’s operations | | | |

| | |Understand how to use SAS for | | | |

| | |control charts, dashboards, and | | | |

| | |model building | | | |

|Session #12. |(continued) |Understand foundational |Phase 4, Session #12: Using |In-class presentation | |

|Week of | |knowledge, skills, methods, |SAS for control charts, |and/or demonstration | |

|11/11/13. | |tools, and resources for business|dashboards, and model |of updates to data | |

| | |analytics for an organization’s |building, including SAS |collected, data | |

| | |operations |programming, SAS functions, |analysis, insights | |

| | |Understand ideas, strategies, and|example data, and actual |revealed, and actions | |

| | |approaches for how leading |business data |taken | |

| | |companies use business analytics | | | |

| | |for an organization’s operations | | | |

| | |Understand how to use SAS for | | | |

| | |control charts, dashboards, and | | | |

| | |model building | | | |

|Session #13. |Customer service |Understand foundational |In Phase 5 of the project |Finalize your business| |

|Week of | |knowledge, skills, methods, |during class sessions #13-15,|analytics project and | |

|11/18/13. No | |tools, and resources for business|students will focus on using |data-driven decision | |

|formal class. | |analytics for an organization’s |SAS for collaborative |making and innovation | |

|Use class time | |customer service |filtering, design of | | |

|to meet with | |Understand ideas, strategies, and|experiments, and analysis of | | |

|your classmates| |approaches for how leading |variance. Students will | | |

|and work on | |companies use business analytics |develop SAS programs that use| | |

|your projects. | |for an organization’s customer |SAS functions and | | |

| | |service |capabilities to define, | | |

| | |Understand how to finalize your |compare, and quantify | | |

| | |business analytics project and |alternatives in a tradespace | | |

| | |data-driven decision making and |of possible outcomes for | | |

| | |innovation |decisions, variations, or | | |

| | |Understand how to use SAS for |opportunities. The students | | |

| | |collaborative filtering, design |will organize these | | |

| | |of experiments, and analysis of |tradespaces into | | |

| | |variance |experimentation approaches | | |

| | | |and use analysis of variance | | |

| | | |techniques on actual data | | |

| | | |from “big data” applications.| | |

| | | |Students will understand how | | |

| | | |to develop SAS programs that | | |

| | | |measure what matters most, | | |

| | | |reveal best actions, expose | | |

| | | |threats, and gain predictive | | |

| | | |insights that compel the | | |

| | | |right actions. | | |

| | | |Phase 5, Session #13: Using | | |

| | | |SAS for collaborative | | |

| | | |filtering, design of | | |

| | | |experiments, and analysis of | | |

| | | |variance, including SAS | | |

| | | |programming, SAS functions, | | |

| | | |example data, and actual | | |

| | | |business data | | |

|Session #14. |(continued) |Understand foundational |Phase 5, Session #14: Using | |Analysis of |

|Week of | |knowledge, skills, methods, |SAS for collaborative | |second business |

|11/25/13. No | |tools, and resources for business|filtering, design of | |case due |

|formal class | |analytics for an organization’s |experiments, and analysis of | | |

|due to | |customer service |variance, including SAS | | |

|Thanksgiving. | |Understand ideas, strategies, and|programming, SAS functions, | | |

| | |approaches for how leading |example data, and actual | | |

| | |companies use business analytics |business data | | |

| | |for an organization’s customer | | | |

| | |service | | | |

| | |Understand how to use SAS for | | | |

| | |collaborative filtering, design | | | |

| | |of experiments, and analysis of | | | |

| | |variance | | | |

|Session #15. |Financial reporting |Understand foundational |Phase 5, Session #15: Using |In-class presentation | |

|Week of | |knowledge, skills, methods, |SAS for collaborative |and/or demonstration | |

|12/2/13. | |tools, and resources for business|filtering, design of |of your final business| |

| | |analytics for an organization’s |experiments, and analysis of |analytics project and | |

| | |financial reporting |variance, including SAS |data-driven decision | |

| | |Understand ideas, strategies, and|programming, SAS functions, |making and innovation | |

| | |approaches for how leading |example data, and actual | | |

| | |companies use business analytics |business data | | |

| | |for an organization’s financial | | | |

| | |reporting | | | |

| | |Understand how to use SAS for | | | |

| | |collaborative filtering, design | | | |

| | |of experiments, and analysis of | | | |

| | |variance | | | |

|Final exam. | |Final exam |

|Wednesday, | |(cumulative) |

|12/11/13, | | |

|7:00-9:00pm. | | |

USC and Marshall Policies

Add/Drop Process

The end of the third week of classes is the last day to add this class, and it is also the last day to drop this class without a mark of “W”. The end of the twelfth week of classes is the last day to drop this class with a mark of “W”.

Marshall Grading Guidelines

Marshall does not have a “curve” or hard target for the distribution of grades for individual assignments or the course as a whole. Our principle is that students should be given the grade they deserve based on class performance and should not be assigned an undeserved grade simply to fit a curve. Instructors determine what qualifies as an accurate grade. Historically, the mean GPA for graduate courses is 3.3 for core and 3.5 for electives.

Retention of Graded Coursework

Final exams and all other graded work which affected the course grade will be retained for one year after the end of the course if the exam or other graded work has not already been returned to the student. If the exam or other graded work has been returned to the student, it is the responsibility of the student to retain it if he or she desires to do so.

Technology Video/Audio Policy

Videotaping faculty lectures is not permitted, due to copyright infringement regulations. Audiotaping may be permitted if approved in advance by the professor. Use of any recorded material is reserved exclusively for USC students.

Statement for Students with Disabilities

Any student requesting academic accommodations based on a disability is required to register with Disability Services and Programs (DSP) each semester. A letter of verification for approved accommodations can be obtained from DSP. Please be sure the letter is delivered to the professor as early in the semester as possible. DSP is located in STU 301 and is open from 8:30am to 5:00pm Monday through Friday. The phone number for DSP is (213) 740-0776.

Statement on Academic Integrity

USC seeks to maintain an optimal learning environment. General principles of academic honesty include the concept of respect for the intellectual property of others, the expectation that individual work will be submitted unless otherwise allowed by an instructor, and the obligations both to protect one’s own academic work from misuse by others as well as to avoid using another’s work as one’s own. All students are expected to understand and abide by these principles. SCampus, the Student Guidebook, contains the Student Conduct Code (available at ).

Students will be referred to the Office of Student Judicial Affairs and Community Standards for further review should there be any suspicion of academic dishonesty. The Review process can be found at . Failure to adhere to the academic conduct standards set forth by these guidelines will not be tolerated by the USC Marshall community and can lead to dismissal.

Emergency Preparedness/Course Continuity

In case of an emergency that causes travel to campus to be difficult, USC executive leadership will announce an electronic way for instructors to teach students in their residence halls or homes using a combination of Blackboard, teleconferencing, and other technologies. Professors should be prepared to assign students a “Plan B” project that can be completed at a distance. For additional information about maintaining your classes in an emergency please see .

Incomplete Grades Explanation

An incomplete (IN) grade may be assigned due to an “emergency” that occurs after the twelfth week of classes. An “emergency” is defined as a serious documented illness or an unforeseen situation beyond the student’s control that prevents a student from completing the semester. Prior to the twelfth week, the student still has the option of dropping the class. Arrangements for completing an IN course should be initiated by the student and negotiated with the professor. Class work to complete the course should be completed within one calendar year from the date the IN was assigned. The IN mark will be converted to an F grade should the course not be completed.

Sexual Harassment

USC policies prohibit sexual harassment. According to Faculty Handbook 2008 Section 6-D, sexual harassment consists of unwelcome sexual advances, requests for sexual favors, and other verbal or physical conduct of a sexual nature when: (a) submission to such conduct is either explicitly or implicitly made a term or condition of an individual’s employment, appointment, admission, or academic evaluation; (b) submission to such conduct is used as a basis for evaluation in personnel decisions or academic evaluations affecting an individual; or (c) such conduct has the purpose or effect of unreasonably interfering with an individual’s work or academic performance, or creating an intimidating, hostile, or offensive working or learning environment.

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