Business Intelligence & Data Mining with SAS Suite

Course Syllabus Page 1

Carnegie Mellon University, Heinz College

Spring 2020 (94-832)

Course Information

Course

Course Number/Section 94-832

Course Title Business Intelligence & Data Mining with SAS Suite

Dates and Room Mons/Weds 10:30-11:50am (Section B4), HbH 1206

Professor Contact Information

Professor Beibei Li [email protected]

Office Location HbH 2118G

Office Hours Mons/Weds 12pm-1pm (or by individual appointment)

TA Information Zhenwei Feng [email protected] TBD

Course Pre-requisites, Co-requisites, and/or Other Restrictions

No official prerequisite, preferably some knowledge in statistics, economics and database.

Course Description

With the proliferation of mobile, IoT, and Web 2.0 making inroads into the enterprises and industries, the

ability to understand, analyze and interpret businesses from large-scale and fine-grained data has become

increasingly more important today. This class aims to equip you with highly demanded business analytics

skills in the current job market. The course will focus on extracting business intelligence by leveraging

firm's business data as well as online social media content for various applications, including (but not

limited to) search engine marketing, social media analytics, crowd-sourcing management, FinTech and

market analysis, demand estimation, social network analysis, customer segmentation, customer

relationship management (CRM), web mining and health care management.

The class will be hands-on and the emphasis will be placed on the "know-how" aspect - how to extract and

apply business intelligence to improve business decision making and marketing strategies. We will analyze

real-world business data from Fortune 500 companies using various business intelligence tools, primarily

SAS Enterprise Miner. Time permits, we will also introduce some advanced economic and predictive

models in analyzing digital markets. Prior programming skill is not required. Throughout the class business

insights from several market leaders such as Google, Microsoft, Amazon, Travelocity, Netflix, Uber, Yelp

and Facebook will be revealed.

Class Objectives

• Differentiate, design and assess various business intelligence (BI) and data mining models.

• Identify and translate real-world business problems into BI and data mining problems.

• Exhibit ability in pre-preparing and visualizing the right data towards these problems.

• Implement efficient BI strategies to solve these problems.

• Develop proficiency in BI software (SAS Enterprise Miner).

• Enhance knowledge and skills in the current trends in the management and use of BI.

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Carnegie Mellon University, Heinz College

Spring 2020 (94- 832 )

Course Information

Course

Course Number/Section 94 - 832 Course Title Business Intelligence & Data Mining with SAS Suite Dates and Room Mons/Weds 10:30-11:50am (Section B4), HbH 1206 Professor Contact Information Professor Beibei Li [email protected] Office Location HbH 2118G Office Hours Mons/Weds 12 pm-1pm (or by individual appointment) TA Information Zhenwei Feng [email protected] TBD Course Pre-requisites, Co-requisites, and/or Other Restrictions No official prerequisite, preferably some knowledge in statistics, economics and database. Course Description With the proliferation of mobile, IoT, and Web 2.0 making inroads into the enterprises and industries, the ability to understand, analyze and interpret businesses from large-scale and fine-grained data has become increasingly more important today. This class aims to equip you with highly demanded business analytics skills in the current job market. The course will focus on extracting business intelligence by leveraging firm's business data as well as online social media content for various applications, including (but not limited to) search engine marketing, social media analytics, crowd-sourcing management, FinTech and market analysis, demand estimation, social network analysis, customer segmentation, customer relationship management (CRM), web mining and health care management. The class will be hands-on and the emphasis will be placed on the "know-how" aspect - how to extract and apply business intelligence to improve business decision making and marketing strategies. We will analyze real-world business data from Fortune 500 companies using various business intelligence tools, primarily SAS Enterprise Miner. Time permits, we will also introduce some advanced economic and predictive models in analyzing digital markets. Prior programming skill is not required. Throughout the class business insights from several market leaders such as Google, Microsoft, Amazon, Travelocity, Netflix, Uber, Yelp and Facebook will be revealed. Class Objectives

Differentiate, design and assess various business intelligence (BI) and data mining models.
Identify and translate real-world business problems into BI and data mining problems.
Exhibit ability in pre-preparing and visualizing the right data towards these problems.
Implement efficient BI strategies to solve these problems.
Develop proficiency in BI software (SAS Enterprise Miner).
Enhance knowledge and skills in the current trends in the management and use of BI.

Recommended Textbooks and Materials

Recommended Textbook (Optional)

Data Mining Techniques: For Marketing,

Sales, and Customer Relationship

Management, 3rd Edition

Gordon Linoff and Michael Berry,

2011 , Wiley,

ISBN: 0470650931

Deliverables and Grading

Assignment submission instructions You will submit your assignments (in the required file format with a simple file name and a file extension) by using the Assignments tool at Blackboard. For the team project assignment, one group member will submit the assignment for the group and all group members will be able to view the results and feedback once it’s been graded. Instructions for each assignment will be posted to Blackboard and the rubric will be provided in class. Assignments are due on the dates stipulated by the instructor on the syllabus or in class. Assignments will not be accepted past the due date and time unless a religious observance or a documented medical condition prevents on-time submission and the student has consulted with the instructor in advance for approval of an alternate deadline. Class participation Your class participation is extremely important for your final grade. The grade I assign for your participation is a careful, subjective assessment of the value of your input to classroom learning. I keep track of your contributions towards each class, and these contributions include (but are not restricted to):

Attend class on time.
Participate in class discussions of case studies and assigned readings.
Respond to general and individual questions based on assigned readings.
Raise questions that make your classmates think.
Provide imaginative yet relevant analysis of a situation.
Contribute background or a perspective on a classroom topic that enhances its discussion.
Answer questions raised in class.
Class presentation. We’ll have a list of extremely useful readings/cases on which students are highly encouraged to lead the discussions in class. Each team will also get an opportunity to present your group projects in class. Emphasis is placed on the quality of your contribution, rather than merely on its frequency. A lack of preparation, negative classroom comments, or improper behavior (such as talking to each other, sleeping in the classroom or walking in and out of the class while the lecture is in process) can lower this grade. Regular attendance and participation are very important.

Any attempt to represent the work of others as your own will be considered plagiarism and will be referred to the CMU Discipline Committee. Penalties determined by this committee range from academic probation to expulsion. It is in your best interest to submit nothing or a partial assignment, rather than an assignment copied in violation of the honor code. Canvas Much of the class information can be found at the Canvas Portal. Students will use their CMU account to login to the course at: http://www.cmu.edu/canvas/. The data which can be found there include the class schedule, assignments, the lecture slides and class news. This is also where you will access the Discussion Board. Posting comments on the discussion board will be counted towards class participation.

Class Schedule

Week Date Topic Work Due 1

Course Overview: Data mining theory & methodology Translating business problems into data mining problems; BI & DM overview; Model prediction & assessment; BI case studies; Install SAS EM; Get Familiar with SAS EM Environment; 2

SAS EM Analytics Scheme: SEMMA palette for BI. Lab 1: SEMMA SEMMA palette; HW 1 3

Data Exploration and Pattern Discovery: Developing Intuition about data; Market Basket Analysis; Association Rules; Clustering and segmentation; Using SAS EM for pattern recognition. Lab 2: Association Mining and Link Graph HW 2 4

Predictive Modeling (1): Classification and predictive modeling; Decision trees as classification tools; Using SAS EM to build decision trees. Lab 3: Predictive Modeling (Decision Trees, Model Comparison) HW 3 5

Predictive Modeling (2): Regression Models (Linear & Logistic); Using SAS EM to build regression models. Lab 4: Predictive Modeling (Regression, Variable Selection, Missing Value, Transforming Variable) HW 6

Group Project Meeting (No Class on 4/20) Comprehensive Quiz; Quiz 7

Final Team Presentation Final Presentation 8 Exam week No Final Exam Final Report Due

Business Intelligence & Data Mining with SAS Suite, Lecture notes of Calculus

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Carnegie Mellon University, Heinz College