Courses

Applied Predictive Analytics I
Professor: Richard Briesch, Professor of Marketing, Corrigan Research Professor, Research Fellow, National Center for Arts Research. http://www.smu.edu/Cox/Departments/FacultyDirectory/BrieschRichard

Textbook: Econometric Analysis (7th Edition) by Greene; The Little SAS Book: A Primer, Fifth Edition by Delwiche and Slaughter.
Software: SAS.
Contents: Endogeneity/Simultaneous Equations, Sample Selection Correction/Tobit, Information Criteria/Model selection, Likelihoods.
Tips: This is a basically Econometrics class with SAS application. You’d better to be familiar with basic SAS coding skills, because the Professor won’t go through every detail in SAS coding, especially the basic data manipulation skills. For the theory part, the textbook is too heavy and mathematical for the class contents. So just use it as a reference book.

Business Metrics
Professor: Jacquelyn Thomas, Associate Professor of Marketing, Frank and Susan Dunlevy Faculty Fellow
http://www.smu.edu/Cox/Departments/FacultyDirectory/ThomasJacquelyn

Textbook: Marketing Metrics, Paul Farris, Neil Bendle, Philip Pfeifer, and David Reibstein
Software: Excel - Pivot tables, Regression
Contents: Through the use of problem sets and business cases, the course focuses on the calculation of financial, marketing and operational metrics and demonstrate contexts in which managers would rely upon such metrics for decision-making.
Tips: You have to remember a lot of calculations for different kind of metrics. It is not that hard, but keep to the slides and the book.

Introduction to Business Process Analytics
Professor: Ulrike Schultze, Associate Professor 2012-2013 Dunlevy
http://www.smu.edu/Cox/Departments/FacultyDirectory/SchultzeUlrike

Textbook: No required.
Software: No.
Contents: In this class, students will learn the conceptual frameworks, tools and skills needed to develop a blueprint for analytics. This entails successfully analyzing the high-level requirements for business analytics, prioritizing and outlining solutions, proposing business process improvements to generate the requisite data, and making the business case.
Tips: This is a totally case-based class, without any programming skills requirement. But the course load will be very heavy and you have to read a lot of case before class and discuss in class. The conceptual framework is very important. Participation means a lot in this course.

Decision Models
Professor: Amy Puelz, Clinical Professor
http://www.smu.edu/Cox/Departments/FacultyDirectory/PuelzAmy

Textbook: Practical Management Science, Fourth edition by Winston and Albright.
Software: Excel and Palisade’s Decision Tool Suite software.
Contents: linear, integer, binary and nonlinear optimization models in Excel; decision trees for business decisions given uncertainty, simulation models for business decision given uncertainty.
Tips: It basically teaches operation research models with Excel applications. The Professor keeps a perfect pace of presenting how to build decision models in Excel at class. You will learn the most in class. Follow every examples in class and finish the corresponding homework, and you will get a good result.

Advanced Decision Models

Professor: Amy Puelz, Clinical Professor 
http://www.smu.edu/Cox/Departments/FacultyDirectory/PuelzAmy

Textbook: Practical Management Science, Fourth edition by Winston and Albright.

Software: Excel and Palisade’s Decision Tool Suite software.

Contents: Goal Programming, Non-linear Model, Non-Smooth Model, VBA in Excel, stochastic optimization and Logistic Regression models.

Tips: This is the advanced version of the Decision Model in Module A. Goal programming may be the most difficult part in this course. Follow every step that the Professor presents in class and you will have no problem in success in the course.

Applied Predictive Analytics II

Professor: Michael Braun, Associate Professor

http://www.smu.edu/Cox/Departments/FacultyDirectory/BraunMichael
Textbook: No required.

Software: R.

Contents: Construct probability models of customer activity, and estimate model parameters using the method of maximum likelihood; Choose among model specifications, using quantitative and qualitative criteria; Apply Bayesian methods for inference, classification and forecasting; Model integrated customer purchase and retention processes, and how to use those models to estimate expected customer lifetime value.

Tips: This may be the most difficult course I have taken in this program so far. Each class has a lot of contents to cover and master. The theory part is on probability models and Bayesian methods. The context is marketing related, such as new product adoption, sales forecasts and customer lifetime value.  The application is R based and it involves a lot of programming work. The homework is also difficult and it usually go a little beyond class materials. The professor wants you to discover something by yourself in homework. Anyway, this is a hard course but very state-of-the-art. There is no textbook, but you rely on the Slides and several recently published academic articles.

Database Design for Business Applications

Professor: Stewart Rogers, Adjunct Professor

http://www.smu.edu/Cox/Departments/FacultyDirectory/RogersStewart

Textbook: Concepts of Database Management, 8^th Edition, Philip Pratt and Mary Last.

Software: Access and Excel.

Contents: Fundamentals of relational database design, Data Management (Table design, Input and Output, Client/Server, Administration Issues), How to talk to IT intelligently (Database Jargon, current capabilities), Excel Data Capabilities (Data Functions, Table Manipulation, In Memory DB’s).

Tips: It is an introduction level course for database. It is not hard and the concept is the basic for advanced database course. You will learn basic SQL, which is very useful.

Revenue Management

Professor: John Semple, Charles Wyly Professor of MIS

http://www.smu.edu/Cox/Departments/FacultyDirectory/SempleJohn

Textbook: Pricing and Revenue Optimization, by Robert L Phillips.

Software: Excel.

Contents: optimal pricing model, Little Woods’ two class model, n-class dynamic model, network capacity control, choice model, overbooking management.

Tips: This class is mainly a modelling class. You will use Excel to build up those models. The homework is very interesting and challenging at the same time. So you will learn both in class and in doing homework.

Business Forecasting

Professor: Dr. Tom F Tan

http://www.smu.edu/Cox/Departments/FacultyDirectory/TanTom

Textbook: Business Forecasting, 9^th Edition, by Hanke and Wichern

Software: SAS JMP, Excel

Contents: Exploring Data Patterns, Moving Average and Smoothing Methods, Time Series Decomposition, Regression with Time Series Data, ARIMA model.

Tips: The key topics that will be covered include qualitative techniques, smoothing and decomposition of time series, regressions, the Box-Jenkins Methodology and Judgmental Forecasting. We will emphasize on hands-on applications using a statistical package JMP, rather than technical foundations and derivations. You will use Excel and SAS JMP to build up those models. The final project will be very helpful to have on your resume. 

Business Intelligence

Professor: Bryan Smith

http://www.smu.edu/Cox/Departments/FacultyDirectory/SmithBryan
Textbook:  There are no course packets or textbooks for this class. Professor provides sufficient documentation and guides for the required software and technologies.

Software: Microsoft SQL, Power BI, Report Builder, Visual Studio

Contents: SQL Basics, NULLs, ETL Development, SQL Nesting, Windowing, SQL Numerics, Report Development and Data Discovery.
Tips: The course will be based on lab practice sessions and homework at the end of every class. Though we are allowed to work in groups for this course, we highly recommend being able to master the SQL techniques and concepts at your best individual level. They will be very helpful for your interviews.

Data Mining

Professor: Timothy McDonough

http://www.smu.edu/Cox/Departments/FacultyDirectory/McDonoughTimothy
Textbook:  Data Mining Techniques : For Marketing, Sales and Customer Relationship Management, Third Edition, Gordon S Linoff and Michael J. A. Berry, Wiley (2011).

Software: KNIME

Contents: Data Mining, Data Scrubbing, Clustering, Neural Networks, Market Basket Analysis, Text Mining.

Tips: You might want to pay attention to concepts like Clustering, Neural Networks and Market Basket Analysis very closely. These become very important if you are hoping to take up a job in Retail Analytics or any Customer Relationship based jobs for that matter. KNIME might seem difficult at first but it will be the simplest drag and drop tool to learn for mining purposes.

Data Visualization

Professor: Hettie Tabor

http://www.smu.edu/Cox/Departments/FacultyDirectory/TaborHettie
Textbook:  You will be provided with a step by step guide to perform various visualisations by the professor.

Software: Tableau, Alteryx

Contents: Data Blending, Data Visualization - Tables, Charts, Mapping and Images, Dashboards and Story points.

Tips: There will be a quiz every week based on what has been covered in the previous week. Assignments are simple and straight forward. You will have a final group project in which you will collaborate with a real client and work together to provide visualizations and insights from their data. This has been my most favorite project and was a great learning experience. I can talk about it in all my interviews and is great to have on your resume too. Tableau is one of those key tools an analyst must have in his pocket.

Retailing Analytics
Professor: Edward Fox

http://www.smu.edu/Cox/Departments/FacultyDirectory/FoxEd
Textbook: Professor provides with sufficient handouts for the concepts covered in class. His power point presentations have been our guide.

Software: SAS 

Contents: Demand Modeling, Price Modeling and Analysis, Promotion Modeling and Analysis, Inventory Analysis, Market Share Modeling.

Tips: This course is way easier to understand and score well on considering we have enough exposure to SAS already. All that we need to do is master the techniques for performing Retailing Analytics. Retailing Analytics course has covered me for any interview I have given in that space. 

Managing Big Data
Professor: Bryan Smith

http://www.smu.edu/Cox/Departments/FacultyDirectory/SmithBryan

Textbook: You will have to buy a Student Guide and Lab Manual by Horton Works that you will use throught out the course module.

Software: Hadoop - Spark, Pig, Hive, MapReduce, Sqoop, Flume.

Contents: Using HDFS Commands, HCatalog with Pig, Understanding Hive, running MapReduce Job, Joining Datasets using Pig & Hive, Importing RDBMS Data into HDFS, Exporting HDFS Data to an RDBMS, Analyzing Big Data with Hive.

Tips: Professor will expose you to various labs that are most important for you to crack the Hadoop Certification. Being Hadoop certified, will be your major take away if you clear the certification exam.

Multivariate Analysis
Professor: Raj Sethuraman

http://www.smu.edu/Cox/Departments/FacultyDirectory/SethuramanRaj

Textbook: Multivariate Data Analysis by Joseph Hair, William Black, Barry Babin, Rolph Anderson, 7th Edition, Pearson.

Software: SAS.

Contents: Discriminant Analysis, Cluster Analysis, Factor Analysis, ANOVA/MANOVA, Structural Equation Modeling(SEM).

Tips: This course will expose you to some theory and principles underlying the various multivariate statistical techniques and some hands-on experience through analysis of cases and real world databases in SAS.

Project Management
Professor: Sreekumar Bhaskaran

http://www.smu.edu/Cox/Departments/FacultyDirectory/BhaskaranSreekumar

Textbook: No separate textbook for this course. Professors presentations and his notes will be helpful.

Software: Excel.

Contents: Project Planning and Execution, Project planning under uncertainty, Risk Management, Portfolio Planning and Distributed Project Management.

Tips: This course introduces analytical tools and concepts that enable project managers to evaluate, manage and execute critical functions of any project while ensuring speed, efficiency and market impact. It is designed to cover the essential elements of creative and effective project management, including vertical and horizontal project management and the technical and social aspects of project execution.

Understanding What Customers Value 
Professor: Bill Dillon 

http://www.smu.edu/Cox/Departments/FacultyDirectory/DillonBill

Textbook: Getting Started with Conjoint Analysis, 3rd Edition by Bryan K.Orme.

Software: Excel

Contents: The objectives of this course are three fold: 1) to familiarize students to issue challenges and protocols setting price, 2) to expose the student to a variety of preference and choice models used by brand managers and marketing analysis 3) to give students hands on experience in using conjoint/choice modeling techniques and choice simulators.

Tips: This course examines these marketing decisions using a combination of lectures, case studies and exercises. Take copious notes and review the practice problems. Much of what is seen on the final comes from the HW and the practice problems on the slides.

Web and Social Media Analytics
Professor: Rajiv Mukherjee

http://www.smu.edu/Cox/Departments/FacultyDirectory/MukherjeeRajiv

Textbook: Networks, Crowds and Markets : Reasoning about a highly connected world by David Easley and Jon Kleinberg. Other case studies, articles and readings useful for the course will be provided by the professor.

Software: R - igraph, SAS - Sentiment Analyzer, SAS Enterprise Miner, Google Analytics

Contents: Social Network analysis, Text/ Data Mining, Web Analytics and running experiments on social media platform.

Tips: It will particularly be useful for those seeking careers in management or digital media strategy consulting and business analytics. You can choose to write Google Analytics certification at the end of the course, which will add value to your resume. The group projects, case studies assigned in this course are pretty interesting and will be useful to talk about during your interviews.

We tried to cover all the mandatory courses and most of the electives but we could have missed a couple of other electives that were available to us and were not taken by any of the authors. We will add them in the following weeks.