Decision Sciences Institute Data Analytics and Statistics Instruction (DASI) Session 2:50 PM - 4:20 PM; Saturday, Nov 18, 2017 A Panel Presentations and Discussions Session Title: What Statistics Knowledge should students know to prepare for Graduate Business Analytics Program Ram B. Misra Session Chair

A DASI Panel Discussion Panel Abstract: A panel of textbook authors and professors teaching graduate level analytics courses lead a discussion among those attending on the content of a prerequisite statistics class.  What current topics should be retained and what new topics should be added for analytics preparation?  What computational tools are the most appropriate?   Session Chair: Ram B Misra, Montclair State University Presenters:      Jeffrey Camm, Wake Forest University, cammjd@wfu.edu  David Levine, Baruch College, davidmlevine@msn.com Helen Zhang, University of Arizona, hzhang@math.arizona.edu Ram Misra, Montclair State University, misrar@mail.montclair.edu

MSU Master’s Program in Business Analytics 30 Credits, part time (2 courses a semester) Students with undergraduate majors in Engineering Math Sciences Business

MSU Master’s Program in Business Analytics Applied Business Statistics for Business Analytics Enterprise Architecture and Data Management Foundation of Business Analytics Decision Analytics and Optimization Big Data Analytics Advanced Business Analytics Decision Risk Modeling Price Analytics and Revenue Management Capstone Practicum in Business Analytics Elective - Data Analytics and Visualization, or Data Mining, or Machine Learning

Business Statistics for Business Analytics Course Learning Objectives Learn various statistical definitions and their usage Learn the basic concepts of data visualization Learn the basic concepts of how to make conclusions about the population from the sample data Learn advanced statistical tools used in business analytics. Learn the use of common tools used in business analytics such as Excel, Tableau, etc. Learn how to complete a project in a team environment in an effective manner.

Contents of the Course I am teaching this Semester Data Concepts types and scope, sources, collection methods Data Visualization Data Characteristics central tendency (mean, median and mode) variation (range, variance and standard deviation) shape.

Contents of the Course Probability Concepts definitions, joint probabilities, probability rules Bayesian Theorem Probability Distributions Discrete – Binomial and Poisson Continuous - uniform, exponential and normal

Contents of the Course Inferential Statistics Sampling Distributions Confidence Intervals Hypothesis Testing ANOVA Statistics for Predictive Analytics Linear Regression – the concepts, assumptions, interpretation Developing multiple regression models Logistic Regression

Contents of the Course Advanced Topics Understanding and determining effect size Data dimension reduction techniques Principle component analysis .....

Tools to be used Excel with Data Analysis Add on Excel with Mega Stat Add on

A DASI Panel Discussion Questions ….