1. 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

2. 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
David Levine, Baruch
Helen Zhang, University of
Ram Misra, Montclair State

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

4. 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

5. 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.

6. 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.

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

8. 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

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

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

11. A DASI Panel Discussion
Questions ….