[BSDM] Basic Stat for Quantitative Decision Making

Essentials of Hypotheses Testing

[BSCS] Basic Stat for Understanding Credit Scoring Model

Logistic Regression and Discriminant Analysis

 

[BSCB] Basic Stat for Understanding Customer Behavior and Segmentation

Cluster Analysis

 

[BSRA] Basic Stat for Understanding Quantitative Risk Assessment

Monte Carlo Simulation

 

[BSPD] Basic Stat for Understanding New Product Design

Conjoint Analysis

 

[BSPA] Basic Stat for Understanding Predictive Analytics

Linear Regression and ANOVA

 

[BSFT] Basic Stat for Understanding Forecasting Techniques

Moving Average and Autoregressive Models

 

[BSBS] Basic Stat for Understanding the Underlying Business Structure

Exploratory Factor Analysis

 

Course Description

It is all about decision making! The Quantitative decision making process needs data to be collected, structured and packaged in a form decision makers can understand and use to make informed decisions, whether related to customer behavior and segmentation, product design, sales forecasting, risk assessment, credit rating or scoring, and more. In certain complex business scenarios, specialized technologies and platforms are needed. Whether you or others want to call it Big Data Analytics, Predictive Analytics, Data Analysis, or Machine Learning, the bottom line is that statistics reside at the root of all technologies. Clearly with no robust base of understanding the fundamentals and the basics of statistics, it is not possible to comprehend how these higher level concepts and techniques work. It is no longer an option; businesses nowadays need not only to deploy a big data analytics platform or technology, but also to educate their staff to understand the fundamentals of data analytics. Sophisticated learning content is not needed to develop the required knowledge and skills to deal with such technologies.

Basic Statistics has it all. It is not only about numbers and formulas from which decisions can be made, but about the process and the change in the mindset of employees to become critical thinkers with a better understanding of the business and therefore better decision makers.

This hands-on course will introduce the participants the fundamentals and the essential concepts of statistics ranging from the sample-and-population up to probability distributions terms. Moreover, this course will show participants how to apply this basic knowledge in business issues depending on the objective of the course.

PIZZATISTICS® consists of two parts; Base and Topping. While the Base addresses an introduction to basic statistics, the Topping addresses the application of the acquired knowledge and skills to particular business issues. The available Toppings so far are: Logistic Regression, Discriminant Analysis, Conjoint Analysis, Multiple Linear Regression, Cluster Analysis, Exploratory Factor Analysis, Structural Equation Modeling, Advanced Hypotheses Testing, Monte Carlo Simulation and Forecasting Techniques.

To enroll in the PIZZATISTICS® course, participants select a business application to be addressed such as customer segmentation, product design etc. and accordingly attend the Base. Upon completion, participants will be split into groups to attend the Topping, depending on the selected business application.

The duration of the Base is 3 full days, the Topping varies from 1 to 2 days depending on the topping selected.

Course Outlines: The Base

Day 1:    The Framework and the Right Questions

                Top Down Thinking – from business issues to data

                Framework Building Process

                Who should participate?

                Asking the Right Questions

                Population and Sample (1)

Day 2:    Back to Basics

Population vs. Sample (2)

True values versus their Estimates

Data Types and Measurements

Descriptive and Inferential Data Analysis

Assessing the Effect

Types of Effect

Types of Errors and their Sources

Day 3:    No Probability No Decision

                Linking Probability Concepts to Life

                Quantifying the Effect

                Toolkit for Answering Questions

During the Base part participants will work in groups to increase learning outcomes. Statistical packages or Microsoft excel program with Data Analysis Add-ins will be used during the program.

Course Outlines: The Topping

Day 4:    It depends on the business application and the Topping

Linear Regression and ANOVA

Logistic Regression and Discriminant Analysis

Exploratory Factor Analysis

Conjoint Analysis

Cluster Analysis

Monte Carlo Simulation

Moving Average and Autoregressive Model

During the Topping, participants will work in groups to increase learning outcomes. Statistical packages will be used throughout the program.