  # Class Details

Price: \$1,950﻿

5 Day Course

Tuition Includes:﻿

• Expert instructor-led training, hands-on exercises and practice questions
• All course materials and textbooks provided
• Continental breakfast available in the morning
• Free coffee, tea, and soda available all day
• Fresh baked cookies every afternoon (only at participating locations)

## Course Outline

Module 1: Introduction to The Course

•         Introduction to Analytics
•         Different Types of Analytics
•         Why are There So Many Different Methods?
•         Terminology and Notation
•         Core Ideas in Data Analytics
•         The Steps in Data Analytics Projects

Module 2: Data Exploration

•         Introduction to Statistics
•         Variable Types
•         Summarizing Data
•         Descriptive Statistics: Measures of Central Tendency
•         Descriptive Statistics: Measures of Variation
•         Statistical Displays: Histograms and Boxplots

Module 3: Excel for Data Analysis

•         Introduction to Excel
•         Sort/Filter/Conditional Formatting
•         Pivot Tables
•         Data Visualization

Module 4: Breakeven Analysis

• Linear Functions
• Revenue and Cost Models
• Exponential Functions
• Curve Fitting
• What-If Analysis / Goal Seek

Module 5: Time Value of Money

• Simple Interest
• Compound Interest

Module 6: Probability Models

•       Basic Principles
•       Conditional Probability
•       Discrete Random Variables
•       Continuous Random Variables
•       Normal Distribution
•       Z-score
•        Outlier Detection Method

Module 7: Statistical Inferences

•      Sampling Types / Survey Errors
•         Confidence Intervals
•         t-Distribution
•         Introduction to Hypothesis Testing
•         Single Sample t-Test
•         Type I/II Errors

Module 8: Linear Regression – Part 1

•         Correlation
•         Simple Linear Regression
•      Multiple Linear Regression
•        Fit Measures

Module 9: Predictive Modeling Basics

• Data Preparation
• Integrating Data from Multiple Sources

Module 10: Linear Regression – Part 2

• Regression for Prediction
• Performance Evaluation

Module 11: Classification Models

•         Distance Measures
•         K-Nearest Neighbors
•         Performance Evaluation
•         Other Methods

Module 12: Segmentation Modeling / Cluster Analysis

•         Introduction to Segmentation
•         Cluster Analysis
•         Clusters Interpretation

Module 13: Spreadsheet Models / Optimization

• Linear Optimization Models
• Maximizing Profit / Minimizing Cost

Module 14: Data Analysis using R

• Introduction to R
• Data Analysis using R

Module 15: Decision Analysis (optional)

• Introduction to Decision Making Under Uncertainty
• Decision Analysis Without Probabilities
• Decision Analysis With Probabilities
• Decision Trees

## Objectives

• To develop one’s ability and confidence in effectively communicate analytical, quantitative, and statistical concepts.﻿
• To improve analytical thinking and develop effective problem solving strategies and validation techniques for different problem situations.
• To familiarize students with useful, efficient, and proper methodologies for summarizing and communicating quantitative and qualitative data in Excel.
• To build statistical models replicating the real life situation as closely as possible and to formulate appropriate hypothesis given the context
• To help students acquire effective modeling skills in designing and implementing readable and reliable spreadsheet models
• To teach students how to interpret the results of statisticaltests and decision models, and to use them in making decisions