Class Details

Price: $1,790

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

 

 

 

 

 

 

Register for Class

Date Location
12/03/18 - 12/07/18, 5 days, 8:30AM – 4:30PM Tysons Corner, VA Register