Introduction to Data Analysis

Live Classroom
Duration: 2 days
Live Virtual Classroom
Duration: 2 days
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The field of data analysis is an ever-evolving discipline that focusses on new predictive modeling techniques along with rich analytical tools that help us handle the ever-increasing volume of big data. The course explores the qualifications required for data analysts as well as the analytic tools associated with the role. It also helps participants understand how to chart a career path ahead for themselves, along with developing an understanding of how the discipline of data analysis has developed over the years.

What You'll Learn

  • Learn the terms, jargon, and impact of business intelligence and data analytics
  • Gain knowledge of the scope and application of data analysis
  • Explore ways to measure the performance of and improvement opportunities for business processes
  • Describe the need for tracking and identifying the root causes of deviation or failure
  • Review the basic principles, properties, and application of Probability Theory
  • Discuss data distribution including Central Tendency, Variance, Normal Distribution, and non-normal distributions
  • Learn about Statistical Inference and drawing conclusions about a Data Population
  • Learn about Forecasting, including introduction to simple Linear Regression analysis
  • Learn about Sample Sizes and Confidence Intervals and Limits, and how they influence the accuracy of your analysis


  • Definitions of BI
  • History of BI
  • How is BI used to help Businesses
  • Definition of DA
  • The relationship between BI and DA

  • Oracle study on business data preparedness
  • Overview of Study Findings-overwhelmed by volume of data and inability to utilize data effectively
  • Possible solutions to data overflow problems

  • Role of a data analyst
  • Skill set required to be an effective data analyst
  • Exercise: Channeling your inner analyst

  • The two types of Decision Models Businesses use
  • The Benefits of Fact-Based Decision Making
  • Rational Decision Model: Six- Step Method
  • Pal’s Diner: An Example of how the Rational Model is used in practice
  • Exercise: Who’s the boss?

  • Definition of Big Data
  • The 4 V’s of Big Data
  • Structured versus Unstructured Data
  • The Challenges of Big Data
  • Exercise: Camp WoeBeData

  • Data Types: Qualitative versus Quantitative
  • Taking a Closer Look: Data Measurement
  • Four Types of Data Variables
    • Definition and examples of Nominal Variables: Name only
    • Definition and examples of Ordinal Variables: Order Matters
    • Definition and examples of Interval Variables
    • Definition and examples of Ratio Variables
    • Summary of Statistics/Operations that can be performed on each type

Exercise: Marketing to low renters

Five ways we use data visualization techniques

  • How to create custom tables in Excel
  • How to Sort/Filter tabular data
  • How to create and manipulate pivot tables

  • How to create Pie, Column, and Line charts using Excel
  • Communicating effectively using different chart types
  • How to choose the correct chart to display the correct data type
  • Exercise: Table Mining
  • Exercise: Charting poverty

  • Measures of Centrality: Mean, Median, Mode
  • Format of Data Values: Grouped Discrete and Grouped Continuous
  • Formulas for the Mean
    • Examples: Applying 3M’s to Grouped Discrete and Grouped Continuous Data
  • Measures of Spread: Standard Deviation, Range, Inter-quartile Range
    • Examples: Applying Measures of Spread to Grouped Discrete and Grouped Continuous Data
  • Exercise: Faulty Wear
  • Exercise: “Faulty Wear: Take 2”

  • Origin of Probability
  • Probability: Examples of Business Applications
  • The traditional definition of Probability
  • Simple Computation: The TopBottomFraction Method
  • How to calculate probabilities from contingency tables
  • How to Calculate conditional probability from contingency tables
  • Applying probability to calculate relative frequency
  • Applying probability to calculate the expected value
  • Using Expected Value in Decision Making
  • Exercise: Pocket Probability
  • Exercise: Magazine Money
  • Exercise: Home Sweet Home
  • Exercise: Peck Up Your Speed
  • Exercise: Rent Me

  • Examples of Normally Distributed Data Variables
  • Characteristics of the Normal Distribution
  • Interpreting the Empirical Rule
  • Components of the Normal Distribution: Probabilities and X values
  • Using the NORMDIST function in Excel to calculate probability from a normal distribution
  • Using the NORM. INV function in Excel to calculate X values related to a normal distribution
  • Exercise: Fill ‘R’ Up

  • Definition of Correlation and Regression
  • The relationship between Correlation and Regression
  • Correlation Coefficient: Values
  • Examples of Correlation
  • Interpretation of a Regression Equation
  • Step-by-Step example of How to Do a Regression Analysis
  • Exercise: Paid Sickouts
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Who should attend

The course is highly beneficial for –


  • Business Analyst, Business Systems Analyst, Staff Analyst
  • Those interested in CBAP®, CCBA®®, or other business analysis certifications
  • Systems, Operations Research, Marketing, and other Analysts
  • Project Manager, Team Leads, Project Leads, Project Assistants, Project Coordinators
  • Those interested in PMP®, CAPM®, or other project management certifications
  • Program Managers, Portfolio Managers, Project Management Office (PMO) staff
  • Data Modelers and Administrators, DBAs
  • Technical & other Subject Matter Experts (SMEs)
  • IT Staff, Manager, VPs
  • Finance Staff, Manager
  • Operations Analyst, Supervisor
  • External and Internal Consultants
  • Risk Managers, Operations Risk Professionals
  • Operations Managers, Line Managers, Operations Staff
  • Process Improvement, Compliance, Audit, & other Governance Staff
  • Thought Leaders, Transformation & Change Champions, Change Manager
  • Executives, Directors, & other senior starr exploring cost reduction and process improvement options
  • Executive and Administrative Assistants and Coordinators
  • Job seekers and those who want to show dedication to data analysis and process improvement
  • Leaders at all levels who wish to increase their Data Analysis capabilities


There are no mandatory prerequisites for this course. Participants would benefit from completing the Introduction to R course, prior to taking up this course.

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