Data Analysis Training Course (2 days) 

Note: this outline is our proposal, but the training can be tailored to your specific requirements upon prior request ahead of the proposed course date.

Why Learn Data Analysis?

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.


Course details


The final training outline will be designed depending on your particular requirements.

The practical exercises constitute a big part of the course time, besides demonstrations and theoretical presentations. Discussions and questions can be asked throughout the course.

During this course, you will:

  • 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.

  • Be able to 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.

  • Explore different methods and easy algorithms for forecasting future results and to reduce current and future risk.


Course Outline


Part 1: What are BI and DA?

  • Definitions of BI

  • History of BI

  • How is BI used to help Businesses

  • Definition of DA

  • The relationship between BI and DA


Part 2: Data Here, There, and Everywhere!

  • 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


Part 3: Got Data? The Unique Role of the Data Analyst

  • Role of a Data Analyst

  • Skill set required to be an effective Data Analyst


Part 4: Fact-Based Decision-Making Process

  • 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


Part 5: Big Data Anatomy

  • The Attributes of Big Data

    • Definition of Big Data

    • The 4 V's of Big Data

    • Structured versus Unstructured Data

    • The Challenges of Big Data


Part 6: Getting to Know Your Data

  • 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


Part 7: The Fundamental Ways we use data Visualization techniques

  • The five ways we use data visualization techniques


Part 8: Displaying Tabular Data in Excel

  • How to create custom tables in Excel

  • How to Sort/Filter tabular data

  • How to create and manipulate pivot tables


Part 9: Using Charts and Graphs to Communicate Data

  • 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


Part 10: Using Numerical Descriptives to Summarize Data

  • 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


Part 11: Probability: Quantifying Uncertainty

  • 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


Part 12: The Normal Distribution

  • 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


Part 13: Correlation and Regression

  • 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