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 everevolving discipline that focusses on new predictive modeling techniques along with rich analytical tools that help us handle the everincreasing 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 nonnormal 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 Findingsoverwhelmed 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: FactBased DecisionMaking Process

The two types of Decision Models Businesses use

The Benefits of FactBased 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, Interquartile 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

StepbyStep example of How to Do a Regression Analysis