Introduction to Data Analysis

Course Code: 1914

$1495

Overview
Schedule classes
What you'll learn
Outline
Prerequisites
Who should attend

Overview

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.

Schedule Classes

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Course Delivery

This course is available in the following formats:

Live Classroom
Duration: 2 days

Live Virtual Classroom
Duration: 2 days

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

Outline

Module 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

Module 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

Module 3: Got data? The unique role of the data analyst

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

Module 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
Exercise: Who’s the boss?

Module 5: Big data anatomy

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

Module 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

Exercise: Marketing to low renters

Module 7: The fundamental ways we use data visualization techniques

Five ways we use data visualization techniques

Module 8: Displaying tubular data in Excel

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

Module 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
Exercise: Table Mining
Exercise: Charting poverty

Module 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
Exercise: Faulty Wear
Exercise: “Faulty Wear: Take 2”

Module 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
Exercise: Pocket Probability
Exercise: Magazine Money
Exercise: Home Sweet Home
Exercise: Peck Up Your Speed
Exercise: Rent Me

Module 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
Exercise: Fill ‘R’ Up

Module 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
Exercise: Paid Sickouts

Prerequisites

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

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