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Introduction to Data Analysis

SS Course: 9000382

Course Overview

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Data Analysis is an ever-evolving discipline with lots of focus on new predictive modeling techniques coupled with rich analytical tools that keep increasing our capacity to handle big data. However, in order to chart a coherent path forward, it is necessary to understand where the discipline has come from since its inception.

The field of Business intelligence depends largely on Data analysis tools and techniques in order to inform effective decision-making. In fact, the disciplines are so intertwined that some often confuse the two. Therefore, we begin our introduction by examining the history of Business intelligence, its relationship to data analysis, and why the two are needed to help businesses deliver a complete assembly of their 'data puzzle'. This module also addresses some of the hurdles businesses face when dealing with data overload and suggests some possible solutions to the problem.

With the explosion of big data, businesses recognize there is a greater need for employing someone who is qualified to correctly analyze the data. In this module, we explore the qualifications for the data analyst as well as the analytic tools associated with the position. It is unfortunate that there is such a dearth of data analysts. With a projected shortage of 190,000 data science jobs into 2018, it is no wonder that businesses are scrambling to recruit talent.

                                                                  

Scheduled Classes

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08/27/20 - AVT - Virtual-Instructor Led - Virtual-Instructor Led
09/24/20 - AVT - Virtual-Instructor Led - Virtual-Instructor Led
10/22/20 - AVT - Virtual-Instructor Led - Virtual-Instructor Led
11/19/20 - AVT - Virtual-Instructor Led - Virtual-Instructor Led
12/17/20 - AVT - Virtual-Instructor Led - Virtual-Instructor Led

What You'll Learn

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  • 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.

Outline

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Viewing outline for:

Part 1: What are BI and DA?

  1. Definitions of BI
  2. History of BI
  3. How is BI used to help Businesses
  4. Definition of DA
  5. The relationship between BI and DA
Part 2: Data Here, There, and Everywhere!
  1. Oracle study on business data preparedness
  2. Overview of Study Findings-overwhelmed by volume of data and inability to utilize data effectively
  3. Possible solutions to data overflow problems
Part 3: Got Data? The Unique Role of the Data Analyst
  1. Role of a Data Analyst
  2. Skill set required to be an effective Data Analyst
Part 4: Fact-Based Decision-Making Process
  1. The two types of Decision Models Businesses use
  2. The Benefits of Fact-Based Decision Making
  3. Rational Decision Model: Six- Step Method
  4. Pal's Diner: An Example of how the Rational Model is used in practice
Part 5: Big Data Anatomy
  1. 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
  1. Data Types: Qualitative versus Quantitative
  2. Taking a Closer Look: Data Measurement
  3. 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
  1. The five ways we use data visualization techniques
Part 8: Displaying Tabular Data in Excel
  1. How to create custom tables in Excel
  2. How to Sort/Filter tabular data
  3. How to create and manipulate pivot tables
Part 9: Using Charts and Graphs to Communicate Data
  1. How to create Pie, Column, and Line charts using Excel
  2. Communicating effectively using different chart types
  3. >How to choose the correct chart to display the correct data type
Part 10: Using Numerical Descriptives to Summarize Data
  1. Measures of Centrality: Mean, Median, Mode
  2. Format of Data Values: Grouped Discrete and Grouped Continuous
  3. Formulas for the Mean
    • Examples: Applying 3M's to Grouped Discrete and Grouped Continuous Data
  4. 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
  1. Origin of Probability
  2. Probability: Examples of Business Applications
  3. The traditional definition of Probability
  4. Simple Computation: The TopBottomFraction Method
  5. How to calculate probabilities from contingency tables
  6. How to Calculate conditional probability from contingency tables
  7. Applying probability to calculate relative frequency
  8. Applying probability to calculate the expected value
  9. >Using Expected Value in Decision Making
Part 12: The Normal Distribution
  1. Examples of Normally Distributed Data Variables
  2. Characteristics of the Normal Distribution
  3. Interpreting the Empirical Rule
  4. Components of the Normal Distribution: Probabilities and X values
  5. Using the NORMDIST function in Excel to calculate probability from a normal distribution
  6. Using the NORM. INV function in Excel to calculate X values related to a normal distribution
Part 13: Correlation and Regression
  1. Definition of Correlation and Regression
  2. The relationship between Correlation and Regression
  3. Correlation Coefficient: Values
  4. Examples of Correlation
  5. Interpretation of a Regression Equation
  6. Step-by-Step example of How to Do a Regression Analysis

Prerequisites

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Although it is not mandatory, students who have completed the self-paced Introduction to R eLearning course have found it very helpful when completing this course.

    Who Should Attend

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    Anyone involved in operations, project management, business analysis, or management who needs an introduction to Data Analysis, would benefit from this class:

    • 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

    Next Step Courses

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