Introduction to Data Analysis

Part 1: Data and Information

1. Data in the Real World
2. Data vs. Information
3. The Many “Vs” of Data
4. Structured Data and Unstructured Data
5. Types of Data

Part 2: Data Analysis Defined

1. Why do we analyze data?
2. Data Analysis Mindset
3. Data Analysis Steps
4. Data Analysis Defined
5. Descriptive Statistics vs Inferential Statistics

Part 3: Types of Variables

1. Categorical vs Numerical
2. Nominal Variables
3. Ordinal Variables
4. Interval Variables
5. Ratio Variables

Part 4: Central Tendency of Data

1. (Arithmetic) Mean
2. Median
3. Mode

Part 5: Basic Probability

1. Probability Uses In Business
2. Ways We Can Calculate Probability
3. Probability Terms
4. Calculating Probability
5. Calculating Probability from a Contingency Table
6. Conditional Probability
7. Frequency Distribution

Part 6: Distributions, Variance, and Standard Deviation

1. Discrete Distributions
2. Continuous Distributions
3. Range
4. Quartiles
5. Variance
6. Standard Deviation
7. Population vs. Sample
8. Application of the Standard Deviation
   • Standard Deviation and the Normal Distribution
   • Sigma (σ) Values (Standard Deviations)
9. Bimodal distribution
10. Skew and Summary
11. Other Distributions
    • Poisson Distribution
    • Exponential Distribution
    • Pareto Distribution (“80/20”)
    • Log Normal Distribution
12. Distributions in Excel
     

Part 7: Fitting Data

1. Bivariate Data (Two Variables)
2. Covariance and Correlation
3. Simple Linear Regression
4. Linear Regression
5. Fitting Functions
   • Linear Fit
   • Polynomial Fit
   • Power-Law Fit

Part 8: Predictive Analytics Overview

1. Monte Carlo Method

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

Professionals who may benefit include:

• 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 staff 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

• Measure business performance.
• Identify improvement opportunities for business processes.
• Describe the need for tracking and identifying the root causes of deviation or failure.
• Use the principles, properties, and application of Probability Theory.
• Discuss data distribution including central tendency, variance, normal distribution, and non-normal distributions.
• Draw conclusions about a data population using statistical inference.
• Forecast trends using simple linear regression analysis.
• Perform accurate analysis after learning 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.