Introduction to Data Analysis

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For those with little or no experience analyzing and manipulating data, this course quickly equips you with fundamental techniques to use data for better decisions.

Data analysis and analytics are evolving disciplines. We constantly hear about big data, prediction, AI, and modeling techniques.

However, advanced techniques rest on fundamentals which can be applied in many job roles. This class quickly equips you with that foundation. Whether you’re charting your overall business intelligence strategy or performing analysis yourself, these basic tools and techniques rapidly inform effective decision-making. In this fast-paced introductory workshop, we’ll examine the history of business intelligence, its relationship to data analysis, and why the two are needed to help businesses deliver a complete assembly of the ‘data puzzle’. We’ll also address hurdles teams face when dealing with data overload and suggests some possible solutions.

Amid an ongoing explosion of data, there’s also a greater need to understand who is qualified to correctly analyze data. We will explore the qualifications of data analysts as well as the analytic tools available for those people to use associated with the position.

Please note: Exercises in this course are not compatible with Excel in a web browser. Please make sure you have a version of Excel locally downloaded on your computer or access to Excel via Microsoft 365.

In this 2-day class you’ll learn:

  • 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.

Course Outline:

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)
  1. Bimodal distribution
  2. Skew and Summary
  3. Other Distributions
    • Poisson Distribution
    • Exponential Distribution
    • Pareto Distribution (“80/20”)
    • Log Normal Distribution
  1. 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