The statistical analysis techniques taught here form the foundation of any analytics or data science practice.
This selfpaced class is an excellent handson walkthrough of the most foundational techniques used to understand and ask questions of datasets. The statistical techniques taught in this workshop underpin virtually every common method for analyzing data. They also provide a practical, useful introduction to contemporary data science techniques.
This program comprises roughly five hours of expertled content, with basic assignments and exercises you can participate in between chapters.
 Upcoming Dates and Locations

Guaranteed To Run
 Course Outline

Lesson 1 – Statistics and Statistical Terminology
Objectives:
 What are statistics?
 What is a statistical population?
 Examples
Lesson 2 – Sampling; Descriptive vs. Inferential Statistics
Objectives:
 What is sampling?
 Population vs. sample
 When do we sample?
 Examples
Lesson 3 – Sampling and Statistical Bias
Objectives:
 Random sampling
 What is bias?
 Why we should avoid bias in samples
 Examples
Lesson 4 – Measures of Central Tendency & Arithmetic Mean
Objectives:
 What are the Measures of Central Tendency
 Arithmetic mean
 Examples
Lesson 5 – Geometric Mean and Harmonic Mean
Objectives:
 Geometric mean
 Harmonic mean
 Examples
Lesson 6 – Median
Objectives:
1) What is median
2) Examples
Lesson 7 – Mode and Midrange
Objectives:
 What is mode
 What is midrange
 Examples
 Review and Exercises
Lesson 8 – Measures of Dispersion
Objectives:
 What are the measures of dispersion
 Range
 Mean absolute difference
 Examples
Lesson – Measures of Dispersion
Objectives:
 Variance
 Standard deviation
 Examples
Lesson 10 – Some Examples
Lesson 11 – IQR (Interquartile range) and Outliers
Objectives:
 What is IQR
 1.5 * IQR rule
 Examples
Lesson 12 – Distributions
Objectives:
 What is distribution
 Histograms
 Probability mass function and cumulative probability
 Examples
Lesson 13 – Working with Distributions
Lesson 14 – Different Types of Distributions
Objectives:
 Discrete distributions vs. continuous distributions
 Introduction to MonteCarlo simulations
 Examples
Lesson 15 – Continuous and Parametric Distributions
Objectives:
 Continuous distributions
 Parametric distributions
 Normal distribution
 Examples
Lesson 16 – Inferential Statistics
Objectives:
 When we use inferential statistics
 Sample mean and sample variance
 Central Limit Theorem
 Estimation of population mean and confidence interval
 Examples
Lesson 17 – Hypothesis Testing for the Population Mean
Objectives:
 tdistribution
 Estimation of population mean confidence interval
 What is hypothesis testing
 How to run a twotailed test for a population mean
 Examples
Lesson 18 – Difference Between Two Means
Objectives:
 Problem formulation
 Hypothesis testing
 Examples
 Who should attend

 Data Analysts
 Any data practitioner needing a statistics refresher
 Business Analysts and Project Managers who query data
 Data and sytem architects
 Application developers who wish to integrate data processes
 Analysts who want to grow their basic data science skills
 PreRequisites

You will need a computer to view and participate in the class.