Statistics Essentials for Analytics

Brush up your statistics skills for real-world data analysis

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Key Highlights

  • Self-paced
  • Practical Assignments
  • Lifetime Access to Learning Management System
  • 24x7 Expert Support
  • Course Completion Certificate
  • Online Forum for Discussions

Course Price

$199.00 $399.00

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Available Courses Delivery

This course is available in the following formats:

Self Paced (On-Demand)

24x7 access to instructor-led videos and practical activities
Convenient training that syncs with your schedule
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$399  $199

Course Overview

This course has been designed to train learners in numerous basic and advanced Statistical Approaches to resolve real-world complex problems. It educates about Bayesian Inference and Regression modelling. It teaches about different types of data and various data testing techniques.

Course Objectives

  • Educate about different categories of data
  • Introduction to various sampling techniques
  • Train in descriptive statistics
  • Teach about probabilistic approach to solve real-life complex problems
  • Acquaint learners with Bayesian Inference
  • Introduction to Clustering techniques
  • Educate about Regression modelling
  • Develop proficiency in Hypothesis
  • Train in testing the data

Career Benefits

  • Better remuneration as an expert in statistics-oriented data analysis
  • Demonstrate expertise in solving complex problems using statistical knowledge
  • Multi-industry opportunities


  • None

Who should take up?

  • Analytics Manager
  • Business Analysts
  • Developers and Analysts keen on learning Data Science
  • R Professionals

Course Content

  • Introduction to Data Types
  • Numerical parameters to represent data
  • a Mean
  • b Mode
  • c Median
  • d Sensitivity
  • e Information Gain
  • f Entropy Statistical parameters to represent data
  • Uses of probability
  • Need of probability
  • Bayesian Inference
  • Density Concepts
  • Normal Distribution Curve
  • Point Estimation
  • Confidence Margin
  • Hypothesis Testing
  • Levels of Hypothesis Testing
  • Association and Dependence
  • Causation and Correlation
  • Covariance
  • Simpsons Paradox
  • Clustering Techniques
  • Parametric Test
  • Parametric Test Types
  • Non- Parametric Test
  • Experimental Designing
  • A/B testing
  • Logistic and Regression Techniques
  • Problem of Collinearity
  • WOE and IV
  • Residual Analysis
  • Heteroscedasticity
  • Homoscedasticit


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