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Statistics Essentials for Analytics

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Statistics Essentials for Analytics training provides complete knowledge to delegates about the Statistical Techniques. The delegates will learn how every technique is employed on a real-world data set to analyse and conclude insights. The delegates will get familiar with the implementation of the various statistical technique. These techniques are explained using dedicated illustrations. The delegates will learn about the various data types and variables types. With the help of the training, the delegates will able to understand Skewness, Modality, Measures of Center and Measures of Spread. Throughout the training, the delegates will understand the various statistical techniques from the basics.

The delegates will also understand the relationship between these terminologies. With the help of the Statistics Essentials for Analytics training, the delegates will able to analyse airlines data set to gather insights.  The delegates will get an opportunity to understand the rules of probability and also learn about the Disjoint and Independent events. The delegates will also understand the concept of probability, implement these concepts on a case-study. The training provides a complete knowledge to the delegates about the Normal distribution, interpreting z-scores and calculating percentiles, Mean, Binomial Distribution and Standard deviation.

Prerequisites

There are no prerequisites for attending Statistics Essentials for Analytics Training.


Course Objectives

After the completion of Statistics Essentials for Analytics Training at Silicon Beach Training, the delegates will be able:

  • To analyse different types of data
  • Understand Regression modelling
  • Understand Clustering techniques
  • To apply the probabilistic approach to solve the real-life complex problems
  • Understand how to explain and derive the Bayesian inference
  • Implement Milgram's Experiment
  • Understand Master different sampling techniques
  • Understand how and where to apply which statistical technique
  • Learn several statistics techniques such as Bayesian Theorem, Sampling Methods, Conditional Probability and many more

Statistics Essentials for Analytics Course Content

Understanding the Data

  • Introduction to Data Types
  • Numerical parameters to represent data
  • Mean
  • Mode
  • Information Gain
  • Entropy
  • Median
  • Sensitivity
  • Statistical parameters to represent data

Probability and its uses

  • Uses of probability
  • Need of probability
  • Bayesian Inference
  • Density Concepts
  • Normal Distribution Curve

Statistical Inference

  • Point Estimation
  • Hypothesis Testing
  • Confidence Margin
  • Levels of Hypothesis Testing

Data Clustering

  • Association and Dependence
  • Simpson’s Paradox
  • Clustering Technique
  • Covariance
  • Causation and Correlation 

Testing the Data

  • Parametric Test
  • Parametric Test Types
  • Non- Parametric Test
  • Experimental Designing
  • A/B testing

Regression Modelling

  • Logistic and Regression Techniques
  • Problem of Collinearity
  • WOE and IV
  • Residual Analysis
  • Heteroscedasticity
  • Homoscedasticity

Booking

* Required

For in-company or private training please request a quote

There are no scheduled dates for this course.

Total price (excluding any discounts and offers)
£1,995.00 ex VAT
£2,394.00 Inc VAT

Course Details

  1. Course Title

    Statistics Essentials for Analytics

  2. Duration

    2 Days

  3. Price Starting From

    £1,995.00 + VAT

Who is this course for?

This course is designed for everyone as well as for those who desire to learn more about the essential statistics which are required for Data Science and Data analytics. The below professionals also can attend this training:

  • Developers who want to be Data
  • Analytics Managers who are managing a team of analysts
  • Information Architects who desire to be expert in Predictive Analytics
  • 'R' professionals who want to captivate and analyse Big Data
  • Analysts wanting to understand Data Science methodologies
  • Business Analysts who wishes to understand about the Machine Learning (ML) Techniques

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