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Home   >    All Courses   >   Big Data and Analytics   >   Statistics Essentials for Analytics

Statistics Essentials for Analytics

SUPPORT NO. +1 302 956 2015 (USA)

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Why this course ?

With help of this course you will able understand and implement various statistical techniques like Sampling Methods, Conditional Probability, Bayesian Theorem, etc. You will also learn where and how to apply which statistics technique.

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Course Duration

You will undergo self-paced learning where you will get an in-depth knowledge of various concepts that will be covered in the course.

Real-Life Case Studies

Towards the end of the course, you will be working on a project where you are expected to implement the techniques learnt during the course.

Assignments

Each module will contain practical assignments, which can be completed before going to next module.

Life Time Access

You will get lifetime access to all the videos,discussion forum and other learning contents inside the Learning Management System.

Certification

addiLEARN certifies you as an expert in Statistics based on the project reviewed by our expert panel.

Forum

We have a community forum for all our customers that further facilitates learning through peer interaction and knowledge sharing.

   Learning Process

Online Self Learning Courses are designed for self-directed training, allowing participants to begin at their convenience with structured training and review exercises to reinforce learning. You'll learn through videos, PPTs and complete assignments, projects and other activities designed to enhance learning outcomes, all at times that are most convenient to you.

   How will I execute the Practicals?

The practicals are shown in 'R' which is a open-source analytics tool. The step-wise set-up guide for R will be provided to you

   Understanding the Data

Objectives: At the end of this Module, you should be able to:

·         Understand various data types

·         Learn Various variable types

·         List the uses of variable types

·         Explain Population and Sample

·         Discuss sampling techniques

·         Understand Data representation

 

Topics:

·         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

   Probability and its Uses

Objectives: At the end of this Module, you should be able to:

·         Understand rules of probability

·         Learn about dependent and independent events

·         Implement conditional, marginal and joint probability using Bayes Theorem

·         Discuss probability distribution

·         Explain Central Limit Theorem

 

Topics:

·         Uses of probability

·         Need of probability

·         Bayesian Inference

·         Density Concepts

·         Normal Distribution Curve

   Statistical Inference

Objectives: At the end of this Module, you should be able to:

·         Understand concept of point estimation using confidence margin

·         Draw meaningful inferences using margin of error

·         Explore hypothesis testing and its different levels

 

Topics:

·         Point Estimation

·         Confidence Margin

·         Hypothesis Testing

·         Levels of Hypothesis Testing

   Data Clustering

Objectives: At the end of this module, you should be able to:

·         Understand concept of association and dependence

·         Explain causation and correlation

·         Learn the concept of covariance

·         Discuss Simpson’s paradox

·         Illustrate Clustering Techniques

 

Topics:

·         Association and Dependence

·         Causation and Correlation

·         Covariance

·         Simpson’s Paradox

·         Clustering Techniques

   Testing the Data

Objectives: At the end of this module, you should be able to:

·         Understand Parametric and Non-parametric Testing

·         Learn various types of parametric testing

·         Discuss experimental designing

·         Explain a/b testing

 

Topics:

·         Parametric Test

·         Parametric Test Types

·         Non- Parametric Test

·         Experimental Designing

·         A/B testing

   Regression Modelling

Objectives: At the end of this module, you should be able to:

·         Understand the concept of Linear Regression

·         Explain Logistic Regression

·         Implement WOE

·         Differentiate between heteroscedasticity and homoscedasticity

·         Learn concept of residual analysis

 

Topics:

·         Logistic and Regression Techniques

·         Problem of Collinearity

·         WOE and IV

·         Residual Analysis

·         Heteroscedasticity

·         Homoscedasticity

   Will I get the Placement Assistance?

To help you in this endeavor, we have added a resume builder tool in your LMS. Now, you will be able to create a winning resume in just 3 easy steps. You will have unlimited access to use these templates across different roles and designations. All you need to do is, log in to your LMS and click on the "create your resume" option.

   Who are the Instructor at Certhippo?

All the instructors at Certhippo are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by Certhippo for providing an awesome learning experience

   What if I have more Queries?

You can give us a CALL at +91- 989 926 9264(India) / 1800-688-5897 (US) OR email at support@Certhippo.org

   Certhippo Certification Process:

    • Once you are successfully through the project (Reviewed by a Certhippo expert), you will be awarded with Certhippo’s Statistics Expert certificate.
    • Certhippo certification has industry recognition and we are the preferred training partner for many MNCs e.g.Cisco, Ford, Mphasis, Nokia, Wipro, Accenture, IBM, Philips, Citi, Ford, Mind tree, BNYMellon etc. Please be assured.