Statistics Essentials for Analytics Online Training | Edureka

Statistics Essentials for Analytics

A self-paced course that helps you to understand the various Statistical Techniques from the very basics and how each technique is employed on a real world data set to analyze and conclude insights. Statistics and its methods are the backend of Data Science to "understand, analyze and predict actual phenomena". Machine learning employs different techniques and theories drawn from statistical & probabilistic fields.


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Why this course ?

Online self - paced learning

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.
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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.

Lifetime Access

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

Certification

edureka 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.

The self-paced Statistics Essentials for Analytics Course has been designed in such a manner that it is easy for a future Data Scientist to get a solid foundation on the concepts. The complete mechanism of Data Science is explained in detail in terms of Statistics and Probability. Data and its types are discussed along with different kind of sampling procedures.

Other essential concepts of Statistics (statistical inference, testing, clustering) are emphasized here as well since that’s a very important part of being a Data Scientist. In addition, you will be introduced to primary machine learning algorithms in this Course.

After the completion of this course, you should be able to:
  • Analyze different types of data
  • Master different sampling techniques
  • Illustrate Descriptive statistics
  • Apply probabilistic approach to solve real life complex problems
  • Explain and derive Bayesian inference
  • Understand Clustering techniques
  • Understand Regression modelling
  • Master Hypothesis
  • Illustrate Testing the data

The course is designed for all those who want to learn essential statistics required for Data Science and Data analytics. The curated statistics course will help you form a strong foundation for the Data Science and predictive modelling (nowadays Machine Learning) field.

The following professionals can go for this course:
  • Developers aspiring to be a 'Data Scientist'
  • Analytics Managers who are leading a team of analysts
  • Business Analysts who want to understand Machine Learning (ML) Techniques
  • Information Architects who want to gain expertise in Predictive Analytics
  • 'R' professionals who want to captivate and analyze Big Data
  • Analysts wanting to understand Data Science methodologies

No prerequisites are required for this course.

Statistics and its methods are the backend of Data Science to "understand, analyze and predict actual phenomena". Machine learning employs different techniques and theories drawn from statistical & probabilistic fields. This Statistics Essentials for Analytics Course enables you to gain knowledge of the essential statistics required for analytics and Data Science, understand the mechanism of popular Machine Learning Algorithms like K-Means Clustering, Regression. The course also takes you through the glimpse of hypothesis testing and its methods enabling you perform test on alternative hypothesis.

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.
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
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
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
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
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
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
. Call a Course Adviser for discussing Curriculum Details . 1844 230 6361
We do provide placement assistance by routing relevant job opportunities to you as and when they come up. To get notified on relevant opportunities, it is important that you fill out your profile details.

It is important to attend classes and complete assignments. Course completion is an important criterion based on which we screen profiles of learners interested in a particular job. Also, before your profile is shared with prospective employers, you will have to go through an internal assessment by edureka. So it is important to be well versed with the course concepts to become eligible for placement opportunities.
All the instructors at edureka are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by edureka for providing an awesome learning experience.
You can Call us at +91 90660 20867 /1844 230 6362 ( US Tollfree ) OR Email us at sales@edureka.co . We shall be glad to assist you. 

  • Once you are successfully through the project (Reviewed by a edureka expert), you will be awarded with edureka’s Statistics Expert certificate.
  • edureka 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, Mindtree, BNYMellon etc. Please be assured.

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Statistics Essentials for Analytics