Statistics Essentials For Analytics Training And Certification

Statistics Essentials For Analytics Training

Statistics Essentials for Analytics Certification Training is intended to make you an expert in SAS programming and Analytics. You will be able to analyse and write SAS code for real problems, learn to use SAS to work with datasets, perform advanced statistical techniques to obtain optimized results with Advanced SAS programming.

SAS is a Leader in 2017 Gartner Magic Quadrant for Data Science Platform .

Since 1976, SAS has helped customers at more than 83,000 sites make better decisions faster.

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Course Curriculum

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Statistics Essentials for Analytics

SELF PACED

Statistics Essentials for Analytics Certification Training is intended to make you an expert in SAS programming and Analytics. You will be able to analyse and write SAS code for real problems, learn to use SAS to work with datasets, perform advanced statistical techniques to obtain optimized results with Advanced SAS programming.

  • WEEK 5-6
  • 10 Modules
  • 6 Hours
Safe Paced
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.
Goal: You will learn to model estimate and classify events based on the values of dependent variables. You will be taught to perform different types of clustering methodologies to bunch your observations.

Objective:

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

  • Define Clustering and its types.
  • Explain Clustering Algorithms.
  • Nest data in Different Clusters. 
  • Analyse the Regression between two or Multiple Variables.
  • Model and Estimate an event based on Logits.
Topics:
  • Introduction to Clustering.
  • Hierarchical Clustering.
  • Non-Hierarchical Clustering (K means Clustering).
  • Simple and Multiple Linear Regression.
  • Logistic Regression.
Hands On:
  • Demonstrate the use of PROC CLUSTER. 
  • Writing a SAS Advanced Program with PROC FASTCLUS.
  • Performing Operations on Regression with PROC REG.
  • Demonstrate Modelling using PROC LOGISTIC.
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.

Program Syllabus

Curriculum

You can also view the program syllabus by downloading this program Curriculum.

Curriculum

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

Course Description

About The Course
About the Course.
  • 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.
Objectives Of Our Online PySpark Training Course
Course Objectives. 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.
Go For Online Spark Training
Who should go for this course?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.
  • 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.
Learning With Our PySpark Certification Training
Pre-requisites.

No prerequisites are required for this course.

Who Should Go For Our PySpark Training Course
Why learn Statistics Essentials for Analytics?

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.

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Features

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Instructor-led Sessions

30 Hours of Online Live Instructor-Led Classes.
Weekend Class : 10 sessions of 3 hours each.

Real Life Case Studies

Real-life Case Studies

Live project based on any of the selected use cases, involving implementation of the various real life solutions / services.

Assignments

Assignments

Each class will be followed by practical assignments.

24 x 7 Expert Support

24 x 7 Expert Support

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Certification

Towards the end of the course, OL Tech Edu certifies you for the course you had enrolled for based on the project you submit.

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