Upcoming Batches for Statistics Essentials for Analytics (Online Self Paced)
Course Description
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.
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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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
- Statistical parameters to represent data
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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
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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
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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
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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
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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
Prerequisite
No prerequisites are required for this course.
Who should take 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.
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