About The Course
The self-paced Statistics Essentials for Analytics Course is designed for the learners to understand and implement various statistical techniques. These techniques are explained using dedicated examples. The use case is taken up at the end of each module and insights are gathered, thus at the end of the course we have a Project which is consistently worked upon throughout the course.
After the completion of this course at LearnChase, you should be able to:
1.Learn various statistics techniques like Sampling Methods, Conditional Probability, Bayesian Theorem, etc.
2.Understand where and how to apply which statistical technique
3.Implement Milgram’s Experiment
4.Implement statistics to conclude insights on the real world FLIGHT Data
Who should go for this course?
This course is designed for a wide range of people right from graduate students who might have no knowledge of statistics to business analysts who want to make their mark in the Analytics Domain.This course is also for people who might have a past knowledge of statistics and want to refresh the concepts.
No prerequisites are required for this course.
Why learn Statistics Essentials for Analytics?
Statistical methods are required to ensure that data are interpreted correctly and that apparent relationships are meaningful and not simply chance occurrences. This proves helpful in gathering insights of a business/product and also to make efficient business decisions, be it any domain. Thus, statistics is an essential technique to learn in today’s world of infinite data.
1. Introduction to Statistics and Basic Probability
Learning Objectives – At the end of this module, you will be able to understand Skewness, Modality, Measures of Center, Measures of Spread etc. You will also understand the relationship between these terminologies. You will also be able to analyze airlines data set to gather insights.
Topics – Statistics & Basic Probability – Sampling Methods, Measures of Center, Measures of Spread.
2. Basic Probability, Conditional Probability and Bayesian Inference
Learning Objectives – At the end of this module, you will be able to understand the rules of probability, learn about Disjoint and Independent events, understand the concept of probability, implement these concepts on a case-study. You will also learn and implement Bayes’ Theorem and implement Bayes’ theorem on a case-study.
Topics – Conditional Probability & Bayesian Inference – Terms, Definitions, Examples, Concepts & Applications.
3. Distributions and Regression Modeling
Learning Objectives – At the end of this module, you will be able to understand Normal distribution, interpreting z-scores and calculating percentiles, Binomial Distribution, Mean and Standard deviation. You will also understand the Milgram Experiment.
Topics – Probability Distributions & Regression Modeling – Normal Distribution, Binomial Distribution, Linear Regression Model and Analysis.