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. Course Description

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. 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. 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... Read More »

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.

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.

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.

Course Outcomes:
• 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
Course Details:

Target Audience

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

Access Timeframe

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

Prerequisites

No prerequisites are required for this course.
Certificate Info:

Type of Certification

Certificate of Completion

Digital

Professional Association/Affiliation

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.

Course Outline

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.
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.
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.

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