Understand the fundamentals of Predictive Analytics in R.
Enhance skills by applying them to Model Data.
Get accustom to Predictive Analytics as career option with practical knowledge on some of the techniques that are currently in demand, such as Hypothesis Testing, Linear Regression, Multiple Regression, Logistic Regression, Correlations, Chi-Square Test etc.
Use Predictive Modelling Techniques on different types of data.
Work with different types of data and perform Data Manipulation and Preparation.
Develop constructive approach to solve business queries with R.
Analyze real time data and perform learnt skills.
7 sections • 44 lectures • 4h 22m total length
Why predictive modelling?
Applications of predictive modelling
What is R?
Introduction to the interface of R
Why R for predictive modelling?
Group Manipulation Concept
Group Manipulation Hands On
String Manipulation Concept
String Manipulation Hands On
Transform and Split
Handling missing values
Hypothesis Test Concept
Hypothesis Testing Hands On
Chi-square Test Concept
Chi-Square Test Hands On
T-Test Hands On
Z-Test Hands On
Introduction to Regression Analysis
Linear Regression Concept
Implementing Linear Regression in R
Multiple Regression Concept
Implementing Multiple Regression Concept in R
Logistic Regression Concept
Implementing Logistic Regression Concept in R
You should have some basic idea about R tool and its objective
If you have knowledge about basic statistics previously, it would be an added advantage for you.
In this course you will learn about predictive
analytics using R language
It starts with an introduction to the predictive modelling along with its application.
Also you learn about R and and how R is used for Predictive modelling
You will also design statistical experiments and analyze the results using modern methods
You will also learn Data manipulation methods and predictive Modelling techniques in R.
Collectively, this course will help you internalize a core set of practical and effective predictive analytics methods and concepts, and apply them to solve some real world problems.
This course contains lectures as videos along with the hands-on implementation of the concepts, additional assignments are also provided in the last section for your self-practice.
Who this course is for:
Developers who want to step-up as 'Data Scientists'
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