Predicitve Analytics on R

About this Course

The following topics will be covered as part of this series. Each topic is described in detail with hands-on exercises done on RStudio to help students learn with ease. We will cover all the nitty-gritty that you need to know to get started with R along with the correction and handling of errors as and when they pop-up. The program builds a solid foundation by covering the most popular and widely used data science technologies and its applications.

The topics that are covered in this tutorial are as follows:
Introduction to Analytics
Understanding Probability and Probability Distributions
Introduction to Sampling Theory and Estimation
Introduction to Segmentation Techniques: Factor Analysis in R
Introduction to Segmentation Techniques: Cluster Analysis in R
Correlation and Linear Regression in R
Introduction to categorical data analysis and Logistic Regression in R
Introduction to Time Series Analysis
Text Mining and Sentiment analysis in R
Market Basket Analysis in R
Statistical Significance T Test Chi Square Tests and Analysis of Variance.

What will you learn
  • From this course students will have a clear understanding about the data science theory, techniques that are applied in analytics and also its application in RStudio platform


Requirements

For better understanding knowledge on R Programming is recommended.

Section

  • 12 Sections

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