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Data Science on R By OrangeTree Global

A clear understanding about the data science theory, techniques and its application in RStudio platform
Course from Udemy
 38 students enrolled
 en
From this course students will have a clear understanding about the data science theory, techniques that are applied and also its application in RStudio platform
In this course we focus on the following topics
1) What is Business Analytics and why is Analytics used in the Business field
2) A detailed understanding about Descriptive Statistics
3) Understanding probability theory and different types of distributions along with its application in R
4) Clarity about Sampling and its distribution along with its application in R
5) Building of hypothesis and learn how to test it in R
6) Checking the significance using different types of T-Test and its application in R
7) The theory of ANOVA and its application in R
8) Finding the Association between variables using Chi Square and Correlation in R
9) Learn what is Linear Regression and how to build a model to predict the values in R
10) Learn what is Logistic Regression and how to build a model to predict the Binary values in R
11) Learn what is Factor and Cluster Analysis and how to apply in R
12) An understanding about Time series in the field of business analytics and how to build a model, forecast future values using R

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:

  1. Introduction to Analytics

  2. Understanding Probability and Probability Distributions

  3. Introduction to Sampling Theory and Estimation

  4. Introduction to Segmentation Techniques: Factor Analysis in R

  5. Introduction to Segmentation Techniques: Cluster Analysis in R

  6. Correlation and Linear Regression in R

  7. Introduction to categorical data analysis and Logistic Regression in R

  8. Introduction to Time Series Analysis

  9. Text Mining and Sentiment analysis in R

  10. Market Basket Analysis in R

  11. Statistical Significance T Test Chi Square Tests and Analysis of Variance

Data Science on R By OrangeTree Global
$ 94.99
per course
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