# Data Science on R By OrangeTree Global

*30+ In-Demand Skills & Tools, Access to 150+ instructor-led online classes for a year. Master*

**Data Science**with R, SAS & Python. Also, Learn Machine Learning, Bigdata & Tableau. Industry Expert Trainers. 400+ Professional Courses. High Pass Rate.
Created by
OrangeTree Global

Published 10/2019

English

Current priceRp280,000

Original Price~~Rp2,800,000~~

Discount90% off

30-Day Money-Back Guarantee

This course includes

- 11.5 hours on-demand video
- Full lifetime access
- Access on mobile and TV

- Certificate of Completion

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What you'll learn

- 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

Requirements

- For better understanding R Programming by OrangeTree Global course is recommended

Description

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

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