Data Science on R By OrangeTree Global


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 Data Science on R By OrangeTree Global New
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Created by OrangeTree Global
Published 10/2019
English
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Current priceRp280,000
Original PriceRp2,800,000
Discount90% off
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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 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

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