Rating 3.6 out of 5 (5 ratings in Udemy)
What you'll learn- From this course students will have a clear understanding about the data science theorytechniques 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 …
Rating 3.6 out of 5 (5 ratings in Udemy)
What you'll learn- From this course students will have a clear understanding about the data science theorytechniques 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
DescriptionThe 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
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
