Business Analytics using R - Hands-on!
Description
This section will give you a brief description about what is R and how business analytics is done using R.
This section gives an overall view of all the headings which will be included in the entire course.
This section gives a brief note on the theory of discriminant analysis in R, Linear discriminant function, Quadratic discriminant function, Fisher’s linear discriminant and the observations of the analysis.
Introduction to R and Analytics
This section tells you what R is, why it is used and how to make R work for you. This will also let you know the basics of R programming, its data types and functions.
Evolution of Business Analytics
This section explains what is the meaning of business analytics, its importance, scope. History of business analytics is also explained.
Business Example – Hotel
A real life example of business analytics in the field of hotel industry is explained in this section.
In this section you will learn how to input data in business analytics in R. It also includes data cleaning which will produce a data set for analysis. It also includes functions used in data inspection and other functions.
R has a wide variety of data types and in this section each data type is explained in detail with examples. It tells you what is ordinal data and the types of analysis to be made for the ordinal data.
This sections tells you what is decision model, what are the tools used in decision model, the models and techniques used in decision model along with detailed example.
Under this topic you will learn what is descriptive decision making model, its introduction, descriptive models, the theories and examples of descriptive decision models.
Section 2: Business Analytics Life Cycle
In this section you will learn the stages of business analytics life cycle, its design, data analytics and data visualization.
This section deals with developing and deploying predictive models in R and how to automate the deployment of R models in production.
Steps in Problem Solving Process
This topic helps you to know the effective problem solving steps in business analytics and the strategies of problem solving process
Software used in Business Analytics
This section will let you know the most popular and widely used business analytics tools along with its advantages and disadvantages.
This chapter will introduce you to the R language. It will let you learn the basics of R and works as a beginners guide to you and helps broadening your skills. It gives you few examples for your easy understanding.
This section will give you the steps to install R studio in your system. You can know the requirements you needed to install R studio
Section 3: Understanding R
This section is designed to just provide the basics of R and an introduction to the use of R for new beginners.
This section provides an introduction to all the basic R functions with a long list and its explanation for easy understanding.
This chapter includes all the data types of R including Scalars, vectors, matrices, data frames and lists.
Here you can learn what is recycling rule and the implementation of recycling rule in R.
In this chapter you can know the symbols used to represent the special numerical values in R and how to call these functions.
This topic will give a brief introduction about parallel processing and parallel computing in R
Here you will come to know the logical operators and its symbols used in R
This chapter will help you to learn how two strings can be merged in R. Gives you examples using the functions to combine strings.
It explains what is coercion, how it occurs and what are the types of coercion.
This chapter helps you to understand the functions of arrays and matrices and shows you the manipulation techniques of array and matrices in R
Explains what is mean by factors and the levels of factors
Lets you what are the packages available in repository and its policies
Tells you how to install a package in R using
Lets you learn how to import data from other files
This chapter will help you to learn to import data from SPSS into R
This section will let you know how to create subsets in data, how to create a data frame in R and how to organize your data.
This chapter tells you what is meant by data aggregating and when the function should be used using an example.
Section 4: Data Manipulation & Statistics Basics
Data Manipulation & Statistics Basics
Introduction to data manipulation and the different ways to manipulate data in R is explained in this section
Lets you understand how to merge two data frames using the merge function.
Common data creation commands and the method to create a data set from scratch is given in detail in this section.
An example for merging data frames is given for your easy understanding.
Introduction to online statistical tool in R and the sample R codes for performing statistical computations are given here.
Lets you learn how to create new variables, recode variables, rename variables and specifying variables in R.
This chapter is a tutorial on computing the quartiles of observation variables in R and how to apply the quantile function.
Explains what is variance, types of variance, an problem with solution.
This section tells you what is covariance, what is positive covariance, what is negative covariance, an example.
This section is an tutorial on how to compute the cumulative frequency distribution in R and explains with a graph
Explains what is MASS in R and what are the functions and data sets that are supported by MASS
Faithful is a data frame in R and this section tells you where you should use it and what is the purpose of using it.
What are the ways to create a scatter plot in R and what is the function used to denote a scatter plot in R
Control flow is the order of the code in R. This chapter explains some of the basic control flow constructs of R and how it functions.
Section 5: Statistics, Probability & Distribution
Statistics, Probability & Distribution
In this section we will tell you how to compute a few well known statistical and probability distributions that occurs most often in the field of statistics.
Explains how to generate a random number in R from its library
Choosing a random number in R is illustrated with an example in this chapter
An example to help you understand the discrete distributions in R is given in this section
Gives you real time practice problems to help you understand R in detail
Case studies are given in this chapter for easier understanding
Exponential Distribution Practice Problem
The simulation of exponential distribution using R along with real time example is explained under this topic
Lets you know how to get expected value for a data set
Tells you how R can be used for Betting analysis
This section gives you an example of R being used in Deal or No deal game.
Explains the type of distribution in R, how it is used and what are its functions
Binomial Distribution continued
Explains what is binomial distribution, its usage, arguments and details.
Explains the assumptions of a binomial distribution
Lets you understand what is uniform distribution, its usage, arguments and details.
Probability distributions examples
Explains the four basic probability distribution types in R along with examples
Probability distributions examples continued
This section also continues with the examples of probability distribution in R
Section 6: Business Analytics using R
Tells you how R can be used for business analytics
Explains how the normal PDF can be calculated and plotted in R
What is Normal, Not Normal
Lets you understand what is a normal distribution in R and what is not a normal distribution in R
SAT Example
Gives an example of SAT computation in R
Gives an example of birth weights computation in R
dNorm, pNorm, qNorm
Explains all these types of probability distribution along with their functions used in R
Helps you to understand the estimation done using R
Lets you know the properties of good estimators in R
Shows you the simulation of the central limit theorem in R
Explains what is kurtosis and its measures in R
Constructing Central Limit Theorem
Lets you know what are the functions used for central limit theorem in R
Confidence Intervals for the Mean
Lets you to find out the interval estimate of the mean population with R
Explains the above chapter with detailed examples
Gives you a computer lab example to get a deeper insight into the topic
Lets you understand the t distribution in R and where and why it is used
Gives example for easy understanding of t distribution
Section 7: Examples, Testing and Forecasting
This section gives you examples of R in business analytics
This topic explains how to calculate the standard error of the mean in R
Course Duration:-16h 11m
