Business Analytics using R - Hands-on!

This is an online course on Business Analytics using R where you will be learning about business analytics life cycle, you will understand the function of R in business analytics, also you will learn about data manipulation, statistics basics, probability and distribution, examples, testing and forecasting, also you will understand about visualizations. These all concepts you will be learning through this course.

What is Business Analytics?

Business analytics is the combination of skills, technologies and applications used by an organization to understand their business data and statistics in order to help them to take decisions. Business analytics solutions generally use data and quantitative analysis to calculate the past performance. Business analytics are used to identify the weaker areas in an organization and to enhance their growth. The need for business analytics has led to the development of business analytics software and enterprise platforms.

Business Analytics Using R

In the recent years R has become the widely used programming language for computational statistics, visualization and data science. R is used by many statisticians, scientists and data analysts to find a solution for their problem in various fields. R is used as the most important tool for business analytics in companies like Google, Facebook and LinkedIn.

R contains every data analytics techniques at your fingertips. It helps you to perform some of the most commonly performed tasks by business analysts with the help of its 4000 packages.

Course Objectives

At the end of this course you will be able to

• Know about R programming
• Understand the concepts of business analytics
• Apply various data importing techniques in R
• Understand Machine Learning techniques
• Use various functions like cor(), llist(), hclust(),etc
• Use R programming for performing business analytics
• Work on real life project by implementing R analytics

Pre requisites

The pre requisites for this course includes basic knowledge of statistics. Knowledge in any other programming language is an added advantage but not a must.

Target Audience for this course

This course will be useful for students and Data analysts and people from Analytics domain. It will also be useful for people who wanted to learn business analytics using R.

Course Description- Analytics using R Programming

Section 1: Introduction

• Course Introduction

This section will give you a brief description about what is R and how business analytics is done using R.

• Course Curriculum

This section gives an overall view of all the headings which will be included in the entire course.

• Discriminant Analysis

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.

• Data for Business Analytics

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.

• Ordinal Data

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.

• Decision Model Example

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.

• Descriptive Decision Models

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

• Business Analytics Life Cycle

In this section you will learn the stages of business analytics life cycle, its design, data analytics and data visualization.

• Model Deployment

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.

• Getting Started with R

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.

• Installing R Studio

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

• Basics of R

This section is designed to just provide the basics of R and an introduction to the use of R for new beginners.

• Basic R Functions

This section provides an introduction to all the basic R functions with a long list and its explanation for easy understanding.

• Data Types

This chapter includes all the data types of R including Scalars, vectors, matrices, data frames and lists.

• Recycling Rule

Here you can learn what is recycling rule and the implementation of recycling rule in R.

• Special Numerical Values

In this chapter you can know the symbols used to represent the special numerical values in R and how to call these functions.

• Parallel Summary Functions

This topic will give a brief introduction about parallel processing and parallel computing in R

• Logical Conjunctions

Here you will come to know the logical operators and its symbols used in R

• Pasting Strings together

This chapter will help you to learn how two strings can be merged in R. Gives you examples using the functions to combine strings.

• Type Coercion

It explains what is coercion, how it occurs and what are the types of coercion.

• Array & Matrix

This chapter helps you to understand the functions of arrays and matrices and shows you the manipulation techniques of array and matrices in R

• Factor

Explains what is mean by factors and the levels of factors

• Repository & Packages

Lets you what are the packages available in repository and its policies

• Installing a Package

Tells you how to install a package in R using

• Importing Data

Lets you learn how to import data from other files

• Importing Data SPSS

This chapter will help you to learn to import data from SPSS into R

• Working with Data

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.

• Data Aggregation

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

• Merging

Lets you understand how to merge two data frames using the merge function.

• Data Creation

Common data creation commands and the method to create a data set from scratch is given in detail in this section.

• Merge Example

An example for merging data frames is given for your easy understanding.

• What is Statistics

Introduction to online statistical tool in R and the sample R codes for performing statistical computations are given here.

• Variables

Lets you learn how to create new variables, recode variables, rename variables and specifying variables in R.

• Quantiles

This chapter is a tutorial on computing the quartiles of observation variables in R and how to apply the quantile function.

• Calculating Variance

Explains what is variance, types of variance, an problem with solution.

• Calculating Covariance

This section tells you what is covariance, what is positive covariance, what is negative covariance, an example.

• Cumulative Frequency

This section is an tutorial on how to compute the cumulative frequency distribution in R and explains with a graph

• Library (mass)

Explains what is MASS in R and what are the functions and data sets that are supported by MASS

• Head (faithful)

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.

• Scatter Plot

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

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.

• Random Variable

Explains how to generate a random number in R from its library

• Random Example

Choosing a random number in R is illustrated with an example in this chapter

• Discrete Example

An example to help you understand the discrete distributions in R is given in this section

• Practice problem

Gives you real time practice problems to help you understand R in detail

• Continuous Case

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

• Expected Value

Lets you know how to get expected value for a data set

• Gambling Example

Tells you how R can be used for Betting analysis

• Deal or no deal

This section gives you an example of R being used in Deal or No deal game.

• Distribution details

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.

• Expected Value from Binomial

Explains the assumptions of a binomial distribution

• Uniform Random Variables

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

• Business Analytics using R

Tells you how R can be used for business analytics

• Normal PDF

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

• Example- Birth Weights

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

• Understanding Estimation

Helps you to understand the estimation done using R

• Properties of Good Estimators

Lets you know the properties of good estimators in R

• Central Limit Theorem

Shows you the simulation of the central limit theorem in R

• Kurtosis

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

• Confidence Intervals Examples

Explains the above chapter with detailed examples

• Computer Lab Example

Gives you a computer lab example to get a deeper insight into the topic

• t-distribution

Lets you understand the t distribution in R and where and why it is used

• t-distribution continued

Gives example for easy understanding of t distribution

Section 7: Examples, Testing and Forecasting

• R Examples

This section gives you examples of R in business analytics

• Standard error of the mean

This topic explains how to calculate the standard error of the mean in R

• Downloading the Package

Under this section you will learn how to download and install the packages in R

• Sample Differences

This is an R tutorial about population mean between two different samples.

• Hypothesis Generation & Testing

Here we can learn about statistical hypothesis testing and generation based on few approaches

• Hypothesis Testing

An R tutorial on statistical hypothesis testing on critical value approach

• One sided P Value

Here you can learn about the steps to calculate the p value for a particular test

• Power & Sample Size

This topic explains the power analysis which helps to determine the sample size

• Testing Hypothesis using R

This tutorial explains few of the statistical tests which can be performed to test the hypothesis

• Calculating the Z value

Under this section you will learn what is Z value and how to calculate it

• Lower Tail proportion of population proportion

This topic makes you understand how to conduct lower tail test about population proportion

• Forecasting

This section lets you learn how to forecast with R

• Time Series Analysis Applications

This topic describes the creation of time series and its applications

• Approaches to Forecasting

Explains the simple forecasting approaches with daily time series

• Observation Components

This section covers the principal components of observation in R

• Traditional Approaches

Describes the traditional approach to research and modelling in R

• Double Exponential Smoothing

Lets you learn how to make forecasts using double exponential smoothing method

• ARIMA Steps

This section is the step by step guide to do forecasting through ARIMA

• Forecasting Performance

This includes evaluating and comparing the performance of some methods in forecasting

• Univariate ARIMA

Explains the univariate time series model and how to fit ARIMA model to univariate time series.

Section 8: Understanding Visualizations

• R Visualization

This section is a guide to data visualization in R

• Why Visualize

Here you will learn why visualization is important in R

• Overlaying Plots

It explains how to overlay scatter plots in R

• Graphs representation of Data

This is an introduction to graphical representation of data in R using various tools.

• Graphs representation of Data continued

Explains the tools of graphical representation in detail with examples

• Advanced Graphs

This chapter explains how to customize your graphs and also explains how to create more sophisticated graphs

• Bubble Charts

This section lets you learn how to make beautiful bubble charts with R

• Anova

This section is an instruction on how to perform ANOVA test in R

• Concept of effect

Explains what is effect size and effect displays in R for different models

• Estimate of Treatment effect

Here we can learn what is an estimating treatment effect with an example using t-test

• Factorial Anova

In this section we discuss about performing the factorial Anova in R

• Regression

This is an tutorial for performing different regression analysis using R

• Regression Model

Explains the different types of regression models in R with examples

• Linear Relationship

This section makes you understand how to use R for performing Linear regression

• Output of Regression Model

This section lets you learn how to interpret the output of the various regression models

FAQs

• What are the career benefits of this course ?

Certified data analyst will get a lucrative salary with very good growth prospects. Undertaking this course will open up career opportunities in almost every industry which requires the help of data analysts.

• Does this course require a prior statistical knowledge ?

It is not a mandatory but some basic knowledge in statistics is better. The course also covers section on basic statistical concepts with few examples which will help to improve your statistical skills.

Testimonials

Stephen John

The course on business analytics is very well structured for professionals. It brings many real time examples into the content which makes the learning process more easy and understandable. I am very satisfied and highly recommend this course for beginners as well as professionals.

Faiz Aurag

This business analytics course is the best for beginners as it is very easy to understand. It gives an idea to focus on the important things as a data analyst. Excellent course to get a basic understanding of R in business analytics.

Where do our learners come from?

Professionals from around the world have benefited from eduCBA’s Business Analytics using R Programming – Learn Programming In R And R Studio with Real Exercises! Courses. Some of the top places that our learners come from include New York, Dubai, San Francisco, Bay Area, New Jersey, Houston, Seattle, Toronto, London, Berlin, UAE, Chicago, UK, Hong Kong, Singapore, Australia, New Zealand, India, Bangalore, New Delhi, Mumbai, Pune, Kolkata, Hyderabad and Gurgaon among many.

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