Course Overview
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Expert| English[Auto-generated] What is Analytics?
Analytics is the process of transforming converting data into insight for making better decisions. Business analytics is comprised of solutions which can be used to build analysis models and predict future business states. Business analytics can be focussed on internal or external processes. Business analytics involves the use of statistical techniques, information system software and operation research methodologies to explore the trends in data. There are different type of analytics which could be of great help to organizations.
What is R?
R serves as an alternative to traditional SPSS and SAS packages. R is an open source programming language which works in different environment. This is mostly used for predictive analysis and data visualization. R is widely used in Academic and research centres as well as in many business organizations. R is also commonly used by statisticians and data analysts for developing statistical software and data analysis.
Why Analytics is Important?
- Business analytics can be used to get deeper insight into the business data
- Business analytics can be used to automate and optimize business processes
- Because of using business analytics data is treated as a corporate asset so that it can be used for competitive advantage
- By using analytics you can find new patterns and know why certain results occurred in the business
- Business analytics will help you to forecast the future events of a business
Business Analytics using R Course Objectives
After the completion of this course you will be able to
- Understand the basics of R programming
- Know the usage of R programming in business analytics
- Apply several data importing techniques in R
- Have a deeper understanding of concepts like classification, regression, time series, sentiment analysis
- Get all the skills required to analyze large sets of data and develop solutions which will help in decision making
Pre Requisites for taking this course
The pre requisites includes basic statistical knowledge. Knowledge in Mathematics is an added advantage in taking up this course.
Target Audience for this course
This course is meant for all those who are interested in working in the analytics field and are keen to improve their technical skills. This course is also excellent for all those who wanted to become data analysts in the near future.
Business Analytics using R Course
Section 1: Business Analytics Using R
Business Analytics using R
R is used in business analytics for the analysis, exploration and simplification of large highly complicated data sets. R takes care of some of the most commonly performed tasks in a business. This topic covers what is business analytics and how r helps in business analytics in brief.
Normal Distribution
This chapter will help you to understand what is a normal distribution, its description and details, the usage of normal distribution in R, the arguments of normal distribution and the values of normal distribution. It also explains the functions of normal distribution. The normal distribution is explained with few examples like SAT and birth weights for your easy understanding.
dNorm, pNorm, qNorm
In this chapter we will discuss the following topics
- The normal density and the density curve with the R-function named dnorm (density).
- The cumulative normal distribution function with the R-function named pnorm (probability).
- The Quantiles with the R-function named qnorm (quantile).
Understanding Estimation
In this chapter of the tutorial you will see how to compute the estimates in R based on simple random data. The steps are easily explained using built-in data frame called surveys. The section also covers the following topics
- Point estimate of population mean
- Interval estimate of population mean with known variance
- Interval estimate of population mean with unknown variance
- Sampling size of population mean
- Point estimate of population proportion
- Interval estimate of population proportion
- Sampling size of population proportion
Properties of Good Estimators
This topic will list you all the qualities of a good estimator and how to become a potential estimator.
Central Limit Theorem
Here you will learn
The simulations of the central limit theorem to demonstrate that the distribution of the sample mean is approximately normal for large sample size. It also tells you when the central limit theorem should be used. The central limit theorem is explained with examples. Constructing a histogram using such theorem is explained in detail with example.
Kurtosis
Under this chapter you will learn what is kurtosis ? what is its formula ? types of kurtosis like platykurtic, leptokurtic and mesokurtic. Examples are given along with the solution for easy understanding.
Confidence Intervals for the Mean
Confidence intervals can be calculated from a Normal Distribution, t distribution and many t distributions. This section deals with how to use R to find confidence intervals and sampling confidence intervals in R. The commands for finding confidence interval is explained with examples and with histograms.
t-distribution
This section involves the meaning of t distribution, how student t distribution is derived, what is the formula of student t distribution and the functions that generate the values of t distribution. The commands of t distribution are explained with examples.
Section 2: Examples, Testing and Forecasting
R Examples
This chapter provides a brief introduction to R and explains collection of R code snippets with explanations for each. Such examples include the following sections under it Basics, Functions, Countdown, User Input, Reading files, Probability and Statistics, Regression and Time Series Analysis.
Standard error of the mean
This chapter explains what is standard error of mean, when to use standard error and how to compute the standard error of the mean in R. An example is given to make you understand the concept easily
Downloading the Package
The directory where the R packages are stored is called Library. R comes with a set of standard packages. This chapter will help you to learn the step by step procedure to install the packages, Install packages without root access and setting the repository. This section also contains the steps to create your own package and add packages to R.
Sample Differences
This is a chapter on statistical inference about difference between two population proportions in R. This is explained with examples which contains a problem and its solution.
Hypothesis Generation and Testing
In this section the procedure of hypothesis testing in R is explained in detail. It includes the following topics
- Type 1 error
- Significance level
- One sided P value
- Lower Tail test of Population Mean with known variance
- Lower Tail test of Population Mean with unknown variance
- Upper Tail test of Population Mean with known variance
- Upper Tail test of Population Mean with unknown variance
- Two Tailed test of Population Mean with known variance
- Two Tailed test of Population Mean with unknown variance
- Lower Tail test of Population proportion
- Upper Tail test of Population proportion
- Two Tailed test of Population proportion
Power & Sample Size
Power analysis is an crucial aspect of experimental design. This section will tell you how the sample size is determined using the power analysis. It gives an overview of power analysis and sample determination with examples.
Calculating the Z value
This chapter includes the meaning for Z score, what value it represents and how Z score is calculated. An example is given to demonstrate how it is calculated and used in R.
Forecasting
This chapter contains the following sections under it
- Getting started – This topic contains definition of forecasting, planning and goals, determining what to forecast, Forecasting methods, Steps in forecasting, exercises and case studies.
- Forecaster’s toolbox – Forecast Packages in R, graphics, evaluating forecast accuracy, prediction intervals
- Judgemental forecasts – Key principles, Delphi method, New product forecasting, Judgemental adjustments
- Exponential smoothing – Simple exponential smoothing, Double exponential smoothing, Holt’s linear trend method, Exponential trend method, Damped trend method, Exercises
- ARIMA models – This topic covers Stationarity and differencing, Backshift notation, Autoregressive models, Moving average models, Non seasonal ARIMA models, ARIMA modelling in R, Seasonal ARIMA models, Exercises and examples.
- Advanced forecasting methods – Dynamic regression models, Vector auto regressions, Neural network models, Forecasting hierarchical.
Time Series Analysis Applications
Under this chapter you will learn What is time series analysis, how to read time series data, how to plot time series data, what are the components of time series and how to separate it. All these topics are explained with case studies and examples.
Observation Components
This explains what is principal component analysis, its usage, arguments, details, values and how to perform a Principal Component Analysis in R using examples.
Traditional Approaches
This topic lets you learn how to implement the traditional statistical methods using R. R also converts the traditional scripting languages within it.
ARIMA Steps
Here you will know the step by step procedure of forecasting using ARIMA modelling in R. It is explained with case studies.
Section 3: Understanding Visualizations
R Visualization
One of the most appealing things in R is its ability to create data visualizations with just a few lines of code. This chapter is a comprehensive guide to data visualization in R. The chapter starts with a brief introduction to data visualization in R and lists out the reasons why the data should be visualized. The various type of data visualizations like the basic Histogram, Bar or line chart, Box plot, Scatter plot and the advanced visualization techniques like Heat Map, Mosaic Map, Map visualizations, 3D graphs, Correlogram are explained in detail with examples mentioning the commands and codes for each type of visualization.
There are also few interactive visualization packages in R which can be used to visualize data in a wonderful way. These packages are also included in this chapter with their technical features, codes and examples.
Overlaying Plots
Overlaying plots means two or more data series are plotted in one plot in R. This is explained in detail in this section with few illustrations.
Graphs representation of Data
The graphical representations in R are very powerful. This chapter is a quick overview to help you to create a quick graph in R to visualize your data. The arguments used for creating graphs are given and each type of graph is given a brief introduction with examples to make it easy for you to understand.
Bubble Charts
A bubble chart is nothing but a scatter chart whose markers are expressed in various colour and sizes. This chapter teaches you how to make bubble charts in R with examples.
Anova
This section is a presentation of the ANOVA in R. It includes an introduction, functions used for ANOVA in R, post hoc testing, how the ANOVA results are obtained and how it is represented visually.
Concept of effect
This topic makes you learn what is effect size, why is it important in R, how to calculate it in R, how it can be interpreted, relationship between effect size and significance and the factors affecting the effect size.
Estimate of Treatment effect
This is an package available in R. The topics covered under this heading includes description, usage, arguments, values and examples of treatment effect package.
Factorial Anova
This section will illustrate the computation of factorial ANOVA using R commands. It tells you what is factorial ANOVA, how to summarize the factorial data and how to represent the factorial ANOVA visually using plots or graphs.
Regression
This chapter explains how to use R for Linear regression. The topics covered under this section are given below
- Simple regression – Simple linear model, Least squares estimation, Regression and correlation, Evaluating the regression model, Forecasting with regression, regression with time series, exercises and examples.
- Multiple regression – Introduction to Multiple Linear Regression, Predictors, Selecting predictors, Residual diagnostics, Matrix formation, Non linear regression, Correlation and forecasting, exercises and examples.
FAQ’s General Questions
- What if I have queries after completing this course ?
Our support team is available 24*7 to help you in solving out your queries during and after the course.
- What are the benefits of learning analytics with R training online ?
The first benefit is that you need not go anywhere. You can attend these classes sitting at your home or office. The second benefit is that you can reschedule the classes if you miss any of them. The third benefit is that you can get the study material then and there and you can use it for reference purpose.
Testimonials
- Braden Pham
This is a very great course to start R programming. It has a good introduction to R and is of great help to the beginners as well as professionals in this field of analytics. The course is well organized and helps to understand the concepts with simple examples.
- Jones
Finally a great course to learn analytics using R. It is good for freshers as well as working people. The course has clear instruction and is interesting. The course is overall precise and serves its purpose. It helped me a lot to understand the basics of analytics using R.
| Where do our learners come from? |
| Professionals from around the world have benefited from eduCBA’s Business Analytics using R 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. |
