About the Course
The Edureka Mastering Data Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It starts with the fundamental concepts of Data Manipulation, Exploratory Data Analysis etc before moving over to advance topics like the Ensemble of Decision trees, Collaborative filtering, etc.
After the completion of the Edureka Mastering Data Analytics with R course, you should be able to:
1. Understand concepts around Business Intelligence and Business Analytics
2. Explore Recommendation Systems with functions like Association Rule Mining , user-based collaborative filtering and Item-based collaborative filtering among others
3. Apply various supervised machine learning techniques
4. Perform Analysis of Variance (ANOVA)
5. Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc
6. Use various packages in R to create fancy plots
7. Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights
Who should go for this Course?
This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become 'Data Analysts' in near future. This is a must learn course for professionals from Mathematics, Statistics or Economics background and interested in learning Business Analytics.
What are the pre-requisites for this Course?
The pre-requisites for learning 'Mastering Data Analytics with R' include basic statistics knowledge. We provide a complimentary course "Statistics Essentials for R" to all the participants who enroll for the Data Analytics with R Training. This course helps you brush up your statistics skills.
Towards the end of the Course, you will be working on a live project. You can choose any of the following as your Project work:
Project #1: Sentiment Analysis of Twitter Data
Industry : Social Media
Description : A sports gear company is planning to brand themselves by putting their company logo on the jersey of an IPL team. We assume that any team which is more popular on twitter will give a good ROI. So, we evaluate two different teams of IPL based on their social media popularity and the team which is more popular on twitter will be chosen for brand endorsement. The data to be analyzed is streamed live from twitter and sentiment analysis is performed on the same. The final output involves a comparable visualization plot of both the teams, so that the clear winner can be seen.
The following insights need to be calculated :
1) Setup connection with twitter using twitter package. And perform authentication using handshake function.
2) Import tweets from the official twitter handle of the two teams using SearchTwitter function.
3) Prepare a sentiment function in R, which will take the arguments and find its negative or positive score.
4) Score against each tweet should be calculated.
5) Compare the scores of both the teams and visualize it.
Project #2: Census Data Analysis
Industry : Government Dataset
Description : Analyze the census data and predict whether the income exceeds $50K per year. Follow end to end modelling process involving:
1) Perform Exploratory Data Analysis and establish hypothesis of the data.
2) Test for Multi col-linearity, handle outliers and treat missing data.
3) Create training and validation data sets using Stratified Random Sampling (SRS) of data.
4) Fit Classification model on training set (Logistic Regression/Decision Tree)
5) Perform validation of the models (ROC curve, Confusion Matrix)
6) Evaluate and freeze the final model.
Here is the list of few additional case studies that you will get at edureka for deeper understanding of R applications.
Study#1: Market Basket Analysis
Industry: Retail - CPG
Description: Market Basket Analysis is done to see if there are combinations of products that frequently co-occur in transactions. The analysis gives clues as to what a customer might have bought if the idea had occurred to them. This is done using the “Association Rules” on real-time data. In this case study, you shall understand various methods for finding useful associations in large data sets using statistical performance measures. You will also learn how to manage the peculiarities of working with transaction data.
Data-set: The data set used here is from a grocery super store with 9835 rows of free flowing data without any labels.
Study#2: Strategic Customer Segmentation for Retail Business
Industry: E-Commerce, Retail
Description: In this case study, we will consider the dataset from a UK-based online retail business for the last two years. The objective of this case study is to do customer segmentation in this data set.
For this exercise, we are going to use customer’s recency, frequency and monetary (RFM) values. From these three derived values, we will segment entire customer base and will generate insights on the data set provided to do customer segmentation using RFM Model based Clustering Analysis.
Data-set: comprises 0.5 million records and 8 variables. Each record is for one online order placed by the customer.
Study#3: Pricing Analytics and Price Elasticity
Description: A retailer is planning to sell a new type of cheese in some of its stores. This is a pilot project for the retailer & based on the data collected during this pilot phase, retailer wants to understand a few things.
To promote sales of cheese, the retailer is planning for two different types of in-store advertisement:
1) Cheese as a natural product
2) Cheese as a family caring product
Now the retailer wants to know:
1) Which in-store advertisement theme is better and giving better sales of cheese in the store?
2) How the sales of cheese is reacting to its price change i.e. price elasticity?
3) What is the impact of the price changes of other products in the same store (e.g. Ice-cream & Milk) on the sales of cheese i.e. cross-price elasticity.
4) What should be the best price of cheese to maximize the sales and then do sales forecast.
Data-set: The data set used in this case study will have the following columns -
1) Price of Cheese
2) Sales of Cheese
3) Advertising method for cheese (either as a natural product or as a family product)
4) Price of Ice cream
5) Price of Milk
Study#4: Clustering Application using Shiny
Industry: Consumer Packaged Goods
Description: Shiny turn your analyses into interactive web applications, it is a web application framework for R. The data set that we are using in this case study relates to the clients of a wholesale distributor. It comprises, the annual spending in monetary units (m.u.) on diverse product categories. With this data we want to create a web based shiny application which can segment customers of wholesale distributor based upon the parameter passed thru ui.r
Data-set: The data set used in this case study has 440 rows of data and has the following attributes in columns -
Why learn Data Analytics with R?
The Data Analytics with R training certifies you in mastering the most popular Analytics tool. "R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists.
Below is a blog that will help you understand the significance of R and Data Science:
Edureka Certification Process:
At the end of your course, you will work on a real time Project. You will receive a Problem Statement along with a data-set to work.
Once you are successfully through the project (Reviewed by an expert), you will be awarded a certificate with a performance-based grading.
If your project is not approved in 1st attempt, you can take extra assistance for any of your doubts to understand the concepts better and reattempt the Project free of cost.
We partnered with Edureka. We provide up to 35% discount from the actual course fee.
Yes, the access to the course material will be available for lifetime once you have enrolled into the course.
1. Introduction to Data Analytics
Learning Objectives - This module introduces you to some of the important keywords in R like Business Intelligence, Business Analytics, Data and Information. You can also learn how R can play an important role in solving complex analytical problems. This module tells you what is R and how it is used by the giants like Google, Facebook, Bank of America, etc. Also, you will learn use of 'R' in the industry, this module also helps you compare R with other software in analytics, install R and its packages.
Topics - Introduction to terms like Business Intelligence, Business Analytics, Data, Information, how information hierarchy can be improved/introduced, understanding Business Analytics and R, knowledge about the R language, its community and ecosystem, understand the use of 'R' in the industry, compare R with other software in analytics, Install R and the packages useful for the course, perform basic operations in R using command line, learn the use of IDE R Studio and Various GUI, use the ‘R help’ feature in R, knowledge about the worldwide R community collaboration.
2. Introduction to R Programming
Learning Objectives - This module starts from the basics of R programming like datatypes and functions. In this module, we present a scenario and let you think about the options to resolve it, such as which datatype should one to store the variable or which R function that can help you in this scenario. You will also learn how to apply the 'join' function in SQL.
Topics - The various kinds of data types in R and its appropriate uses, the built-in functions in R like: seq(), cbind (), rbind(), merge(), knowledge on the various subsetting methods, summarize data by using functions like: str(), class(), length(), nrow(), ncol(), use of functions like head(), tail(), for inspecting data, Indulge in a class activity to summarize data, dplyr package to perform SQL join in R
3. Data Manipulation in R
Learning Objectives - In this module, we start with a sample of a dirty data set and perform Data Cleaning on it, resulting in a data set, which is ready for any analysis. Thus using and exploring the popular functions required to clean data in R.
Topics - The various steps involved in Data Cleaning, functions used in Data Inspection, tackling the problems faced during Data Cleaning, uses of the functions like grepl(), grep(), sub(), Coerce the data, uses of the apply() functions.
4. Data Import Techniques in R
Learning Objectives - This module tells you about the versatility and robustness of R which can take-up data in a variety of formats, be it from a csv file to the data scraped from a website. This module teaches you various data importing techniques in R.
Topics - Import data from spreadsheets and text files into R, import data from other statistical formats like sas7bdat and spss, packages installation used for database import, connect to RDBMS from R using ODBC and basic SQL queries in R, basics of Web Scraping.
5. Exploratory Data Analysis
Learning Objectives - In this module, you will learn that exploratory data analysis is an important step in the analysis. EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis. You will also learn about the various tasks involved in a typical EDA process.
Topics - Understanding the Exploratory Data Analysis(EDA), implementation of EDA on various datasets, Boxplots, whiskers of Boxplots. understanding the cor() in R, EDA functions like summarize(), llist(), multiple packages in R for data analysis, the Fancy plots like the Segment plot, HC plot in R.
6. Data Visualization in R
Learning Objectives - In this module, you will learn that visualization is the USP of R. You will learn the concepts of creating simple as well as complex visualizations in R.
Topics - Understanding on Data Visualization, graphical functions present in R, plot various graphs like tableplot, histogram, Boxplot, customizing Graphical Parameters to improvise plots, understanding GUIs like Deducer and R Commander, introduction to Spatial Analysis.
7. Data Mining: Clustering Techniques
Learning Objectives - This module lets you know about the various Machine Learning algorithms. The two Machine Learning types are Supervised Learning and Unsupervised Learning and the difference between the two types. We will also discuss the process involved in 'K-means Clustering', the various statistical measures you need to know to implement it in this module.
Topics - Introduction to Data Mining, Understanding Machine Learning, Supervised and Unsupervised Machine Learning Algorithms, K-means Clustering.
8. Data Mining: Association Rule Mining & Collaborative filtering
Learning Objectives - In this module, you will learn how to find the associations between many variables using the popular data mining technique called the "Association Rule Mining", and implement it to predict buyers' next purchase. You will also learn a new technique that can be used for recommendation purpose called "Collaborative Filtering". Various real-time based scenarios are shown using these techniques in this module.
Topics - Association Rule Mining, User Based Collaborative Filtering (UBCF), Item Based Collaborative Filtering (IBCF)
9. Linear and Logistic Regression
Learning Objectives - This module touches the base of 'Regression Techniques’. Linear and logistic regression is explained from the basics with the examples and it is implemented in R using two case studies dedicated to each type of Regression discussed.
Topics - Linear Regression, Logistic Regression.
10. Anova and Sentiment Analysis
Learning Objectives - This module tells you about the Analysis of Variance (Anova) Technique. The algorithm and various aspects of Anova have been discussed in this module. Additionally, this module also deals with Sentiment Analysis and how we can fetch, extract and mine live data from Twitter to find out the sentiment of the tweets.
Topics - Anova, Sentiment Analysis.
11. Data Mining: Decision Trees and Random Forest
Learning Objectives - This module covers the concepts of Decision Trees and Random Forest. The algorithm for creation of trees and classification of decision trees and the various aspects like the Impurity function Gini Index, Pruning, Entropy etc are extensively taught in this module. The algorithm of Random Forests is discussed in a step-wise approach and explained with real-life examples. At the end of the class, these concepts are implemented on a real-life data set.
Topics - Decision Tree, the 3 elements for classification of a Decision Tree, Entropy, Gini Index, Pruning and Information Gain, bagging of Regression and Classification Trees, concepts of Random Forest, working of Random Forest, features of Random Forest, among others.
12. Project Work
Learning Objectives - This module discusses various concepts taught throughout the course and their implementation in a project.
Topics - Analyze census data to predict insights on the income of the people, based on the factors like: age, education, work-class, occupation using Decision Trees, Logistic Regression and Random Forest. Analyze the Sentiment of Twitter data, where the data to be analyzed is streamed live from twitter and sentiment analysis is performed on the same.