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.