Business Analytics with R
OFFER: Get "Self-Paced" MS Excel or Solr or Mahout absolutely free with this course!
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About The Course
This course is designed for professionals who aspire to learn 'R' language for Analytics. Practical approach of learning has been followed in order to provide a real time experience and make you think like an analyst. The course starts from the very basics like: Introduction to R programming, how to import various formats of Data, manipulate it, etc to some advanced R topics like: Data Mining Technique, performing Predictive Analysis to find optimum results based on past data, Data Visualisation using R Commander, Deducer, etc.
After the completion of 'Business Analytics with R' at
Edureka, you should be able to:
1. Understand the fundamentals of 'R'.
3. Get familiar with R Analytics as a career option with practical knowledge of some of the most in-demand techniques like Predictive Analytics, Association Rule Mining to perform prediction on the buyer's next purchase, Data Visualisation to plot, etc.
Who should go for this course?
This course is meant for all those students and professionals who are interested in working in industry analytics 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 'Business Analysts' in near future!
The pre-requisites for learning 'Business Analytics with R' include basic mathematics and good analytical skills. We provide a complimentary course "Statistics Essentials for R" to all the participants who enroll for the Business Analytics with R Training. This course helps you brush up your statistics skills. The good news is that - as this is an applied course, the focus will be on real-world case studies rather than just the theory.
Towards the end of the Course, you will be working on a live project which will be a large dataset and use your analytical and visual skill to bulid attractive projectsProject Title: Census Data Analysis
Description : Analyze the census data and predict whether the income exceeds $50K per year. Follow end to end modelling process involving:
Perform Exploratory Data Analysis and establish hypothesis of the data. Test for Multicollinearity, handle outliers and treat missing data. Create training and validation datasets using Stratified Random Sampling(SRS) of data. Fit Classification model on training set (Logistic Regression/Decision Tree) Perform validation of the models (ROC curve, Confusion Matrix) Freeze the final model.
Project Title: Sentiment Analysis of Twitter Data
Description : A sports gear company is planning to brand themselves by putting their company logo on the dress 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 chosed 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.
Why Learn Business Analytics with R?
'Business Analytics with R' at Edureka will
prepare you to:
1. Learn R programming language and use it in analytical projects including multiple industrial domains and scenarios