About The Course
This course is designed for professionals who aspire to learn ‘R’ language for Analytics. The course starts from the very basics like: Introduction to R programming, how to import various formats of Data, manipulate it, etc. to advanced 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 ‘Mastering Data Analytics with R’ at Edureka, you should be able to:
1. Understand the fundamentals of ‘R’ programming
2. Explore data manipulation with functions like grepl(), sub(), apply(),etc.
3. Apply various Data Importing techniques in R
4. Perform exploratory Data Analysis
5. Learn where to use functions- cor(), llist(), hclust(), lm(), glm(), etc.
6. Apply Data Visualisation to create fancy plots
7. Understand Machine Learning (ML) Techniques
8. Apply Data Mining and understand Decision Trees and Random Forests
9. Implement k-means clustering algorithm to perform Text Analysis
10. Study Association Rule Mining to predict buyers’ next purchase
11. Explore and understand Sentiment Analysis
12. Understand the concept of Regression
13. Implement Linear and Logistic Regression and understand Anova
14. Apply Predictictive Analytics to predict outcomes
15. Work on a real life Project, implementing R Analytics to create 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.
Why Learn Data Analytics with R?
‘Mastering Data Analytics with ‘R’ at Edureka will prepare you to perform analytics and build models for real world data science problems. It is the world’s most powerful programming language for statistical computing and graphics making it a must know language for the aspiring Data Scientists. ‘R’ wins strongly on Statistical Capability, Graphical capability, Cost and rich set of packages. The following blog will help you understand the significance of R Analytics training:
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.
Which Case-Studies will be a part of the Course?
Towards the end of the Course, you will be working on a live project. You can choose any of the following Problem Statements as your Project work :
Project Title: 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 Multicollinearity, handle outliers and treat missing data.
3. Create training and validation datasets 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.
Project Title: 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.
Can I work on my own Use-Case?
Sure, you can. You can either choose one of the Use-Cases from the Primose Repository or create your own.