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:
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 Edureka Repository or create your own.
1. Introduction to Data Analytics
Learning Objectives - This module tells you what Business
Analytics is and how R can play an important role in solving complex
analytical problems. It tells you what is R and how it is used by the
giants like Google, Facebook, Bank of America, etc.
Understand Business Analytics and R, Knowledge on the R
language, 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
Objectives - This module starts from the very basics of R
programming like datatypes and functions. We present a scenario
and let you think about the options to resolve it. E.g which
datatype would you use to store the variable or which R function can
help you in this scenario.
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.
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.
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, Understanding the
cor() in R, EDA functions like summarize(), llist(), Multiple packages
in R for data analysis, The Fancy plots like Segment plot, HC plot in R.
6. Data Visualization in R
Leaning 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.
Understanding on Data Visualization, Graphical functions present
in R, Plot various graphs like tableplot, histogram, boxplot,
Customizing Graphical Parameters to improvise the plots,
Understanding GUIs like Deducer and R Commander, Introduction to
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 'K-means
Clustering' and implement it in this module.
Introduction to Data Mining, Understanding Machine Learning,
Supervised and Unsupervised Machine Learning Algorithms, K-means Clustering.
8. Data Mining: Association Rule Mining and Sentiment Analysis
Objectives - This module discusses the very popular
'Association Rule Mining' Technique. The algorithm
and various aspects of the same have been discussed in this
module.We will also discuss what ‘Sentiment Analysis’ is and how
we can fetch, extract and mine live data from twitter to find out
the sentiment of the tweets.
Topics - Association Rule Mining, Sentiment Analysis.
9. Linear and Logistic Regression
Learning Objectives - This module touches the base with the 'Regression Techniques’. Linear and logistic regression is explained from the very 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 Predictive Analysis
Learning Objectives - This module tells you about the Analysis
of Variance (Anova) Technique. Another topic that is discussed in this
module is Predictive Analysis.
Topics - Anova, Predictive 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 forests is discussed in a step wise approach and explained with
examples. At the end of the class, these are the concepts implemented
on a real-life data set. The case studies are present in the LMS.
Decision Trees, Algorithm for creating Decision Trees,Greedy
Approach: Entropy and Information Gain, Creating a Perfect
Decision Tree, Classification Rules for Decision Trees,
Concepts of Random Forest, Working of Random Forest, Features of
Learning Objectives - This
module discusses the 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, etc.
Though it is not mandatory, elementary statistical knowledge is desirable. We provide you a complimentary course on 'Statistics Essentials for Analytics', an asset of 3 video lectures along with assignments and sample codes which will help you brush up your Statistics skills.
For doing your practicals, you would need to install R set-up or R-Studio on your system. The step-wise installation guides for setting up the environment on various operating systems are present in the LMS. In case you come across any doubt, the 24*7 support team will promptly assist you.
You will never lose any lecture. You can choose either of the two options: 1. View the class presentation and recordings that are available for online viewing through the LMS. 2. You can attend the missed session, in any other live batch. Please note, access to the course material will be available for lifetime once you have enrolled into the course.
All our instructors are working professionals from the Industry and have at least 10-12 yrs of relevant experience in various domains. They are subject matter experts and are trained by Edureka for providing online training so that participants get a great learning experience.
Edureka is the largest online education company and lots of recruitment firms contacts us for our students profiles from time to time. Since there is a big demand for this skill, we help our certified students get connected to prospective employers. We also help our customers prepare their resumes, work on real life projects and provide assistance for interview preparation. Having said that, please understand that we don't guarantee any placements however if you go through the course diligently and complete the project you will have a very good hands on experience to work on a Live project.