We don’t expect any prior knowledge from your side. However, a basic knowledge of programming language can be helpful.
R programming is a statistical language for Data Science specialization that is finding higher adoption rates today, thanks to its extensible nature. It can be widely deployed for various applications and can be easily scaled. Taking up this R Programming training to learn R tool will hence help you grab high-paying jobs offered by large companies.
R language for statistical programming, various features of R, introduction to RStudio, statistical packages, familiarity with different data types and functions, learning to deploy them in various scenarios, use SQL to apply ‘join’ function, components of RStudio like code editor, visualization and debugging tools and learn about R-bind
R functions, code compilation and data in well-defined format called R Packages, R Package structure, package metadata and testing, CRAN (Comprehensive R Archive Network), vector creation and variables values assignment
R functionality, Rep function, generating repeats, sorting and generating factor levels, transpose and stack function
Introduction to matrix and vector in R, understanding various functions like Merge, Strsplit, Matrix manipulation, rowSums, rowMeans, colMeans, colSums, sequencing, repetition, indexing and other functions
Understanding subscripts in plots in R, how to obtain parts of vectors, using subscripts with arrays, as logical variables, with lists and understanding how to read data from external files
Generate plots in R, graphs, bar plots, line plots, histograms and components of a pie chart
Understanding analysis of variance (ANOVA) statistical technique, working with pie charts and histograms and deploying ANOVA with R, one-way ANOVA and two-way ANOVA
K-Means clustering for cluster and affinity analysis, cluster algorithm, cohesive subset of items, solving clustering issues, working with large datasets, association rule mining affinity analysis for data mining and analysis and learning co-occurrence relationships
Introduction to Association Rule Mining, various concepts of Association Rule Mining, various methods to predict relations between variables in large datasets, algorithm and rules of Association Rule Mining and understanding single cardinality
Understanding what is simple linear regression, various equations of line, slope, Y-intercept regression line, deploying analysis using regression, the least square criterion, interpreting the results and standard error to estimate and measure of variation
Scatter plots, two-variable relationship, simple regression analysis and line of best fit
Deep understanding of the measure of variation, the concept of co-efficient of determination, F-test, the test statistic with an F-distribution, advanced regression in R and prediction linear regression
Logistic regression mean and logistic regression in R
Advanced logistic regression, understanding how to do prediction using logistic regression, ensuring if the model is accurate, understanding sensitivity and specificity, confusion matrix, what is ROC, a graphical plot illustrating binary classifier system and ROC curve in R for determining sensitivity/specificity trade-offs for a binary classifier
Detailed understanding of ROC, area under ROC curve, converting the variable, data set partitioning, understanding how to check for multicollinearity, how two or more variables are highly correlated, building of model, advanced data set partitioning, interpreting of the output, predicting the output, detailed confusion matrix and deploying the Hosmer-Lemeshow test for checking whether the observed event rates match the expected event rates
Data analysis with R, understanding the Wald test, MC Fadden’s pseudo R-squared, the significance of the area under ROC curve, Kolmogorov–Smirnov chart which is a non-parametric test of one-dimensional probability distribution
Connecting to various databases from the R environment, deploying the ODBC tables for reading the data and visualization of the performance of the algorithm using confusion matrix
Creating an integrated environment for deploying R on Hadoop platform, working with R Hadoop, RMR package and R Hadoop integrated programming environment and R programming for MapReduce jobs and Hadoop execution
Logistic Regression Case Study
In this case study, you will get a detailed understanding of the advertisement spends of a company that will help to drive more sales. You will deploy logistic regression to forecast the future trends, detect patterns and uncover insights and more, all through the power of R programming. Due to this, the future advertisement spends can be decided and optimized for higher revenues.
Multiple Regression Case Study
You will understand how to compare the miles per gallon (MPG) of a car based on various parameters. You will deploy multiple regression and note down the MPG for the car make, model, speed, load conditions, etc. It includes the model building, model diagnostic and checking the ROC curve, among other things.
Receiver Operating Characteristic (ROC) Case Study
You will work with various data sets in R, deploy data exploration methodologies, build scalable models, predict the outcome with highest precision, diagnose the model that you have created with various real-world data, check the ROC curve and more.
Domain: Restaurant Revenue Prediction
Data set: Sales
Project Description: This project involves predicting the sales of a restaurant on the basis of certain objective measurements. This project will give real-time industry experience on handling multiple use cases and deriving the solutions. This project gives insights about feature engineering and selection.
Domain: Data Analytics
Objective: The project is meant to predict the class of a flower using its petal’s dimensions.
Objective: The project aims to find the most impacting factors in the preferences of pre-paid model and to identify which all are the variables highly correlated with impacting factors.
Domain: Stock Market
Objective: This project focuses on Machine Learning by creating predictive data model to predict future stock prices.
Intellipaat offers a comprehensive training in R Programming language. With this industry-designed training, you will master various aspects of graphical representation, statistical analysis and reporting. This training will also make you proficient in the concepts of functions, data structures, variables and flow of control. Upon the successful completion of the training, you will be awarded the Intellipaat R Certification.
This course is designed for clearing the Intellipaat R Certification exam.
As part of this training, you will be working on real-time projects and assignments that have immense implications in the real-world industry scenarios, thus helping you fast-track your career effortlessly.
At the end of this training program, there will be a quiz that perfectly reflects the type of questions asked in the certification exam and helps you score better marks.
The certification will be awarded upon the completion of the project work (after the expert review) and upon scoring at least 60% marks in the quiz. Intellipaat certification is well recognized in top 80+ MNCs like Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, etc.
A Senior Software Architect at NextGen Healthcare who has previously worked with IBM Corporation, Suresh Paritala has worked on Big Data, Data Science, Advanced Analytics, Internet of Things and Azure, along with AI domains like Machine Learning and Deep Learning. He has successfully implemented high-impact projects in major corporations around the world.
A renowned Data Scientist who has worked with Google and is currently working at ASCAP, Samanth Reddy has a proven ability to develop Data Science strategies that have a high impact on the revenues of various organizations. He comes with strong Data Science expertise and has created decisive Data Science strategies for Fortune 500 corporations.
An experienced Blockchain Professional who has been bringing integrated Blockchain, particularly Hyperledger and Ethereum, and Big Data solutions to the cloud, David Callaghan has previously worked on Hadoop, AWS Cloud, Big Data and Pentaho projects that have had major impact on revenues of marquee brands around the world.
"PMI®", "PMP®" and "PMI-ACP®" are registered marks of the Project Management Institute, Inc.
The Open Group®, TOGAF® are trademarks of The Open Group.
The Swirl logoTM is a trade mark of AXELOS Limited.
ITIL® is a registered trade mark of AXELOS Limited.
PRINCE2® is a Registered Trade Mark of AXELOS Limited.
Certified ScrumMaster® (CSM) and Certified Scrum Trainer® (CST) are registered trademarks of SCRUM ALLIANCE®
Professional Scrum Master is a registered trademark of Scrum.org