About R Programming Big Data:

The R language is an environment for statistical computation and graphical representation of data. R provides a wide variety of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering) and graphical techniques, and is highly extensible. It is an open source language and the source code is written primarily in C and FORTRAN.

The strength of R lies is the well-packaged ecosystem. There are a lot of inbuilt functionalities which are very user-friendly and easy to use for statisticians. Data manipulation and data plotting with the help of graphs is very easy using R.

Our training will help you to assimilate the R language concepts and apply them in the real world of data mining in Big Data era. We teach to create a reproducible high-quality data analysis using R language. Our training course will help all the individuals who handle data mining and data handling irrespective of their domains. We teach from scratch to advanced level.

Course overview:

Our R language training course is specially designed to provide the required knowledge and skills to become a successful data analytics professional. Our course covers basic concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc. Our training course is well designed for beginners who want to start their career as a Business Analyst and experienced statisticians, Data miners and programmers.

Pre-requisites:

To take this course, an individual should have basic computer programming knowledge. This will help you to understand R programming concepts.

It is also advised to have basic Mathematics, Statistics, and Economics knowledge to understand and apply R programming.

This course is designed for software professionals, statistical analysts, and data miners who are looking forward to developing statistical software using R programming. Our training course is helpful to beginners to understand R language from basics and become Business Analysts in the near future.

Course Content:

Introduction to Data Analytics:

• Introduction to Data Analytics terminology
• Business Intelligence, Business Analytics, Data, Information
• Information hierarchy can be improved/introduced
• Understand Business Analytics and R
• Knowledge about the R language, its 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.

Introduction to R:

• R language for statistical programming
• The various features of R
• Introduction to R Studio
• The statistical packages
• Familiarity with different data types and functions
• Learning to deploy them in various scenario
• Use SQL to apply ‘join’ function
• Components of R Studio like code editor, visualization and debugging tools, learn about R-bind.

R-Packages:

• R Functions
• Code compilation and data in well-defined format called R-Packages
• Learn about R-Package structure
• Package metadata and testing
• CRAN (Comprehensive R Archive Network)
• Vector creation and variables values assignment.

Data Manipulation:

• The various steps involved in Data Cleaning, functions used in Data Inspection,
• Problems faced during Data Cleaning, uses of the functions like grepl(), grep(), sub(), Coerce the data, uses of the apply() functions.

Data Import Techniques:

• 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.

Exploratory Data Analysis:

• Understanding the Exploratory Data Analysis (EDA),
• Implementation of EDA on various datasets, Boxplots, whiskers of Boxplots.
• Understanding the cor() in R, EDA functions like summarize(), llist(), multiple packages in R for data analysis,
• The Fancy plots like the Segment plot, HC plot in R.

Data Visualization In R:

• Understanding on Data Visualization
• Graphical functions present in R
• Plot various graphs like tableplot, histogram, Boxplot, customizing Graphical Parameters to improvise plots
• Understanding GUIs like Deducer and R Commander
• Introduction to Spatial Analysis.

Data Mining Clustering Techniques:

• Introduction to Data Mining
• Understanding Machine Learning
• Supervised and Unsupervised Machine Learning Algorithms, K-means clustering.

Data Mining - Association Rule:

• Association Rule Mining, User Based Collaborative Filtering (UBCF), Item Based Collaborative Filtering (IBCF)

Sorting Data frame:

• R functionality
• Rep Function, generating Repeats
• Sorting and generating Factor Levels
• Transpose and Stack Function.

Matrices and Vectors:

• Introduction to matrix and vector in R,
• Understanding the various functions like Merge,
• Strsplit, Matrix manipulation, rowSums, rowMeans, colMeans, colSums, sequencing, repetition, indexing and other functions.

Reading data from external files:

• Understanding subscripts in plots in R,
• How to obtain parts of vectors,
• Using subscripts with arrays, as logical variables, with lists, understanding how to read data from external files.

Generating plots:

Generate plot in R, Graphs, Bar Plots, Line Plots, Histogram, components of Pie Chart.

Analysis of Variance (ANOVA):

• Understanding Analysis of Variance (ANOVA) statistical technique,
• Working with Pie Charts, Histograms, deploying ANOVA with R, one way ANOVA, two way ANOVA.

K-means Clustering:

• K-Means Clustering for Cluster & 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

Association Rule Mining:

• Introduction to Association Rule Mining
• The various concepts of Association Rule Mining
• Various methods to predict relations between variables in large datasets, the algorithm and rules of Association Rule Mining
• Understanding single cardinality.

Regression in R:

• Understanding what is Simple Linear Regression,
• The various equations of Line, Slope, Y-Intercept Regression Line, deploying analysis using Regression, the least square criterion, interpreting the results, standard error to estimate and measure of variation.

Analyzing Relationship with Regression:

Scatter Plots, Two variable Relationship, Simple Linear Regression analysis, Line of best fit

Advance Regression:

• 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, prediction linear regression.

Logistic Regression:

Logistic Regression Mean, Logistic Regression in R.

Advance Logistic Regression:

• Advanced logistic regression
• Understanding how to do prediction using logistic regression
• Ensuring the model is accurate
• Understanding sensitivity and specificity
• Confusion matrix, what is ROC, a graphical plot illustrating binary classifier system, ROC curve in R for determining sensitivity/specificity trade-offs for a binary classifier.

Receiver Operating Characteristic (ROC):

• Detailed understanding of ROC
• Area under ROC Curve
• Converting the variable
• Data set partitioning, understanding how to check for multi collinearity, 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
• Deploying the Hosmer-Lemeshow test for checking whether the observed event rates match the expected event rates.

Kolmogorov Smirnov Chart:

• 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 non-parametric test of one dimensional probability distribution.

Database connectivity with R:

• Connecting to various databases from the R environment
• Deploying the ODBC tables for reading the data
• Visualization of the performance of the algorithm using Confusion Matrix.

Integrating R with Hadoop:

• Creating an integrated environment for deploying R on Hadoop platform
• Working with R Hadoop
• RMR package and R Hadoop Integrated Programming Environment
• R programming for MapReduce jobs and Hadoop execution.

R Case Studies:

Logistic Regression Case Study

In this case study you have to use logistic regression to arrive at a detailed information about the advertisement spends of a company to drive more sales. With the help of logistic regression, you will forecast the future trends, detect patterns, and give insights using R programming.

Multiple Regression Case Study

In this case study, you will deploy multiple regression to compare the miles per gallon (MPG) of a car based on the various parameters. You have to use parameters like MPG for car make, model, speed, load conditions, etc. You have to include the model building, model diagnostic, checking the ROC curve, among the parameters to be used.

Receiver Operating Characteristic (ROC) case study

In this case, you will deploy data exploration methodologies, build scalable models, and predict the outcome precisely. You will use R programming to work on the data and diagnose the model created and compare with the real world data for ROC.

Certification:

Once you complete the training course successfully, you will be awarded R programmer/ Data Analyst certification. Our certification has industry wide recognition and our certified professionals are placed in many Top MNCs like Cisco, Ford, Mphasis, Nokia, Wipro, Accenture, IBM, Philips, Citi, Ford, Mindtree, BNYMellon etc.

Job and Placement:

Our certified Data Analyst/ R programmers are placed in Fortune 500 companies in various designations like software Engineer, Statistical Programmer, R Programmer etc. Depending on their experience, they are absorbed in various designations in companies. With the boom of Big Data there is huge gap of trained and certified professionals on R to work for data mining jobs. Our certified professionals get jobs easily and can perform excellently handling all the challenges.

The average salary of a data scientist is over $120,000 in the United States according to Indeed!