R Programming for Data Science Training Course - Houston, USA

1. Introduction to R for Data Science specialization
2. Various functions in R programming
3. Linear and non-linear regression
4. K-Means clustering and hierarchical clustering
5. Data input and output with respect to R tool
6. Database connectivity with R program

• Software Engineers and Data Analysts
• Business Intelligence Professionals
• SAS Developers wanting to learn the open-source technology
• Those aspiring for a career in Data Science

We don’t expect any prior knowledge from your side. However, a basic knowledge of programming language can be helpful.

Houston is the top city in the United States of America when it comes to being a hotbed for technological innovations and creating tech jobs. Due to the huge impetus given to business innovations and advancements here, there is a huge deployment of R tool for Data Science applications. This is creating huge number of job opportunities in R programming in Houston, Texas.

The R programming market is going through a huge boom in Houston, Texas. Due to the aggressive nature of the business enterprises in Houston, there is a widespread usage of R language for deriving valuable business insights. This growth in the R tool will only intensify in future, thus creating lucrative career opportunities for those who are trained and certified in R programming language.

• 70% of companies say that Analytics is integral to making decisions – IBM Study
• 19% is the annual growth rate of the Analytics market – Pringle & Company
• R Programmers can earn excess of $110,000 per year – O’Reilly Survey

When it comes to Data Science, the R programming and statistical computing tool is the top one. Due to this, there are huge number of job opportunities in R for Data Science specialization. Intellipaat is offering the tailor-made training for R programming which is perfect for working in the Data Science domain thus giving you a huge career advancement and security.

Bangalore, Melbourne, Chicago, Hyderabad, San Francisco, London, Toronto, New York, India, Dubai, Jersey City, Los Angeles, San Jose, Singapore and Sydney

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.

Project 1

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.

Project 2

Domain: Data Analytics

Objective: The project is meant to predict the class of a flower using its petal’s dimensions.

Project 3

Domain: Finance

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.

Project 4

Domain: Stock Market

Objective: This project focuses on Machine Learning by creating predictive data model to predict future stock prices.