R Programming Course Content
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 scenarios, use SQL to apply ‘join’ function, components of R Studio like code editor, visualization and debugging tools, learn about R-bind.
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.
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.
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 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
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 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 multicollinearlity, 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 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, 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 the various parameters. You will deploy multiple regression and note down the MPG for car make, model, speed, load conditions, etc. It includes the model building, model diagnostic, checking the ROC curve, among other things.Receiver Operating Characteristic (ROC) case studyYou 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.
SAS Course Content
Introduction to SAS
Introduction to Base SAS, Installation of SAS tool, Getting started with SAS, various SAS Windows – Log, Explorer, Output, Search, Editor, etc. working with data sets, overview of SAS Functions, Library Types and programming files
SAS Enterprise Guide
Import/Export Raw Data files, reading and sub setting the data set, various statements like WHERE, SET, MergeHands-on Exercise – Import Excel file in workspace, Read data, Export the workspace to save data
SAS Operators & Functions
Various SAS Operators – Arithmetic, Logical, Comparison, various SAS Functions – NUMERIC, CHARACTER, IS NULL, CONTAINS, LIKE, Input/Put, Date/Time, Conditional Statements (Do While, Do Until, If, Else)Hands-on Exercise – Apply logical, arithmetic operators and SAS functions to perform operations
Compilation & Execution
Understanding about Input Buffer, PDV (Backend), learning what is Missover
Defining and Using KEEP and DROP statements, apply these statements, Format and Labels in SAS.Hands-on Exercise – Use KEEP and DROP statements
Creation and Compilation of SAS Data sets
Understanding Delimiter, dataline rules, DLM, Delimiter DSD, raw data files and execution, list input for standard data.Hands-on Exercise – Use delimiter rules on raw data files
The various SAS standard Procedures built-in for popular programs – PROC SORT, PROC FREQ, PROC SUMMARY, PROC RANK, PROC EXPORT, PROC DATASET, PROC TRANSPOSE, , PROC CORR etc.Hands-on Exercise – Use SORT, FREQ, SUMMARY, EXPORT and other procedures
Input statement and formatted input
Reading standard and non-standard numeric inputs with Formatted inputs, Column Pointer Controls, Controlling while a record loads, Line pointer control / Absolute line pointer control, Single Trailing , Multiple IN and OUT statements, DATA LINES statement and rules, List Input Method, comparing Single Trailing and Double Trailing.Hands-on Exercise – Read standard and non-standard numeric inputs with Formatted inputs, Control while a record loads, Control a Line pointer, Write Multiple IN and OUT statements
SAS FORMAT statements – standard and user-written, associating a format with a variable, working with SAS FORMAT, deploying it on PROC Data sets, comparing ATTRIB and FORMAT statements.Hands-on Exercise – Format a variable, deploy format rule on PROC DATA set, Use ATTRIB statement
Understanding PROC GCHART, various Graphs, Bar Charts – Pie, Bar, 3D, plotting variables with PROC GPLOT.Hands-on Exercise – Plot graphs using PROC GPLOT Display charts using PROC GCHART
Interactive Data Processing
SAS advanced data discovery and visualization, point-and-click analytics capabilities, powerful reporting tools.
Data Transformation Function
Character Functions, Numeric Functions, Converting Variable Type.Hands-on Exercise – Use Functions in data transformation
Output Delivery System (ODS)
Introduction to ODS, Data Optimization, How to generate files (rtf, pdf, html, doc) using SASHands-on Exercise – Optimize data, generate rtf, pdf, html and doc files
Macro Syntax, Macro Variables, Positional Parameters in a Macro, Macro StepHands-on Exercise – Write a macro, Use positional parameters
SQL Statements in SAS, SELECT, CASE, JOIN, UNION, Sorting DataHands-on Exercise – Create sql query to select and add a condition
Use a CASE in select query
Advanced Base SAS
Base SAS web-based interface and ready-to-use programs, advanced data manipulation, storage and retrieval, descriptive statistics.Hands-on Exercise – Use web UI to do statistical operations
Report Enhancement, Global Statements, User-defined Formats, PROC SORT, ODS Destinations, ODS Listing, PROC FREQ, PROC Means, PROC UNIVARIATE, PROC REPORT, PROC PRINTHands-on Exercise – Use PROC SORT to sort the results, List ODS, Find mean using PROC Means, print using PROC PRINT
R Programming Projects
Project 1Domain – Restaurant Revenue PredictionData set – SalesProject 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 derive the solution. This project gives insights about feature engineering and selection.
Project 2Domain – Data AnalyticsObjective – To predict about the class of a flower using its petal’s dimensions
Project 3Domain – FinanceObjective – The project aims to find the most impacting factors in preferences of pre-paid model, also identifies which are all the variables highly correlated with impacting factors
Project 4Domain – Stock MarketObjective – This project focuses on Machine Learning by creating predictive data model to predict future stock prices
Project 1 – Build analytical solution for patients taking medicinesDomain: Health CareObjective – This project aims to find out descriptive statistics & subset for specific clinical data problems. It will give them brief insight about BASE SAS procedures and data steps.
Project 2 – Build revenue projections reportsDomain: SalesObjective – This project will give you hands-on experience in working with the SAS data analytics and business intelligence tool. You will be working on the data entered in a business enterprise setup, aggregate, retrieve and manage that data. You will learn to create insightful reports and graphs and come up with statistical and mathematical analysis to scientifically predict the revenue projection for a particular future time frame. Upon completion of the project you will be well-versed in the practical aspects of data analytics, predictive modeling, and data mining.
Project 3Domain: Finance MarketObjective – The project aims to find the most impacting factors in preferences of pre-paid model, also identifies which are all the variables highly correlated with impacting factors
Project 4Domain: AnalyticsObjective – k-Means Cluster analysis on Iris dataset to predict about the class of a flower using its petal’s dimensions