| Week / Date | Content | 
| Week-1 | Day 1 | R Introduction, R software installation, R Studio setup, creating R script.Simple Arithmetic with R, R objects,checking type of object. Lab Activity:Use R like a calculator: arithmetic operations with objects and without objects, built-in mathematical functions. | 
| Day 2 | Working with different data types and data structures: data cleaning and data transformation techniques. Lab Activity: Working with data types: Reading and writing data, data formats, vectors, lists, arrays, matrices, tables and data frames functions. | 
| Day 3 | Working with data types | 
| Day 4 | Data Descriptive Statistics: Summary statistics on different objects. | 
| Day 5 | Lab Activity:Data summaries, aggregations, subset, with functions on R objects. | 
| Day 6 | Lab Activity:apply, lapply, summary statistics, advanced summary functions on various objects. | 
| Week-2 | Day 7 | Data distribution, functions and control statements. | 
| Day 8 | Lab Activity:Random data generation, data distribution functions: binomial, continuous etc. | 
| Day 9 | Handling variety of data in R. | 
| Day 10 | Lab Activity:R with text, csv, web datasets.Case Study 1: Data exploration on IRIS dataset, cars, MS admission process.
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| Day 11 | R with Excel, R Markdown creation. | 
| Day 12 | Lab Activity:Package installation in R with Excel sheet, creating R Markdown and publish in RPUBS cloud. | 
| Week-3 | Day 13 | No SQL databases with R | 
| Day 14 | Handling semi structured (XML) data in R.Lab activity:Restaurant Menu data process
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| Day 15 | SQL on data objects.Lab Activity:SQLDF package installation and SQL queries on data objects in R.
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| Day 16 | Visualization in R. Lab Activity: Plots: Scatterplot, biplot, corrplot, histogram, boxplot, barplot, lineplot, wordcloud plots, par, legand.Case Study 2 : Visualization on MTCARS and IRIS data sets
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| Day 17 | Missing data and outlier detection, finding hidden relationships among attributes in a data set, correlation and covariation analysis. | 
| Day 18 | Data model using linear and logistics regression, residual analysis and model evaluation.Lab Activity:Hidden relation among heart weight and body weight, predictive model on heart weight
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| Day 19 | Case study 3: One predictive Model application |