Week / Date |
Content |
Week-1
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Day 1
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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.
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Day 2
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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.
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Day 3
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Working with data types
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Day 4
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Data Descriptive Statistics: Summary statistics on different objects.
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Day 5
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Lab Activity:Data summaries, aggregations, subset, with functions on R objects.
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Day 6
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Lab Activity:apply, lapply, summary statistics, advanced summary functions on various objects.
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Week-2
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Day 7
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Data distribution, functions and control statements.
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Day 8
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Lab Activity:Random data generation, data distribution functions: binomial, continuous etc.
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Day 9
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Handling variety of data in R.
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Day 10
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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
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R with Excel, R Markdown creation.
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Day 12
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Lab Activity:Package installation in R with Excel sheet, creating R Markdown and publish in RPUBS cloud.
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Week-3
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Day 13
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No SQL databases with R
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Day 14
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Handling semi structured (XML) data in R.
Lab activity:Restaurant Menu data process
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Day 15
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SQL on data objects.Lab Activity:
SQLDF package installation and SQL queries on data objects in R.
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Day 16
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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
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Missing data and outlier detection, finding hidden relationships among attributes in a data set, correlation and covariation analysis.
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Day 18
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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
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Case study 3: One predictive Model application
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