Analytics using R Programming

The following topics will be covered in our Analytics using R Programming Online Training:

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Analytics using R Programming: Data Analytics Using R • Analytics using R Programming: What is Data Analytics • Who uses R and how. • What is R • Why to use R • R products • Get Started with R

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Introduction to R Programming • Different data types in R and when to use which one • Function in R • Various subsetting methods. • Summarizing the data using str(), class(), nrow(), ncol() and length() • Use functions like head() and tail() for inspecting data • Indulge into a class activity to summarize the data.

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Data Manipulation in R • Know the various steps involved in data cleaning • Functions used for data inspection • Tacking the problem faced during data cleaning • How and when to use functions like grep, grepl, sub, gsub, regexpr, gregexpr, strsplit • How to coerce the data. • Apply family functions. Copyright @ 2015 Learntek. All Rights Reserved.

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Data Import Technique in R • Import data from spreadsheets and text files into R • Install packages used for data import • Connect to RDBMS from R using ODBC and basic sql queries in R • Perform basic web scrapping.

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Data Exploration in R • What is data exploration • Data exploring using Summary(), mean(), var(), sd(), unique() • Using Hmisc package and using summarize, aggregate function • Learning correlation and cor() function and visualizing the same using corrgram • Visualizing data using plot and its different flavours • Boxplots • Dist function Copyright @ 2015 Learntek. All Rights Reserved.

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Data Visualization in R • Gain understanding on data visualization • Learn the various graphical functions present in R • Plot various graph like tableplot, histogram, boxplot etc. • Customize graphical parameters to improvise the plots. • Understand GUIs like Deducer and R commander • Introduction to spatial analysis.

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Data Mining : Clustering Techniques • Introduction to data mining • Understand machine learning • Supervised and unsupervised machine learning algos • K means clustering

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Data Mining : Association Rules Mining and Sentiment Analysis â&#x20AC;˘ Understanding associate rule mining â&#x20AC;˘ Understanding sentiment analysis

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Linear and Logistic Regression â&#x20AC;˘ Understand linear regression â&#x20AC;˘ Understand logistic regression

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Annova and Predictive Regression â&#x20AC;˘ Understand Annova â&#x20AC;˘ Understand predictive regression

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Data Mining : Decision Tree and Random Forest • • • • • • •

Understand what is Decision Tree Algos for Decision Tree Greedy approach : Entropy and information gain. A perfect decision tree Understand the concept of random forest How random forest work Features of random forest

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Turn static files into dynamic content formats.

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