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Analytics using R Programming

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Analytics using R Programming

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  1. Analytics using R Programming

  2. The following topics will be covered in our Analytics using R Programming Online Training: Copyright @ 2015 Learntek. All Rights Reserved.

  3. 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 Copyright @ 2015 Learntek. All Rights Reserved.

  4. 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. Copyright @ 2015 Learntek. All Rights Reserved.

  5. 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.

  6. 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. Copyright @ 2015 Learntek. All Rights Reserved.

  7. 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.

  8. 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. Copyright @ 2015 Learntek. All Rights Reserved.

  9. Data Mining : Clustering Techniques • Introduction to data mining • Understand machine learning • Supervised and unsupervised machine learning algos • K means clustering Copyright @ 2015 Learntek. All Rights Reserved.

  10. Data Mining : Association Rules Mining and Sentiment Analysis • Understanding associate rule mining • Understanding sentiment analysis Copyright @ 2015 Learntek. All Rights Reserved.

  11. Linear and Logistic Regression • Understand linear regression • Understand logistic regression Copyright @ 2015 Learntek. All Rights Reserved.

  12. Annova and Predictive Regression • Understand Annova • Understand predictive regression Copyright @ 2015 Learntek. All Rights Reserved.

  13. 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 Copyright @ 2015 Learntek. All Rights Reserved.

  14. Copyright @ 2015 Learntek. All Rights Reserved.

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