The document outlines an online training program for analytics using R programming, covering topics such as data analytics, manipulation, import techniques, exploration, visualization, and various data mining methods. It introduces foundational concepts like different data types, functions, machine learning algorithms, and regression analysis, alongside practical applications with visualizations and decision trees. The training aims to equip learners with comprehensive skills in R for effective data analysis.

1. Analytics using R Programming

2. The following topics will be covered in our
Analytics using R Programming
Online Training:
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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
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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.
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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.
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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.
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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
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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.
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9. Data Mining : Clustering Techniques
• Introduction to data mining
• Understand machine learning
• Supervised and unsupervised machine learning algos
• K means clustering
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10. Data Mining : Association Rules Mining and
Sentiment Analysis
• Understanding associate rule mining
• Understanding sentiment analysis
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11. Linear and Logistic Regression
• Understand linear regression
• Understand logistic regression
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12. Annova and Predictive Regression
• Understand Annova
• Understand predictive regression
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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
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