Analytics using r programming

Oct 4, 20190 likes162 views

Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.

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.
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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
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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
• Understanding associate rule mining
• Understanding sentiment analysis
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Linear and Logistic Regression
• Understand linear regression
• Understand logistic regression
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Annova and Predictive Regression
• Understand Annova
• 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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Analytics using r programming

1. Analytics using R Programming

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. 3

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. 4

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. 5

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. 6

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. 7

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. 8

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. 9

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. 13

Analytics using r programming

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