Analytics using R Programming Online Training

1

• Analytics using R Programming

2

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

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

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.

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.

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.

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

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.

9
Data Mining Clustering Techniques

• Introduction to data mining
• Understand machine learning
• Supervised and unsupervised machine learning
  algos
• K means clustering

10
Data Mining Association Rules Mining and
Sentiment Analysis

• Understanding associate rule mining
• Understanding sentiment analysis

11
Linear and Logistic Regression

• Understand linear regression
• Understand logistic regression

12
Annova and Predictive Regression

• Understand Annova
• Understand predictive regression

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

14
(No Transcript)