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
Get Started with R
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.
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.
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.
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
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.
Data Mining : Clustering Techniques
Introduction to data mining
Understand machine learning
Supervised and unsupervised machine learning algos
K means clustering
Data Mining : Association Rules Mining and Sentiment Analysis
Understanding associate rule mining
Understanding sentiment analysis
Linear and Logistic Regression
Understand linear regression
Understand logistic regression
Annova and Predictive Regression
Understand predictive regression
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
Duration & Timings :
Duration – 30 Hours.
Training Type: Online Live Interactive Session.
Access to Class Recordings.
Weekday Session – Mon – Thu 8:30 PM to 10:30 PM (EST) – 4 Weeks. January 6, 2020.