R programming for data science| data analytics with R

Course Overview

Introduction– You’ll ascertain a way to use RStudio, the setting that permits you to figure with R. This course also will cowl the computer code applications and tools that are unique to R, like R packages. You’ll discover however R allows you to clean, organize, analyze, visualize, and report information in new and additional powerful ways in which. Current Google information analysts can still instruct and supply you with active ways in which to accomplish common information analyst tasks with the simplest tools and resources.Learners WHO complete this data analytics with r are going to be equipped to use for introductory-level jobs as data analysts. No previous experience is necessary.

About Data analysis with R certification– Prepare for a new career within the high-growth field of data analytics, no expertise or degree needed. Get skilled training designed by Google and have the chance to attach with high employers. There area unit 380,000 U.S. job openings in data analytics, data analytics is that the collection, transformation, and organization of knowledge so as to draw conclusions, create predictions, and drive informed decision making. After completing the course Xcelit pro provides data science with r certification which is able to help you to start your career. –

Key features

What is EDA?
R basics
Explore one variable

Pre-requisites

Desire to master data analysis.
Normal, uniform, and skewed distributions.
Histograms and box plots.

Course Syllabus

DATA ANALYSIS WITH R Course Syllabus

Data Analytical With R
Module 1 : What is Data Analytics

R tools and their uses in Business Analytics
Objectives
Analytics
Where is analytics applied?
Responsibilities of a data scientist
Problem definition
Summarizing data
Data collection

Module 2 : About R:

Difference between R and other analytical languages
Different data types in R
Built in functions of R: seq(), cbind (), rbind(), merge().
Subsetting methods
Use of functions like str(), class(), length(), nrow(), ncol(),head(), tail()

Module 3 : Data manipulation in R

Steps involved in data cleaning
Problems and solutions for Data cleaning
Data inspection
Use of functions grepl(), grep(), sub()
Use of apply() function
Coerce the data

Module 4 : Data Import techniques

How R handles data in a variety of formats
Importing data from csv files, spreadsheets and text files
Import data from other statistical formats like sas7bdat and sps
Packages installation used for database import
Connect to RDBMS from R using ODBC and basic SQL queries in R
Basics of Web Scraping

Module 5 : Exploratory Data analysis

Understanding the Exploratory Data Analysis(EDA)
Implementation of EDA on various datasets
Boxplots
Understanding the cor() in R
list()
Multiple packages in R for data analysis
Segment plot HC plot in R

Module 6 : Data Visualization in R

Understanding on Data Visualization
Graphical functions present in R
Plot various graphs like tableplot
Histogram
Box Plot
Customizing Graphical Parameters to improvise the plots
Understanding GUIs like Deducer and R Commander
Introduction to Spatial Analysis

Module 7 : Data Mining: Clustering Techniques

Introduction to Data Mining
Understanding Machine Learning
Supervised and Unsupervised Machine Learning Algorithms
K-means Clustering

Module 8 : Data Mining: Association Rule Mining and Sentiment Analysis

Association Rule Mining
Sentiment Analysis

Module 9 : Linear and Logistic Regression

Linear Regression
Logistic Regression

Module 10 :Anova
Module 11 : Predictive Analysis
Module 12 :More on Data Mining

Data analysis with R

Data analysis with R

Course Overview

Course Syllabus

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Specifications