Data analysis with R

Data analysis with R

This course r programming for data science . in this course can equip you with the talents required to use introductory-level data analyst jobs. During this course, you’ll study the programming language referred to as 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


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

Course Syllabus


Data Analytical With R
Module 1 : What is Data Analytics

    1. R tools and their uses in Business Analytics
    2. Objectives
    3. Analytics
    4. Where is analytics applied?
    5. Responsibilities of a data scientist
    6. Problem definition
    7. Summarizing data
    8. Data collection

Module 2 : About R:

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

Module 3 : Data manipulation in R

    1. Steps involved in data cleaning
    2. Problems and solutions for Data cleaning
    3. Data inspection
    4. Use of functions grepl(), grep(), sub()
    5. Use of apply() function
    6. Coerce the data

Module 4 : Data Import techniques

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

Module 5 : Exploratory Data analysis

    1. Understanding the Exploratory Data Analysis(EDA)
    2. Implementation of EDA on various datasets
    3. Boxplots
    4. Understanding the cor() in R
    5. list()
    6. Multiple packages in R for data analysis
    7. Segment plot HC plot in R

Module 6 : Data Visualization in R

    1. Understanding on Data Visualization
    2. Graphical functions present in R
    3. Plot various graphs like tableplot
    4. Histogram
    5. Box Plot
    6. Customizing Graphical Parameters to improvise the plots
    7. Understanding GUIs like Deducer and R Commander
    8. Introduction to Spatial Analysis

Module 7 : Data Mining: Clustering Techniques

    1. Introduction to Data Mining
    2. Understanding Machine Learning
    3. Supervised and Unsupervised Machine Learning Algorithms
    4. K-means Clustering

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

    1. Association Rule Mining
    2. Sentiment Analysis

Module 9 : Linear and Logistic Regression

    1. Linear Regression
    2. Logistic Regression

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