All in One Offer! | Access Unlimited Courses in any category starting at just $29. Signup today. Offer Ends in: 6 Days!

Browse Library

Get Unlimited Learning Access
$29
6 days left at this price!
30-Day Money-Back Guarantee

It Includes

  • Get Full Access to the platform
  • Access upto 16000+ online courses
  • Play & Pause Course Viewing
  • HD Recorded Lectures
  • Access on Mobile/PC/Tablet
  • Includes Real Projects
  • Online iLab Access
  • Certificate of Completion
  • Download for offline viewing
  • Cancel Anytime
$29
  • This course has been prepared for software professionals aspiring to learn Data Analytics using R Programming. Professionals who are into analytics in general may as well use this course to good effect.

Data Analysis with R Programming is a comprehensive course that provides a good insight into the latest and advanced features available in different formats.
 
It explains in detail how to perform various data analysis functions using R Programming.
 
The course has plenty of resources that explain how to use a particular feature, in a step-by-step manner.
 
The volume of data that one has to deal has exploded to unimaginable levels in the past decade, and at the same time, the price of data storage has systematically reduced.
 
Private companies and research institutions capture terabytes of data about their users’ interactions, business, social media, and also sensors from devices such as mobile phones and automobiles.
 
The challenge of this era is to make sense of this sea of data.This is where data analytics comes into picture.
 
Data Analytics largely involves collecting data from different sources, munge it in a way that it becomes available to be consumed by analysts and finally deliver data products useful to the organization business.
 
The process of converting large amounts of unstructured raw data, retrieved from different sources to a data product useful for organizations forms the core of Data Analytics.
 
In this online course, we will discuss the most advanced concepts and methods of Data Analytics.

  • Before you start proceeding with this course, we assume that you have prior exposure to handling huge volumes of unprocessed data at an organizational level. Through this course, we will develop a mini project to provide exposure to a real-world problem and how to solve it using Data Analytics. This course has been designed for all those readers who depend heavily on R Programming to prepare charts, tables, and professional reports that involve complex data. It will help all those readers who use R Programming regularly to analyze data.
  • Beginner Data Analyst developers curious about Data Analytics, Machine Learning and Data Science.
View More...
  • Section 1 : Introduction to Data Analytics and R Programming. 1 Lectures 00:20:05

    • Lecture 1 :
    • Introduction to Data Analytics and R Programming. Preview
  • Section 2 : R Installation & Setting R Environment. 1 Lectures 00:50:15

    • Lecture 1 :
    • R Installation & Setting R Environment.
  • Section 3 : Variables, Operators & Data types. 1 Lectures 00:53:10

    • Lecture 1 :
    • Variables, Operators & Data types.
  • Section 4 : Structures. 1 Lectures 00:47:08

    • Lecture 1 :
    • Structures.
  • Section 5 : Vectors. 1 Lectures 01:04:04

    • Lecture 1 :
    • Vectors.
  • Section 6 : Vector Manipulation & Sub Setting. 1 Lectures 01:06:03

    • Lecture 1 :
    • Vector Manipulation & Sub Setting.
  • Section 7 : Constants. 1 Lectures 00:41:37

    • Lecture 1 :
    • Constants.
  • Section 8 : RStudio Installation & Lists Part 1 1 Lectures 01:02:19

    • Lecture 1 :
    • RStudio Installation & Lists Part 1
  • Section 9 : Lists Part 2 1 Lectures 00:47:44

    • Lecture 1 :
    • Lists Part 2
  • Section 10 : List Manipulation, Sub Setting & Merging. 1 Lectures 00:45:01

    • Lecture 1 :
    • List Manipulation, Sub Setting & Merging.
  • Section 11 : List to Vector & Matrix Part 1 1 Lectures 00:49:52

    • Lecture 1 :
    • List to Vector & Matrix Part 1
  • Section 12 : Matrix Part 2 1 Lectures 00:44:02

    • Lecture 1 :
    • Matrix Part 2
  • Section 13 : Matrix Accessing. 1 Lectures 00:48:26

    • Lecture 1 :
    • Matrix Accessing.
  • Section 14 : Matrix Manipulation, rep fn & Data Frame. 1 Lectures 00:56:08

    • Lecture 1 :
    • Matrix Manipulation, rep fn & Data Frame.
  • Section 15 : Data Frame Accessing. 1 Lectures 00:54:01

    • Lecture 1 :
    • Data Frame Accessing.
  • Section 16 : Column Bind & Row Bind. 1 Lectures 00:50:32

    • Lecture 1 :
    • Column Bind & Row Bind.
  • Section 17 : Merging Data Frames Part 1 1 Lectures 00:50:04

    • Lecture 1 :
    • Merging Data Frames Part 1
  • Section 18 : Merging Data Frames Part 2 1 Lectures 00:54:25

    • Lecture 1 :
    • Merging Data Frames Part 2
  • Section 19 : Melting & Casting. 1 Lectures 00:52:55

    • Lecture 1 :
    • Melting & Casting.
  • Section 20 : Arrays. 1 Lectures 00:43:50

    • Lecture 1 :
    • Arrays.
  • Section 21 : Factors. 1 Lectures 00:50:53

    • Lecture 1 :
    • Factors.
  • Section 22 : Functions & Control Flow Statements. 1 Lectures 00:40:27

    • Lecture 1 :
    • Functions & Control Flow Statements.
  • Section 23 : Strings & String Manipulation with Base Package. 1 Lectures 00:53:22

    • Lecture 1 :
    • Strings & String Manipulation with Base Package.
  • Section 24 : String Manipulation with Stringi Package Part 1 1 Lectures 00:58:33

    • Lecture 1 :
    • String Manipulation with Stringi Package Part 1
  • Section 25 : String Manipulation with Stringi Package Part 2 & Date and Time Part 1 1 Lectures 00:48:13

    • Lecture 1 :
    • String Manipulation with Stringi Package Part 2 & Date and Time Part 1
  • Section 26 : Date and Time Part 2 1 Lectures 00:53:19

    • Lecture 1 :
    • Date and Time Part 2
  • Section 27 : Data Extraction from CSV File. 1 Lectures 00:42:02

    • Lecture 1 :
    • Data Extraction from CSV File.
  • Section 28 : Data Extraction from EXCEL File. 1 Lectures 00:50:40

    • Lecture 1 :
    • Data Extraction from EXCEL File.
  • Section 29 : Data Extraction from CLIPBOARD, URL, XML & JSON Files. 1 Lectures 00:50:04

    • Lecture 1 :
    • Data Extraction from CLIPBOARD, URL, XML & JSON Files.
  • Section 30 : Database management systems. 1 Lectures 00:50:22

    • Lecture 1 :
    • Database management systems.
  • Section 31 : Structured Query Language. 1 Lectures 00:41:35

    • Lecture 1 :
    • Structured Query Language.
  • Section 32 : Data Definition Language Commands. 1 Lectures 01:02:24

    • Lecture 1 :
    • Data Definition Language Commands.
  • Section 33 : Data Manipulation Language Commands. 1 Lectures 00:47:29

    • Lecture 1 :
    • Data Manipulation Language Commands.
  • Section 34 : Sub Queries & Constraints. 1 Lectures 00:16:07

    • Lecture 1 :
    • Sub Queries & Constraints.
  • Section 35 : Aggregate Functions, Clauses & Views. 1 Lectures 00:07:21

    • Lecture 1 :
    • Aggregate Functions, Clauses & Views.
  • Section 36 : Data Extraction from Databases Part 1 1 Lectures 00:52:31

    • Lecture 1 :
    • Data Extraction from Databases Part 1
  • Section 37 : Data Extraction from Databases Part 2 & DPlyr Package Part 1 1 Lectures 00:52:39

    • Lecture 1 :
    • Data Extraction from Databases Part 2 & DPlyr Package Part 1
  • Section 38 : DPlyr Package Part 2 1 Lectures 00:51:35

    • Lecture 1 :
    • DPlyr Package Part 2
  • Section 39 : DPlyr Functions on Air Quality DataSet. 1 Lectures 00:57:01

    • Lecture 1 :
    • DPlyr Functions on Air Quality DataSet.
  • Section 40 : Plyr Package for Data Analysis. 1 Lectures 00:46:51

    • Lecture 1 :
    • Plyr Package for Data Analysis.
  • Section 41 : Tidyr Package with Functions. 1 Lectures 00:50:48

    • Lecture 1 :
    • Tidyr Package with Functions.
  • Section 42 : Factor Analysis. 1 Lectures 00:57:11

    • Lecture 1 :
    • Factor Analysis.
  • Section 43 : Prob.Table & Cross Table. 1 Lectures 00:50:22

    • Lecture 1 :
    • Prob.Table & Cross Table.
  • Section 44 : Statistical Observations Part 1 1 Lectures 00:51:48

    • Lecture 1 :
    • Statistical Observations Part 1
  • Section 45 : Statistical Observations Part 2 1 Lectures 00:40:35

    • Lecture 1 :
    • Statistical Observations Part 2
  • Section 46 : Statistical Analysis on Credit Data set. 1 Lectures 01:00:29

    • Lecture 1 :
    • Statistical Analysis on Credit Data set.
  • Section 47 : Data Visualization, Pie Charts, 3D Pie Charts & Bar Charts. 1 Lectures 00:59:20

    • Lecture 1 :
    • Data Visualization, Pie Charts, 3D Pie Charts & Bar Charts.
  • Section 48 : Box Plots. 1 Lectures 00:54:38

    • Lecture 1 :
    • Box Plots.
  • Section 49 : Histograms & Line Graphs. 1 Lectures 00:45:26

    • Lecture 1 :
    • Histograms & Line Graphs.
  • Section 50 : Scatter Plots & Scatter plot Matrices. 1 Lectures 01:03:47

    • Lecture 1 :
    • Scatter Plots & Scatter plot Matrices.
  • Section 51 : Low Level Plotting. 1 Lectures 00:56:01

    • Lecture 1 :
    • Low Level Plotting.
  • Section 52 : Bar Plot & Density Plot. 1 Lectures 00:46:31

    • Lecture 1 :
    • Bar Plot & Density Plot.
  • Section 53 : Combining Plots. 1 Lectures 00:35:37

    • Lecture 1 :
    • Combining Plots.
  • Section 54 : Analysis with Scatter Plot, Box Plot, Histograms, Pie Charts & Basic Plot. 1 Lectures 00:51:07

    • Lecture 1 :
    • Analysis with Scatter Plot, Box Plot, Histograms, Pie Charts & Basic Plot.
  • Section 55 : MatPlot, ECDF & BoxPlot with IRIS Data set. 1 Lectures 01:02:55

    • Lecture 1 :
    • MatPlot, ECDF & BoxPlot with IRIS Data set.
  • Section 56 : Additional Box Plot Style Parameters. 1 Lectures 01:01:41

    • Lecture 1 :
    • Additional Box Plot Style Parameters.
  • Section 57 : Set.Seed Function & Preparing Data for Plotting. 1 Lectures 01:09:42

    • Lecture 1 :
    • Set.Seed Function & Preparing Data for Plotting.
  • Section 58 : QPlot, ViolinPlot, Statistical Methods & Correlation Analysis. 1 Lectures 00:59:26

    • Lecture 1 :
    • QPlot, ViolinPlot, Statistical Methods & Correlation Analysis.
  • Section 59 : ChiSquared Test, T Test, ANOVA, ANCOVA, Time Series Analysis & Survival Anal. 1 Lectures 00:54:42

    • Lecture 1 :
    • ChiSquared Test, T Test, ANOVA, ANCOVA, Time Series Analysis & Survival Anal.
  • Section 60 : Data Exploration and Visualization. 1 Lectures 00:51:00

    • Lecture 1 :
    • Data Exploration and Visualization.
  • Section 61 : Machine Learning, Types of ML with Algorithms. 1 Lectures 01:04:53

    • Lecture 1 :
    • Machine Learning, Types of ML with Algorithms.
  • Section 62 : How Machine Solve Real Time Problems. 1 Lectures 00:43:33

    • Lecture 1 :
    • How Machine Solve Real Time Problems.
  • Section 63 : K-Nearest Neighbor(KNN) Classification. 1 Lectures 00:43:33

    • Lecture 1 :
    • K-Nearest Neighbor(KNN) Classification.
  • Section 64 : KNN Classification with Cancer Data set Part 1 1 Lectures 01:03:15

    • Lecture 1 :
    • KNN Classification with Cancer Data set Part 1
  • Section 65 : KNN Classification with Cancer Data set Part 2 1 Lectures 00:43:12

    • Lecture 1 :
    • KNN Classification with Cancer Data set Part 2
  • Section 66 : Navie Bayes Classification. 1 Lectures 00:43:53

    • Lecture 1 :
    • Navie Bayes Classification.
  • Section 67 : Navie Bayes Classification with SMS Spam Data set & Text Mining. 1 Lectures 00:58:43

    • Lecture 1 :
    • Navie Bayes Classification with SMS Spam Data set & Text Mining.
  • Section 68 : WordCloud & Document Term Matrix. 1 Lectures 00:56:39

    • Lecture 1 :
    • WordCloud & Document Term Matrix.
  • Section 69 : Train & Evaluate a Model using Navie Bayes. 1 Lectures 01:11:40

    • Lecture 1 :
    • Train & Evaluate a Model using Navie Bayes.
  • Section 70 : MarkDown using Knitr Package. 1 Lectures 01:02:15

    • Lecture 1 :
    • MarkDown using Knitr Package.
  • Section 71 : Decision Trees. 1 Lectures 00:57:15

    • Lecture 1 :
    • Decision Trees.
  • Section 72 : Decision Trees with Credit Data set Part 1 1 Lectures 00:47:03

    • Lecture 1 :
    • Decision Trees with Credit Data set Part 1
  • Section 73 : Decision Trees with Credit Data set Part 2 1 Lectures 00:45:11

    • Lecture 1 :
    • Decision Trees with Credit Data set Part 2
  • Section 74 : Support Vector Machine, Neural Networks & Random Forest. 1 Lectures 00:46:50

    • Lecture 1 :
    • Support Vector Machine, Neural Networks & Random Forest.
  • Section 75 : Regression & Linear Regression. 1 Lectures 01:02:55

    • Lecture 1 :
    • Regression & Linear Regression.
  • Section 76 : Multiple Regression. 1 Lectures 00:48:24

    • Lecture 1 :
    • Multiple Regression.
  • Section 77 : Generalized Linear Regression, Non Linear Regression & Logistic Regression. 1 Lectures 00:35:37

    • Lecture 1 :
    • Generalized Linear Regression, Non Linear Regression & Logistic Regression.
  • Section 78 : Clustering. 1 Lectures 00:29:04

    • Lecture 1 :
    • Clustering.
  • Section 79 : K-Means Clustering with SNS Data Analysis. 1 Lectures 01:06:18

    • Lecture 1 :
    • K-Means Clustering with SNS Data Analysis.
  • Section 80 : Association Rules (Market Basket Analysis). 1 Lectures 00:39:33

    • Lecture 1 :
    • Association Rules (Market Basket Analysis).
  • Section 81 : Market Basket Analysis using Association Rules with Groceries Data set. 1 Lectures 00:56:19

    • Lecture 1 :
    • Market Basket Analysis using Association Rules with Groceries Data set.
  • Section 82 : Python Libraries for Data Science. 1 Lectures 00:22:31

    • Lecture 1 :
    • Python Libraries for Data Science.
  • How do i access the course after purchase?

    It's simple. When you sign up, you'll immediately have unlimited viewing of thousands of expert courses, paths to guide your learning, tools to measure your skills and hands-on resources like exercise files. There’s no limit on what you can learn and you can cancel at any time.
  • Are these video based online self-learning courses?

    Yes. All of the courses comes with online video based lectures created by certified instructors. Instructors have crafted these courses with a blend of high quality interactive videos, lectures, quizzes & real world projects to give you an indepth knowledge about the topic.
  • Can i play & pause the course as per my convenience?

    Yes absolutely & thats one of the advantage of self-paced courses. You can anytime pause or resume the course & come back & forth from one lecture to another lecture, play the videos mulitple times & so on.
  • How do i contact the instructor for any doubts or questions?

    Most of these courses have general questions & answers already covered within the course lectures. However, if you need any further help from the instructor, you can use the inbuilt Chat with Instructor option to send a message to an instructor & they will reply you within 24 hours. You can ask as many questions as you want.
  • Do i need a pc to access the course or can i do it on mobile & tablet as well?

    Brilliant question? Isn't it? You can access the courses on any device like PC, Mobile, Tablet & even on a smart tv. For mobile & a tablet you can download the Learnfly android or an iOS app. If mobile app is not available in your country, you can access the course directly by visting our website, its fully mobile friendly.
  • Do i get any certificate for the courses?

    Yes. Once you complete any course on our platform along with provided assessments by the instructor, you will be eligble to get certificate of course completion.
  • For how long can i access my course on the platform?

    You require an active subscription to access courses on our platform. If your subscription is active, you can access any course on our platform with no restrictions.
  • Is there any free trial?

    Currently, we do not offer any free trial.
  • Can i cancel anytime?

    Yes, you can cancel your subscription at any time. Your subscription will auto-renew until you cancel, but why would you want to?

395436 Course Views

7 Courses

Having 10+ Years of Experience in Software Industry which includes Development, Support & Training. My Experience Includes Managing, Processing, Predicting and Analyzing of Large volume of Business Data. Expertise in Data Management, BI Technologies & Data Science with Data Analytics, Machine Learning, Deep Learning & Artificial Intelligence using R Programming, Python Programming, WEKA and EXCEL. Having publications and patents in various fields such as machine learning, data security, and data science technologies. I received my Masters of Technology in Computer Science & Engineering from JNTU. Professionally, I am a Data Science management consultant with over 8 years of experience in finance, retail, transport and other industries.
View More...
  • machine-learning-from-scratch-using-python

    Machine Learning from Scratch using...

    By : Saheb Singh chaddha

    Lectures 14 Beginner Level 0:16:2
  • data-preprocessing-for-machine-learning-using-matlab

    Data Preprocessing for Machine Lear...

    By : Dr. Nouman Azam

    Lectures 30 Beginner Level 4:14:3
  • machine-learning-for-data-science-using-matlab

    Machine Learning for Data Science u...

    By : Dr. Nouman Azam

    Lectures 62 Beginner Level 9:12:36
  • machine-learning-with-r

    Machine Learning with R

    By : Bert Gollnick

    Lectures 124 Intermediate Level 13:1:56
  • road-map-to-artificial-intelligence-and-machine-learning

    Road Map to Artificial Intelligence...

    By : Vinoth Rathinam

    Lectures 13 Beginner Level 0:48:49
  • telecom-customer-churn-prediction-in-apache-spark-ml

    Telecom Customer Churn Prediction i...

    By : Bigdata Engineer Engineer

    Lectures 14 Beginner Level 1:43:19
Sign Up & Start Learning
By signing up, you agree to our Terms of Use and Privacy Policy
Create New Password
Enter your email address and we'll send you a link to reset your password.