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Data Analytics using R Programming

Statistical Data Analytics using R Programming Language

Description

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.

Who this course is for:

  • Beginner Data Analyst developers curious about Data Analytics, Machine Learning and Data Science
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Curriculum

  • Introduction to Data Analytics and R Programming
    20:05
    Preview
  • R Installation & Setting R Environment
    50:16
    Preview
  • Variables, Operators & Data types
    53:10
    Preview
  • Structures
    47:08
    Preview
  • Vectors
    01:04:04
  • Vector Manipulation & Sub Setting
    01:06:03
  • Constants
    41:38
  • RStudio Installation & Lists Part 1
    01:02:20
  • Lists Part 2
    47:44
  • List Manipulation, Sub Setting & Merging
    45:01
  • List to Vector & Matrix Part 1
    49:52
  • Matrix Part 2
    44:02
  • Matrix Accessing
    48:26
    Preview
  • Matrix Manipulation, rep fn & Data Frame
    56:08
  • Data Frame Accessing
    54:01
  • Column Bind & Row Bind
    50:32
  • Merging Data Frames Part 1
    50:04
  • Merging Data Frames Part 2
    54:26
  • Melting & Casting
    52:55
  • Arrays
    43:50
  • Factors
    50:53
  • Functions & Control Flow Statements
    40:27
  • Strings & String Manipulation with Base Package
    53:22
  • String Manipulation with Stringi Package Part 1
    58:33
  • String Manipulation with Stringi Package Part 2 & Date and Time Part 1
    48:13
  • Date and Time Part 2
    53:19
  • Data Extraction from CSV File
    42:02
  • Data Extraction from EXCEL File
    50:40
  • Data Extraction from CLIPBOARD, URL, XML & JSON Files
    50:04
  • Database management systems
    50:22
  • Structured Query Language
    41:35
  • Data Definition Language Commands
    01:02:24
  • Data Manipulation Language Commands
    47:29
  • Sub Queries & Constraints
    16:07
  • Aggregate Functions, Clauses & Views
    07:21
  • Data Extraction from Databases Part 1
    52:31
  • Data Extraction from Databases Part 2 & DPlyr Package Part 1
    52:39
  • DPlyr Package Part 2
    51:36
  • DPlyr Functions on Air Quality DataSet
    57:01
  • Plyr Package for Data Analysis
    46:51
  • Tidyr Package with Functions
    50:48
  • Factor Analysis
    57:11
  • Prob.Table & Cross Table
    50:22
  • Statistical Observations Part 1
    51:48
  • Statistical Observations Part 2
    40:35
  • Statistical Analysis on Credit Data set
    01:00:29
  • Data Visualization, Pie Charts, 3D Pie Charts & Bar Charts
    59:20
  • Box Plots
    54:38
  • Histograms & Line Graphs
    45:26
  • Scatter Plots & Scatter plot Matrices
    01:03:47
  • Low Level Plotting
    56:01
  • Bar Plot & Density Plot
    46:31
  • Combining Plots
    35:37
  • Analysis with Scatter Plot, Box Plot, Histograms, Pie Charts & Basic Plot
    51:07
  • MatPlot, ECDF & BoxPlot with IRIS Data set
    01:02:55
  • Additional Box Plot Style Parameters
    01:01:41
  • Set.Seed Function & Preparing Data for Plotting
    01:09:42
  • QPlot, ViolinPlot, Statistical Methods & Correlation Analysis
    59:26
  • ChiSquared Test, T Test, ANOVA, ANCOVA, Time Series Analysis & Survival Anal
    54:42
  • Data Exploration and Visualization
    51:00
  • Machine Learning, Types of ML with Algorithms
    01:04:53
  • How Machine Solve Real Time Problems
    43:33
  • K-Nearest Neighbor(KNN) Classification
    01:07:45
  • KNN Classification with Cancer Data set Part 1
    01:03:15
  • KNN Classification with Cancer Data set Part 2
    43:12
  • Navie Bayes Classification
    43:53
  • Navie Bayes Classification with SMS Spam Data set & Text Mining
    58:43
  • WordCloud & Document Term Matrix
    56:39
  • Train & Evaluate a Model using Navie Bayes
    01:11:40
  • MarkDown using Knitr Package
    01:02:15
  • Decision Trees
    57:16
  • Decision Trees with Credit Data set Part 1
    47:03
  • Decision Trees with Credit Data set Part 2
    45:11
  • Support Vector Machine, Neural Networks & Random Forest
    46:50
  • Regression & Linear Regression
    44:04
  • Multiple Regression
    48:24
  • Generalized Linear Regression, Non Linear Regression & Logistic Regression
    35:37
  • Clustering
    29:04
  • K-Means Clustering with SNS Data Analysis
    01:06:18
  • Association Rules (Market Basket Analysis)
    39:33
  • Market Basket Analysis using Association Rules with Groceries Data set
    56:19
  • Python Libraries for Data Science
    22:32
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Data Analytics using R Programming
This Course Includes
  • 68.5 hours
  • 82 Lectures
  • 82 Resources
  • Completion Certificate
  • Lifetime Access
  • 30-Days Money Back Guarantee

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