Data Analytics using R Programming

Simpliv

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Online

£ 10 VAT inc.

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Description

  • Type

    Course

  • Methodology

    Online

  • Start date

    Different dates available

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

Facilities

Location

Start date

Online

Start date

Different dates availableEnrolment now open

About this course

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

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

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This centre's achievements

2021

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The average rating is higher than 3.7

More than 50 reviews in the last 12 months

This centre has featured on Emagister for 6 years

Subjects

  • Credit
  • Installation
  • Data analysis
  • Programming

Course programme

DATA ANALYTICS using R Programming 82 lectures 68:52:55 1. Introduction to Data Analytics and R Programming 2. R Installation & Setting R Environment 3. Variables, Operators & Data types 4. Structures 5. Vectors 6. Vector Manipulation & Sub Setting 7. Constants 8. RStudio Installation & Lists Part 1 9. Lists Part 2 10. List Manipulation, Sub Setting & Merging 11. List to Vector & Matrix Part 1 12. Matrix Part 2 13. Matrix Accessing 14. Matrix Manipulation, rep fn & Data Frame 16. Column Bind & Row Bind 15. Data Frame Accessing 17. Merging Data Frames Part 1 18. Merging Data Frames Part 2 19. Melting & Casting 20. Arrays 21. Factors 22. Functions & Control Flow Statements 23. Strings & String Manipulation with Base Package 24. String Manipulation with Stringi Package Part 1 25. String Manipulation with Stringi Package Part 2 & Date and Time Part 1 26. Date and Time Part 2 27. Data Extraction from CSV File 28. Data Extraction from EXCEL File 29. Data Extraction from CLIPBOARD, URL, XML & JSON Files 30. Database management systems 31. Structured Query Language 32. Data Definition Language Commands 33. Data Manipulation Language Commands 34. Sub Queries & Constraints 35. Aggregate Functions, Clauses & Views 36. Data Extraction from Databases Part 1 37. Data Extraction from Databases Part 2 & DPlyr Package Part 1 38. DPlyr Package Part 2 39. DPlyr Functions on Air Quality DataSet 40. Plyr Package for Data Analysis 41. Tidyr Package with Functions 42. Factor Analysis 43. Prob.Table & Cross Table 44. Statistical Observations Part 1 45. Statistical Observations Part 2 46. Statistical Analysis on Credit Data set 47. Data Visualization, Pie Charts, 3D Pie Charts & Bar Charts 48. Box Plots 49. Histograms & Line Graphs 50. Scatter Plots & Scatter plot Matrices 51. Low Level Plotting 52. Bar Plot & Density Plot 53. Combining Plots 54. Analysis with Scatter Plot, Box Plot, Histograms, Pie Charts & Basic Plot 55. MatPlot, ECDF & BoxPlot with IRIS Data set 56. Additional Box Plot Style Parameters 57. Set.Seed Function & Preparing Data for Plotting 58. QPlot, ViolinPlot, Statistical Methods & Correlation Analysis 59. ChiSquared Test, T Test, ANOVA, ANCOVA, Time Series Analysis & Survival Anal 60. Data Exploration and Visualization 61. Machine Learning, Types of ML with Algorithms 62. How Machine Solve Real Time Problems 63. K-Nearest Neighbor(KNN) Classification 64. KNN Classification with Cancer Data set Part 1 65. KNN Classification with Cancer Data set Part 2 66. Navie Bayes Classification 67. Navie Bayes Classification with SMS Spam Data set & Text Mining 68. WordCloud & Document Term Matrix 69. Train & Evaluate a Model using Navie Bayes 70. MarkDown using Knitr Package 71. Decision Trees 72. Decision Trees with Credit Data set Part 1 73. Decision Trees with Credit Data set Part 2 74. Support Vector Machine, Neural Networks & Random Forest 75. Regression & Linear Regression 76. Multiple Regression 77. Generalized Linear Regression, Non Linear Regression & Logistic Regression 78. Clustering 79. K-Means Clustering with SNS Data Analysis 80. Association Rules (Market Basket Analysis) 81. Market Basket Analysis using Association Rules with Groceries Data set 82. Python Libraries for Data Science DATA ANALYTICS using R Programming. 82 lectures 68:52:55 1. Introduction to Data Analytics and R Programming 2. R Installation & Setting R Environment 3. Variables, Operators & Data types 4. Structures 5. Vectors 6. Vector Manipulation & Sub Setting 7. Constants 8. RStudio Installation & Lists Part 1 9. Lists Part 2 10. List Manipulation, Sub Setting & Merging 11. List to Vector & Matrix Part 1 12. Matrix Part 2 13. Matrix Accessing 14. Matrix Manipulation, rep fn & Data Frame 16. Column Bind & Row Bind 15. Data Frame Accessing 17. Merging Data Frames Part 1 18. Merging Data Frames Part 2 19. Melting & Casting 20. Arrays 21. Factors 22. Functions & Control Flow Statements 23. Strings & String Manipulation with Base Package 24. String Manipulation with Stringi Package Part 1 25. String Manipulation with Stringi Package Part 2 & Date and Time Part 1 26. Date and Time Part 2 27. Data Extraction from CSV File 28. Data Extraction from EXCEL File 29. Data Extraction from CLIPBOARD, URL, XML & JSON Files 30. Database management systems 31. Structured Query Language 32. Data Definition Language Commands 33. Data Manipulation Language Commands 34. Sub Queries & Constraints 35. Aggregate Functions, Clauses & Views 36. Data Extraction from Databases Part 1 37. Data Extraction from Databases Part 2 & DPlyr Package Part 1 38. DPlyr Package Part 2 39. DPlyr Functions on Air Quality DataSet 40. Plyr Package for Data Analysis 41. Tidyr Package with Functions 42. Factor Analysis 43. Prob.Table & Cross Table 44. Statistical Observations Part 1 45. Statistical Observations Part 2 46. Statistical Analysis on Credit Data set 47. Data Visualization, Pie Charts, 3D Pie Charts & Bar Charts 48. Box Plots 49. Histograms & Line Graphs 50. Scatter Plots & Scatter plot Matrices 51. Low Level Plotting 52. Bar Plot & Density Plot 53. Combining Plots 54. Analysis with Scatter Plot, Box Plot, Histograms, Pie Charts & Basic Plot 55. MatPlot, ECDF & BoxPlot with IRIS Data set 56. Additional Box Plot Style Parameters 57. Set.Seed Function & Preparing Data for Plotting 58. QPlot, ViolinPlot, Statistical Methods & Correlation Analysis 59. ChiSquared Test, T Test, ANOVA, ANCOVA, Time Series Analysis & Survival Anal 60. Data Exploration and Visualization 61. Machine Learning, Types of ML with Algorithms 62. How Machine Solve Real Time Problems 63...

Additional information

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

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Data Analytics using R Programming

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