Library

Course: Data Analytics using R Programming

Data Analytics using R Programming

  • Life Time Access
  • Certificate on Completion
  • Access on Android and iOS App
  • Self-Paced
About this Course

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
Basic knowledge
  • 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
What you will learn
  • 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
Curriculum
Number of Lectures: 82
Total Duration: 68:52:55
DATA ANALYTICS using R Programming
  • 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  
Reviews (0)