R Programming for Data Science And Machine Learning | DataQrious Academy | Skillshare

# R Programming for Data Science And Machine Learning

#### DataQrious Academy, Make You Data Curious

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64 Lessons (10h 37m)
• 1. Introduction

1:29
• 2. Why Learn R

5:15
• 3. R Installation

7:15
• 4. Installing and Exploring RStudio

11:34
• 5. First R Program and Operators in R

11:06
• 6. Data Types in R

8:33
• 7. Creating Vectors in R

5:49
• 8. Sequence in R

14:59
• 9. Replicate Function

5:09
• 10. Accessing Vector Elements

8:04
• 11. Vector Manipulation in R

5:39
• 12. Vector Elements Recycling

5:22
• 13. Sorting Vector Elements

5:35
• 14. Decision Making in R

9:55
• 15. Loop Control using repeat and while loop

6:18
• 16. For loop and next statement

5:10
• 17. Functions in R

13:01
• 18. Matrices in R

13:04
• 19. Factors in R

7:53
• 20. Data Frames in R

16:14
• 21. Combining Data Frames

9:03
• 22. Recursion in R

7:24
• 23. Finding Factorial of a number using recursion in R

5:42
• 24. Program to check Prime Numbers

14:52
• 25. Program to check EVEN or ODD

5:09
• 26. Program to check Positive Negative or ZERO

3:32
• 27. Program to Check Leap Year or NOT

6:20
• 28. Program for Multiplication Table

3:07
• 29. Sample Data from a Population

9:41
• 30. Analysing Data in R from CSV file

18:44
• 31. Creating Pie chart in R

8:30
• 32. Analyzing Data sets using R functions

13:25
• 33. Analyzing Employee Data

13:30
• 34. Reading excel file in R

7:05
• 35. Reading xml file in R

13:45
• 36. Reading JSON file in R

9:30
• 37. Creating Bar plot

14:06
• 38. Stacked Bar Chart in R

5:33
• 39. Boxplot in R

9:04
• 40. Boxlot using mtcars dataset

10:37
• 41. Boxplot with notch

7:04
• 42. Histogram and distribution of Histogram

11:12
• 43. Drawing Histogram using hist function

12:53
• 44. Using breaks xlim ylim in histogram

14:19
• 45. Basic line chart for time series with ggplot2

19:56
• 46. Scatter Plot and plot matrices in R

16:24
• 47. Finding mean in R

19:00
• 48. Finding median and mode in R

18:33
• 49. What is Linear Regression

16:51
• 50. Prediction Using Linear Regression Model

15:11
• 51. Reading CSV, Predicting with LR model

11:11
• 52. Multiple Regression

10:18
• 53. Predicting Car Mileage using Multiple Regression in R

9:36
• 54. Logistic Regression

14:18
• 55. Normal Distribution

5:58
• 56. Normal Distribution using dnorm and pnorm function

8:08
• 57. Normal Distribution using qnorm and rnorm function

4:54
• 58. What are Missing Values and Types of Missing Values

12:40
• 59. Imputing Missing Values NAs in data set

7:18
• 60. Imputing Missing Values using PMM method

16:40
• 61. Data Manipulation Using dplyr package

18:43
• 62. Introduction to Shiny Interactive Dashboards in R

7:01
• 63. ShinyApp Creating Interactive Dashboard with Shiny

15:02
• 64. Conclusion and Project Work

2:54

R is one of the most popular and widely used tools for statistical programming.
It is a powerful, versatile, and easy to use tool for data analytics, and data visualization.
It is the first choice for thousands of data analysts working in both companies and academia.

This course will help you master R programming, as a first step to become a skilled R data scientist.

What you will learn from this class:

• Learn to program in R at a good level and how to use R Studio
• Learn the core principles of R programming
• Learn how to create vectors in R
• Learn how to create variables
• Learn Data types in R
• Decision Making in R
• Learn how to create a while() loop and a for() loop in R
• Learn how to build and use matrices in R
• Learn how to use Functions in R
• Learn the matrix() function, learn rbind() and cbind()
• Learn to use Factors in R
• Learn to use Data Frames in R
• Learn how to install packages in R
• Learn how to use charts and Graphs in R
• Learn to read data from CSV files
• Learn Data Analysis in R
• Learn how to use charts in R for data visualization

Who this class is for:

• Anyone who wants to master R
• Aspiring data scientists