Tutorialsplanet NET Udemy - R Programming for Statistics and Data Science 2020

1. 1. Introduction/1. Ten Things You Will Learn in This Course.mp448.91MB
2. 10. Exploratory data analysis/1. Population vs. sample.mp412.2MB
3. 10. Exploratory data analysis/2. Mean, median, mode.mp410.48MB
4. 10. Exploratory data analysis/3. Skewness.mp47.54MB
5. 10. Exploratory data analysis/5. Variance, standard deviation, and coefficient of variability.mp411.36MB
6. 10. Exploratory data analysis/6. Covariance and correlation.mp414.1MB
7. 11. Hypothesis Testing/1. Distributions.mp4106.95MB
8. 11. Hypothesis Testing/10. Comparing two means - Dependent samples.mp449.26MB
9. 11. Hypothesis Testing/12. Comparing two means - Independent samples.mp444.36MB
10. 11. Hypothesis Testing/2. Standard Error and Confidence Intervals.mp465.66MB
11. 11. Hypothesis Testing/3. Hypothesis testing.mp482.2MB
12. 11. Hypothesis Testing/4. Type I and Type II errors.mp441.66MB
13. 11. Hypothesis Testing/5. Test for the mean - population variance known.mp458.64MB
14. 11. Hypothesis Testing/7. The P-value.mp460.64MB
15. 11. Hypothesis Testing/8. Test for the mean - Population variance unknown.mp454.76MB
16. 12. Linear Regression Analysis/1. The linear regression model.mp457.54MB
17. 12. Linear Regression Analysis/2. Correlation vs regression.mp415.48MB
18. 12. Linear Regression Analysis/3. Geometrical representation.mp46.75MB
19. 12. Linear Regression Analysis/4. First regression in R.mp437.93MB
20. 12. Linear Regression Analysis/5. How to interpret the regression table.mp450.29MB
21. 12. Linear Regression Analysis/7. Decomposition of variability SST, SSR, SSE.mp449.19MB
22. 12. Linear Regression Analysis/8. R-squared.mp434.41MB
23. 2. Getting started/1. Intro.mp46.76MB
24. 2. Getting started/2. Downloading and installing R & RStudio.mp414.09MB
25. 2. Getting started/3. Quick guide to the RStudio user interface.mp414.89MB
26. 2. Getting started/5. Changing the appearance in RStudio.mp44.18MB
27. 2. Getting started/6. Installing packages in R and using the library.mp427.71MB
28. 3. The building blocks of R/1. Creating an object in R.mp443.97MB
29. 3. The building blocks of R/11. Functions and arguments.mp44.52MB
30. 3. The building blocks of R/13. Building a function in R (basics).mp424.6MB
31. 3. The building blocks of R/16. Using the script vs. using the console.mp49.33MB
32. 3. The building blocks of R/3. Data types in R - Integers and doubles.mp48.38MB
33. 3. The building blocks of R/4. Data types in R - Characters and logicals.mp46.35MB
34. 3. The building blocks of R/7. Coercion rules in R.mp45.45MB
35. 3. The building blocks of R/9. Functions in R.mp46.17MB
36. 4. Vectors and vector operations/1. Intro.mp412.24MB
37. 4. Vectors and vector operations/10. Slicing and indexing a vector in R.mp419.04MB
38. 4. Vectors and vector operations/13. Changing the dimensions of an object in R.mp48.93MB
39. 4. Vectors and vector operations/2. Introduction to vectors.mp46.92MB
40. 4. Vectors and vector operations/3. Vector recycling.mp45.12MB
41. 4. Vectors and vector operations/5. Naming a vector in R.mp49.11MB
42. 4. Vectors and vector operations/8. Getting help with R.mp424.77MB
43. 5. Matrices/1. Creating a matrix in R.mp411.66MB
44. 5. Matrices/11. Matrix operations in R.mp410.52MB
45. 5. Matrices/14. Categorical data.mp430.84MB
46. 5. Matrices/15. Creating a factor in R.mp420.78MB
47. 5. Matrices/18. Lists in R.mp450.23MB
48. 5. Matrices/2. Faster code creating a matrix in a single line of code.mp45.71MB
49. 5. Matrices/5. Do matrices recycle.mp43.38MB
50. 5. Matrices/6. Indexing an element from a matrix.mp415.19MB
51. 5. Matrices/7. Slicing a matrix in R.mp47.37MB
52. 5. Matrices/9. Matrix arithmetic.mp414.31MB
53. 6. Fundamentals of programming with R/1. Relational operators in R.mp47.55MB
54. 6. Fundamentals of programming with R/11. While loops in R.mp47.94MB
55. 6. Fundamentals of programming with R/13. Repeat loops in R.mp46.46MB
56. 6. Fundamentals of programming with R/15. Building a function in R 2.0.mp431.78MB
57. 6. Fundamentals of programming with R/16. Building a function in R 2.0 - Scoping.mp451.76MB
58. 6. Fundamentals of programming with R/2. Logical operators in R.mp45.08MB
59. 6. Fundamentals of programming with R/3. Vectors and logicals operators.mp43.93MB
60. 6. Fundamentals of programming with R/6. If, else, else if statements in R.mp49.67MB
61. 6. Fundamentals of programming with R/8. If, else, else if statements - Keep-In-Mind's.mp46.45MB
62. 6. Fundamentals of programming with R/9. For loops in R.mp411.55MB
63. 7. Data frames/1. Intro.mp46.63MB
64. 7. Data frames/10. Getting a sense of your data frame.mp48.92MB
65. 7. Data frames/11. Indexing and slicing a data frame in R.mp410.08MB
66. 7. Data frames/13. Extending a data frame in R.mp49.98MB
67. 7. Data frames/15. Dealing with missing data in R.mp410.69MB
68. 7. Data frames/2. Creating a data frame in R.mp418.98MB
69. 7. Data frames/4. The Tidyverse package.mp415.18MB
70. 7. Data frames/5. Data import in R.mp46.45MB
71. 7. Data frames/6. Importing a CSV in R.mp48.17MB
72. 7. Data frames/7. Data export in R.mp46.32MB
73. 8. Manipulating data/1. Intro.mp415.47MB
74. 8. Manipulating data/2. Data transformation with R - the Dplyr package - Part I.mp418.18MB
75. 8. Manipulating data/3. Data transformation with R - the Dplyr package - Part II.mp47.37MB
76. 8. Manipulating data/4. Sampling data with the Dplyr package.mp43.99MB
77. 8. Manipulating data/5. Using the pipe operator in R.mp47.33MB
78. 8. Manipulating data/8. Tidying data in R - gather() and separate().mp418.69MB
79. 8. Manipulating data/9. Tidying data in R - unite() and spread().mp46.01MB
80. 9. Visualizing data/1. Intro.mp46.67MB
81. 9. Visualizing data/11. Building a scatterplot with ggplot2.mp416.83MB
82. 9. Visualizing data/2. Intro to data visualization.mp47.39MB
83. 9. Visualizing data/3. Intro to ggplot2.mp424.34MB
84. 9. Visualizing data/4. Variables revisited.mp410.33MB
85. 9. Visualizing data/5. Building a histogram with ggplot2.mp422.58MB
86. 9. Visualizing data/7. Building a bar chart with ggplot2.mp412.2MB
87. 9. Visualizing data/9. Building a box and whiskers plot with ggplot2.mp420.27MB