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种子名称： [Tutorialsplanet.NET] Udemy - R Programming for Statistics and Data Science 2020
文件类型： 视频
文件数目： 87个文件
文件大小： 1.75 GB
收录时间： 2021-9-4 12:58
已经下载： 3次
资源热度： 159
最近下载： 2024-5-15 16:14

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[Tutorialsplanet.NET] Udemy - R Programming for Statistics and Data Science 2020.torrent

1. Introduction/1. Ten Things You Will Learn in This Course.mp448.91MB

10. Exploratory data analysis/1. Population vs. sample.mp412.2MB

10. Exploratory data analysis/2. Mean, median, mode.mp410.48MB

10. Exploratory data analysis/3. Skewness.mp47.54MB

10. Exploratory data analysis/5. Variance, standard deviation, and coefficient of variability.mp411.36MB

10. Exploratory data analysis/6. Covariance and correlation.mp414.1MB

11. Hypothesis Testing/1. Distributions.mp4106.95MB

11. Hypothesis Testing/10. Comparing two means - Dependent samples.mp449.26MB

11. Hypothesis Testing/12. Comparing two means - Independent samples.mp444.36MB

11. Hypothesis Testing/2. Standard Error and Confidence Intervals.mp465.66MB

11. Hypothesis Testing/3. Hypothesis testing.mp482.2MB

11. Hypothesis Testing/4. Type I and Type II errors.mp441.66MB

11. Hypothesis Testing/5. Test for the mean - population variance known.mp458.64MB

11. Hypothesis Testing/7. The P-value.mp460.64MB

11. Hypothesis Testing/8. Test for the mean - Population variance unknown.mp454.76MB

12. Linear Regression Analysis/1. The linear regression model.mp457.54MB

12. Linear Regression Analysis/2. Correlation vs regression.mp415.48MB

12. Linear Regression Analysis/3. Geometrical representation.mp46.75MB

12. Linear Regression Analysis/4. First regression in R.mp437.93MB

12. Linear Regression Analysis/5. How to interpret the regression table.mp450.29MB

12. Linear Regression Analysis/7. Decomposition of variability SST, SSR, SSE.mp449.19MB

12. Linear Regression Analysis/8. R-squared.mp434.41MB

2. Getting started/1. Intro.mp46.76MB

2. Getting started/2. Downloading and installing R & RStudio.mp414.09MB

2. Getting started/3. Quick guide to the RStudio user interface.mp414.89MB

2. Getting started/5. Changing the appearance in RStudio.mp44.18MB

2. Getting started/6. Installing packages in R and using the library.mp427.71MB

3. The building blocks of R/1. Creating an object in R.mp443.97MB

3. The building blocks of R/11. Functions and arguments.mp44.52MB

3. The building blocks of R/13. Building a function in R (basics).mp424.6MB

3. The building blocks of R/16. Using the script vs. using the console.mp49.33MB

3. The building blocks of R/3. Data types in R - Integers and doubles.mp48.38MB

3. The building blocks of R/4. Data types in R - Characters and logicals.mp46.35MB

3. The building blocks of R/7. Coercion rules in R.mp45.45MB

3. The building blocks of R/9. Functions in R.mp46.17MB

4. Vectors and vector operations/1. Intro.mp412.24MB

4. Vectors and vector operations/10. Slicing and indexing a vector in R.mp419.04MB

4. Vectors and vector operations/13. Changing the dimensions of an object in R.mp48.93MB

4. Vectors and vector operations/2. Introduction to vectors.mp46.92MB

4. Vectors and vector operations/3. Vector recycling.mp45.12MB

4. Vectors and vector operations/5. Naming a vector in R.mp49.11MB

4. Vectors and vector operations/8. Getting help with R.mp424.77MB

5. Matrices/1. Creating a matrix in R.mp411.66MB

5. Matrices/11. Matrix operations in R.mp410.52MB

5. Matrices/14. Categorical data.mp430.84MB

5. Matrices/15. Creating a factor in R.mp420.78MB

5. Matrices/18. Lists in R.mp450.23MB

5. Matrices/2. Faster code creating a matrix in a single line of code.mp45.71MB

5. Matrices/5. Do matrices recycle.mp43.38MB

5. Matrices/6. Indexing an element from a matrix.mp415.19MB

5. Matrices/7. Slicing a matrix in R.mp47.37MB

5. Matrices/9. Matrix arithmetic.mp414.31MB

6. Fundamentals of programming with R/1. Relational operators in R.mp47.55MB

6. Fundamentals of programming with R/11. While loops in R.mp47.94MB

6. Fundamentals of programming with R/13. Repeat loops in R.mp46.46MB

6. Fundamentals of programming with R/15. Building a function in R 2.0.mp431.78MB

6. Fundamentals of programming with R/16. Building a function in R 2.0 - Scoping.mp451.76MB

6. Fundamentals of programming with R/2. Logical operators in R.mp45.08MB

6. Fundamentals of programming with R/3. Vectors and logicals operators.mp43.93MB

6. Fundamentals of programming with R/6. If, else, else if statements in R.mp49.67MB

6. Fundamentals of programming with R/8. If, else, else if statements - Keep-In-Mind's.mp46.45MB

6. Fundamentals of programming with R/9. For loops in R.mp411.55MB

7. Data frames/1. Intro.mp46.63MB

7. Data frames/10. Getting a sense of your data frame.mp48.92MB

7. Data frames/11. Indexing and slicing a data frame in R.mp410.08MB

7. Data frames/13. Extending a data frame in R.mp49.98MB

7. Data frames/15. Dealing with missing data in R.mp410.69MB

7. Data frames/2. Creating a data frame in R.mp418.98MB

7. Data frames/4. The Tidyverse package.mp415.18MB

7. Data frames/5. Data import in R.mp46.45MB

7. Data frames/6. Importing a CSV in R.mp48.17MB

7. Data frames/7. Data export in R.mp46.32MB

8. Manipulating data/1. Intro.mp415.47MB

8. Manipulating data/2. Data transformation with R - the Dplyr package - Part I.mp418.18MB

8. Manipulating data/3. Data transformation with R - the Dplyr package - Part II.mp47.37MB

8. Manipulating data/4. Sampling data with the Dplyr package.mp43.99MB

8. Manipulating data/5. Using the pipe operator in R.mp47.33MB

8. Manipulating data/8. Tidying data in R - gather() and separate().mp418.69MB

8. Manipulating data/9. Tidying data in R - unite() and spread().mp46.01MB

9. Visualizing data/1. Intro.mp46.67MB

9. Visualizing data/11. Building a scatterplot with ggplot2.mp416.83MB

9. Visualizing data/2. Intro to data visualization.mp47.39MB

9. Visualizing data/3. Intro to ggplot2.mp424.34MB

9. Visualizing data/4. Variables revisited.mp410.33MB

9. Visualizing data/5. Building a histogram with ggplot2.mp422.58MB

9. Visualizing data/7. Building a bar chart with ggplot2.mp412.2MB

9. Visualizing data/9. Building a box and whiskers plot with ggplot2.mp420.27MB