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种子名称： [FreeCourseLab.com] Udemy - R Programming Advanced Analytics In R For Data Science
文件类型： 视频
文件数目： 47个文件
文件大小： 1.24 GB
收录时间： 2019-10-4 13:09
已经下载： 3次
资源热度： 94
最近下载： 2024-6-27 15:28

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[FreeCourseLab.com] Udemy - R Programming Advanced Analytics In R For Data Science.torrent

1. Welcome To The Course/1. Welcome to the Advanced R Programming Course!.mp429.07MB

2. Data Preparation/1. Welcome to this section. This is what you will learn!.mp426.74MB

2. Data Preparation/10. What is an NA.mp413.99MB

2. Data Preparation/11. An Elegant Way To Locate Missing Data.mp448.42MB

2. Data Preparation/12. Data Filters which() for Non-Missing Data.mp430MB

2. Data Preparation/13. Data Filters is.na() for Missing Data.mp421.48MB

2. Data Preparation/14. Removing records with missing data.mp426.31MB

2. Data Preparation/15. Reseting the dataframe index.mp439.17MB

2. Data Preparation/16. Replacing Missing Data Factual Analysis Method.mp424.05MB

2. Data Preparation/17. Replacing Missing Data Median Imputation Method (Part 1).mp448.97MB

2. Data Preparation/18. Replacing Missing Data Median Imputation Method (Part 2).mp415.61MB

2. Data Preparation/19. Replacing Missing Data Median Imputation Method (Part 3).mp419.06MB

2. Data Preparation/2. Project Brief Financial Review.mp46.82MB

2. Data Preparation/20. Replacing Missing Data Deriving Values Method.mp418.45MB

2. Data Preparation/21. Visualizing results.mp431.87MB

2. Data Preparation/22. Section Recap.mp410.92MB

2. Data Preparation/3. Updates on Udemy Reviews.mp458.33MB

2. Data Preparation/4. Import Data into R.mp419.31MB

2. Data Preparation/5. What are Factors (Refresher).mp429.24MB

2. Data Preparation/6. The Factor Variable Trap.mp424.53MB

2. Data Preparation/7. FVT Example.mp422.53MB

2. Data Preparation/8. gsub() and sub().mp433.14MB

2. Data Preparation/9. Dealing with Missing Data.mp442.59MB

3. Lists in R/1. Welcome to this section. This is what you will learn!.mp417.77MB

3. Lists in R/10. Creating A Timeseries Plot.mp438.28MB

3. Lists in R/11. Section Recap.mp46.59MB

3. Lists in R/2. Project Brief Machine Utilization.mp453.14MB

3. Lists in R/3. Import Data Into R.mp415.41MB

3. Lists in R/4. Handling Date-Times in R.mp438.59MB

3. Lists in R/5. What is a List.mp435.97MB

3. Lists in R/6. Naming components of a list.mp411.67MB

3. Lists in R/7. Extracting components lists [] vs [[]] vs $.mp416.75MB

3. Lists in R/8. Adding and deleting components.mp432.55MB

3. Lists in R/9. Subsetting a list.mp424.26MB

4. Apply Family of Functions/1. Welcome to this section. This is what you will learn!.mp427.71MB

4. Apply Family of Functions/10. Using sapply().mp434.94MB

4. Apply Family of Functions/11. Nesting apply() functions.mp424.88MB

4. Apply Family of Functions/12. which.max() and which.min() (advanced topic).mp432.42MB

4. Apply Family of Functions/13. Section Recap.mp49.81MB

4. Apply Family of Functions/2. Project Brief Weather Patterns.mp425.31MB

4. Apply Family of Functions/3. Import Data into R.mp428.07MB

4. Apply Family of Functions/4. What is the Apply family.mp417.23MB

4. Apply Family of Functions/5. Using apply().mp425.69MB

4. Apply Family of Functions/6. Recreating the apply function with loops (advanced topic).mp419.76MB

4. Apply Family of Functions/7. Using lapply().mp438.72MB

4. Apply Family of Functions/8. Combining lapply() with [].mp424.81MB

4. Apply Family of Functions/9. Adding your own functions.mp428.02MB