Linkedin - Python vs. R for Data Science (2021)

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size492.5 MB
  • Uploaded Byfreecoursewb
  • Downloads58
  • Last checkedOct. 12th '21
  • Date uploadedOct. 09th '21
  • Seeders 6
  • Leechers4

Infohash : B692AE16FB942C8366B39360BDB1A6CFC5AE56F1

Python vs. R for Data Science (2021)



MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill Level: Beginner | Genre: eLearning | Language: English + srt | Duration: 39m | Size: 484.8 MB
Python and R are common programming languages used when working with data. Each language is powerful in its own way; however, it's important that you select the language that will best help you achieve your end result. In this course, data scientist and coding instructor Lavanya Vijayan helps you make this choice, sharing important considerations for using each language in various circumstances. Lavanya starts by going over the background of both languages, as well as the strengths and disadvantages of each in different scenarios. She then walks through the process of working on a data science project and how you'd handle the data at various stages using Python and R. Lavanya then covers how to analyze data using both languages. She rounds out the course by discussing the use cases that play to each language's strengths. By the end of this training, you’ll have the essential information you need to determine whether Python or R is right for you.

Files:

[ CourseLala.com ] Linkedin - Python vs. R for Data Science (2021)
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here !
    • Bonus Resources.txt (0.3 KB)
      • Ex_Files_Python_vs_R_Data_Science Exercise Files
      • 02_02_Data_exploration_Python.ipynb (13.2 KB)
      • 02_02_Data_exploration_R.Rmd (0.3 KB)
      • 02_03_Data_cleaning_and_manipulation_Python.ipynb (34.4 KB)
      • 02_03_Data_cleaning_and_manipulation_R.Rmd (0.4 KB)
      • 02_04_Data_visualization_Python.ipynb (41.8 KB)
      • 02_04_Data_visualization_R.Rmd (0.4 KB)
      • Resources.pdf (78.7 KB)
      • exam_grades.csv (0.4 KB)
      [1] Getting Started
      • [1] Python vs. R.mp4 (20.1 MB)
      • [1] Python vs. R.srt (2.1 KB)
      • [2] Important notes for Python and R.mp4 (8.2 MB)
      [2] 1. Python vs. R
      • [1] Working with programming languages.mp4 (26.7 MB)
      • [1] Working with programming languages.srt (3.8 KB)
      • [2] Using Python.mp4 (39.9 MB)
      • [2] Using Python.srt (5.8 KB)
      • [3] Using R.mp4 (32.3 MB)
      • [3] Using R.srt (4.8 KB)
      • [4] Comparing Python and R.mp4 (26.3 MB)
      • [4] Comparing Python and R.srt (4.0 KB)
      [3] 2. Key Differences in Handling Data
      • [1] Data loading in Python vs. R.mp4 (10.6 MB)
      • [1] Data loading in Python vs. R.srt (4.5 KB)
      • [2] Data exploration in Python vs. R.mp4 (51.0 MB)
      • [2] Data exploration in Python vs. R.srt (6.8 KB)
      • [3] Data cleaning and manipulation in Python vs. R.mp4 (81.4 MB)
      • [3] Data cleaning and manipulation in Python vs. R.srt (9.3 KB)
      • [4] Data visualization in Python vs. R.mp4 (59.0 MB)
      • [4] Data visualization in Python vs. R.srt (5.8 KB)
      [4] 3. Data Analysis in R and Python
      • [1] Data analysis in R.mp4 (33.5 MB)
      • [1] Data analysis in R.srt (5.3 KB)
      • [2] Data analysis in Python.mp4 (22.0 MB)
      • [2] Data analysis in Python.srt (3.3 KB)
      [5] 4. Applications
      • [1] Common data science applications with Python.mp4 (54.6 MB)
      • [1] Common data science applications with Python.srt (8.6 KB)
      • [2] Common data science applications with R.mp4 (12.3 MB)
      • [2] Common data science applications with R.srt (4.1 KB)
      [6] Moving Forward
      • [1] Unlocking data analysis in Python or R.mp4 (14.4 MB)
      • [1] Unlocking data analysis in Python or R.srt (2.2 KB)

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