Tutorials
Hands-On Python
Hands-On Python
  • Hands-On Python Tutorial For Real-World Business Analytics Problems
  • Preface
    • Section I. A Note From The Author
    • Section II. Tutorial Overview
    • Section III. What Is The Preflight Checklist?
    • Section IV. Supplimentery Material
  • Preflight Checklist
    • Section V. Select Your Difficulty Setting
    • Section VI. Download Anaconda
    • Section VII. Download PyCharm (Optional)
    • Section VIII. Download SQL Server Developer Edition
    • Section IX. Configure Database Environment
    • Section X. Download The Source Code
    • Section XI. Starting JupyterLab
    • Section XII. How To Get Help With This Tutorial
  • Language Basics
    • Lesson 1. Obligatory Hello World
    • Lesson 2. Code Comments
    • Lesson 3. Data Types
    • Lesson 4. Variables
    • Lesson 5. String Concatenation
    • Lesson 6. Arithmetic Operators
    • Lesson 7. Making Decisions
    • Lesson 8. Control Flow With if-elif-else
    • Lesson 9. Control Flow With while
    • Lesson 10. Data Structures Part I: List
    • Lesson 11. Data Structures Part II: Tuples
    • Lesson 12. Data Structures Part III: Dictionaries
    • Lesson 13. Looping With for
    • Lesson 14. Functions
    • Lesson 15. Importing Modules
    • Lesson 16. Python Programming Standards
  • Advanced Topics
    • Lesson 17. Functional Programing With map
    • Lesson 18. Generators
    • Lesson 19. Comprehensions
    • Lesson 20. Basic File Operations
    • Lesson 21. Working With Data In Numpy
    • Lesson 22. Working With Data In Pandas
    • Lesson 23. Working With JSON
    • Lesson 24. Making File Request Over HTTP And SFTP
    • Lesson 25. Interacting With Databases
    • Lesson 26. Saving Objects With Pickle
    • Lesson 27. Error Handling
    • Lesson 28. Bringing It All Together
  • Solutions To Real World Problems
    • Lesson 29. Download A Zip File Over HTTP
    • Lesson 30. Looping Over Files In A Directory
    • Lesson 31. Convert Comma Delmited Files To Pipe Delimited
    • Lesson 32. Combining Multiple CSVs Into One File
    • Lesson 33. Load Large CSVs Into Data Warehouse Staging Tables
    • Lesson 34. Efficiently Write Large Database Query Results To Disk
    • Lesson 35. Working With SFTP In The Real World
    • Lesson 36. Executing Python From SQL Server Agent
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  1. Preface

Section I. A Note From The Author

PreviousPrefaceNextSection II. Tutorial Overview

Last updated 3 years ago

There are two main programming languages in the business analytics world: R and Python. People often ask which they should learn. People ask me that question so often that I actually wrote a blog post titled

The R versus Python argument might be as old as Betty White. Ok, well not that old. To be frank, it is not even about the age of the argument. It is old as in it is tiresome.

My career has always been about learning the most valuable skills in the marketplace. Back when dinosaurs roamed the earth (2014), employers were pretty agnostic about rather their data people knew R or Python. However, after a few years, I started to notice the market tipping towards Python. Then I noticed that employers started going hard in the paint on Python. Then came the last straw. I tell people this all the time. I was not losing work because I did not know R. I was definitely losing work because I did not know Python.

Whenever people argue about the virtues of R versus Python, they always make technical arguments about various features of the two languages.

I never care about the technical reasons for learning a language. I want to know what the business case is for learning a programming language. What’s my investment? What's my risk? What's my forecasted ROI? If I am going to spend months of my life learning a new language and years mastering it, I want to make sure I am not wasting my time. I want to know I can get a job at the end of the initial learning journey. From that perspective, there is no argument.

Since I have statistics in my background, I actually started using R first. When the market tilted all the way over towards Python, I put R down and have not touched it since.

Now, should you at some point learn R also? Maybe? For data science, probably. Python, for all its awesomeness is a general programing language with modules that let you do data science. Some of the data science modules are poorly implemented. I would not even know that if I did not follow data scientist with PhDs on Twitter. For some things R is superior because it is what is known as a domain specific language. It is written specifically to do statistics work.

My advice? Learn Python. Get a job. Learn R on your employer’s dime.

-Bob

Should I Learn R or Python?