How to Learn Data Science: Python vs R

Imogen Crispe

Written By Imogen Crispe

Last updated on October 17, 2019

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When it comes to data science programming languages, you have two options: Python and R. Both Python and R will help you analyze data to see valuable business insights, but which one should beginners learn first to maximize their chances of landing a job? We asked Galvanize Lead Data Science Instructor, Sean Reed, to explain the differences between Python versus R, what each language does best, and which language he suggests data science students should learn first.

Meet the Expert: Sean Reed

  • Sean Reed is a Lead Data Science Instructor at Galvanize Data Science Bootcamp in New York
  • Sean’s background is in physics, economics, statistics, finance, and web development.
  • He learned data science techniques on the job, and worked as a data scientist for several years before joining Galvanize. 

What are Python and R?

  • Python is a more traditional programming language
  • R was originally designed for statistics but has branched out into a more general-purpose language

Python and R – the Similarities

Both Python and R are used for:

  • Loading data 
  • Cleaning data
  • Doing visualizations
  • Doing analysis
  • Graphing and predictions

The Case for Python

  • Python is a general purpose programming language 
  • It is used for software development as well as data science
  • Its flexibility allows Python to easily integrate into production systems
  • Many people use Python and know how to integrate Python
  • Developers can import R functions and objects into Python and R’s plotting routine can be used within Python

The Case for R

  • R was developed in academia for use in STEM subjects
  • It was originally used for building statistics packages and visualizations for researchers
  • There are a huge number of statistical packages available in R (more than in Python, though Python is catching up)
  • Now data scientists can combine both Python and R for a wider range of capabilities

Python and R Developers in the Real World

  • In the 1990s: Companies previously did not use R in production. Original R statistics packages were developed for individual researchers, not teams
  • Today: Software and data science has progressed to be team based
    • Data scientists share code on GitHub 
    • Data scientists push insights into production
    • Easier to collaborate in Python than in R, but those things are still being done in R by talented programmers.

What can Data Science Bootcamp Students build?

  • Galvanize students do final projects in Python
  • Galvanize students often combine Python with Flask, machine learning systems, and neural networks to create apps
    • One student created a Pet Recommender app. Users can type in dog attributes and find dogs that meet their preferences in local shelters!

Getting a Job in Data Science

Employers hiring data scientists look for:

  • People who are good programmers
  • People who can use the whole software engineering toolkit in Python
    • The Galvanize curriculum integrates the whole toolkit so that graduates are good programmers as well as good data scientists.
  • Galvanize data science graduates have found jobs as:
    • Data scientists
    • Business analysts
    • Data engineers
    • AI/Machine learning engineers

The Bottom Line: 

  • Focus on Python and object-oriented programming skills as a beginner. Understand how programming languages are structured. Learning Python brings you closer to the ecosystem of programming – data science techniques are moving towards software engineering.
  • After you have mastered Python, learn some R! 

Resources for learning Python

Galvanize Data Science Curriculum

The Galvanize curriculum covers:

  • Python
  • Linux
  • AWS infrastructure

Find out more and read Galvanize reviews on Course Report. Check out the Galvanize Data Science website.

About The Author

Imogen Crispe

Imogen Crispe

Imogen is a writer and content producer who loves exploring technology and education in her work. Her strong background in journalism, writing for newspapers and news websites, makes her a contributor with professionalism and integrity.

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