Python vs R for Data Science with Galvanize Bootcamp Instructor Sean

[Music] my name is Sean Reid I am a data science instructor at galvanize my background Lexie was in physics so I did an undergrad in physics later the masters in economics and I got to data science because I've always had data around that meeting some science and so I figured I might as well be official about it so galvanize we teach Python that's part of our tool set it's a primary part of our tool set so we do Python and Linux and AWS kind of infrastructure but we definitely heard a question all the time should I learn Python or are at the beginning of a design I've done it are those are definitely the two most popular things people use in data science Python is a more traditional kind of programming language and always originally designed specifically for statistics but it's kind of branched out into a more general purpose language since then Python in terms of data science is used in similar ways that are in terms of like going through loading data in cleaning data kind of doing visualization doing analysis and and graphing and predictions Python is definitely a general-purpose language and so there are lots of pockets of developers who work in different parts of Python inside Veda science as well as outside and that makes python easier to integrate into production sorts of systems because there are already people who are using Python in those systems or developers that know how to integrate Python into those systems are really developed in academia and the usage was primarily for building stats packages and visualizing it for resurfaces so there is a huge amount of statistical packages available and are you know much more than this available in Python right now people in Python were sad that are had such nice tools and they wanted something for themselves so now you actually have the ability to actually import our functions and our objects into Python when they're using it so it's becoming less and less like Python versus R and much more like you know use whichever bits make sense and whoever has the best stuff it definitely used to be that companies did not use R in production so like and that was mostly true for the data science part of Python as well like the original packages or developed for like single researcher kind of working on their own on their own particular problems and doing their visualizations and things kind of malware software and data science has progressed that's all team based you have to be on good health we have to be here in code and you have to be able to kind of push your insights that you build with your data science models into production and so that's definitely easier to do in Python but in our and more people know how to do that in Python then empower but it's still being done in our because they're discrete programmers who work in every language our students do final projects and they'll typically do that completely in Python a lot of times students will actually build a website using flask and they'll kind of hook up their machine learning system directly to that for example students did a pet recommender system like you kind of would type in what types of followers you like it's particularly around dogs so what kind of qualities and dogs you're looking for and then it would try to find dogs in local shelters that kind of match those requirements in terms of trying to find a job now I think you have to be foremost in data science as well a good programmer like working in Python you really need to be able to kind of use that whole traditional software engineering computer science toolkit and I think that's the toolkit that people ultimately want actually so when we do at galvanize we try to integrate that toolkit into the course so people can make sure when they come out of grade they're also strong programmers overall so there are definitely a lot of different types of jobs that you can get coming out with a data science background I mean you have data scientists you also have sort of business analysts who come out you also have things more specialized like data engineering also people do particularly AI and or machine learning now depending on what their background was before they came here for beginner data scientists I would definitely say focus on Python in the beginning and I think you're gonna learn kind of good object-oriented skills and you'll learn about how programming language in the whole are structured and you'll be able to use kind of baker system around pandas and other tooling that that exists i think that learning Python first makes you closer leaves you closer to the ecosystem of programming because I think data science as a whole is moving much and more towards software engineering environment as a whole so I think learning Python is best for beginners so it galvanized a student has to come in and maybe have a Python assessment and the stats assessment an assessment it deals a little bit with sequel so you have to have studied one of my favorite websites I've seen is called snake Fi and I don't know who owns it but it just looks really fun and it has nice exercises and I think it gives you like if you go through their whole site it seems like they give you a good sort of grounding on on how Python works one other tool in terms of Python resources I like is is something called the whirlwind tour of Python and that's actually a free pdf and there's also github repos associated with it and it kind of gives you a really quick tour about what Python is and how it works and then it kind of leads you into the data science part towards the end galvanized also has a particular data science prep course if you go to galvanize calm and it's data - science - prep and you can kind of sign up for free and you have also some ability to get some intern prep in addition to the free stuff if you if you so choose so definitely my advice is you know learn Python first get really good at it and as you go along after the program you're always going to learn and definitely learn are definitely learn how to read are different learn how to read books with our and learn how to use some of the packages as well

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Id: 9cCLu8eLq6A

Length: 7min 16sec (436 seconds)

Published: Thu Jul 18 2019