When I first started learning about Data Science, I thought the only way to do it was from a supercharged PC. Nowadays, there are discussions about how many GPU’s your machine has or even having terabytes of RAM for projects. Juxtaposed to this “Go big or go home” attitude, you can also work on projects from your phone. There are still limitations, but you are not chained to a desktop to do data mining or exploratory analysis. And now with cloud computing, you could schedule your model to run and let the distributed cloud handle all the work while you sip coffee on your back deck.
For me, I was fortunate to get an iPad Pro at the beginning of 2020, and I have used that as my laboratory/library while on the go. While I do have a more powerful iPad than a regular phone, a lot my recommendations can be done from older iPads or even iPhones (sorry, Apple guy here, but I am sure there are same/similar apps on Android devices).
*Also, for R users, Rstudio Cloud, but this is similar to Kaggle or GoogleColab
Pythonista is great if you are creating your own programs and functions. What’s interesting is that you can even run your programs from your apple watch! So if you wrote up a program to go and pull down data from a site or run a report, you can press a button and boom, it’s ran. There are limitations to the packages that are available, however, so you have to do some workarounds to get things to work like your regular PC.
Juno is the App Store’s answer to running Jupyter notebooks locally. This has all the same functionality of jupyter notebooks, you can install a wide array of packages, and you can even easily navigate your folder structures on your iPad like you would a regular PC (my experience has been painful trying to pull up a file for analysis in other iOS interpreters). The only downfall is that Juno will want to restart the interpreter if you are gone for too long (~5 minutes) or switch between apps, so I normally do side-by-side when working on projects.
Kaggle (and therefore, GoogleColab) has been a game-changer for data analysis. You can upload your own data, schedule workbooks to run, and even run your notebooks with GPU’s for those Deep Learning projects you’ve been working on. I love the functionality and the ease of using these platforms, even from an iPad or iPhone. They already have all the packages ready for you along with an active community more than willing to help you out if you get stuck. The only major pitfall is you need to have an online connection.
Code Repository/Version Control:
Even those small projects should have some kind of version control to them. You never know when you might break your code or delete a file by accident, and having a code repository and version control is just best practice. While I have two apps listed, I really just use Working Copy as this acts as my git client. Working Copy allows me to push/pull/merge code to GitHub as well as do version control locally. The GitHub app has been really helpful to discover new code repositories and projects that I may be inspired by.
- O’Reilly Books
- Data Science Newsletters (listed below) in GMail or Google Chrome
- Data Science Weekly
- Programmer Weekly
- Data Elixer
- Papers with Code
When I am trying to refresh myself on something (happens every day) or learn something new, I bring these apps side by side with my interpreter. I can work through an article, maybe even copy the code from one to the other and tweak to my heart’s content. I learn best when I can apply what I am learning, and these resources are not only extremely powerful in learning new tricks and concepts, but the side-by-side functionality of the iPad allows me to feel like I am coding on my regular machine.
There are certainly a lot of tricks you can do from your phone or iPad these days. I have listed only the most pertinent apps or sources that I use. However, you can stroll through the App Store or even find places like replit.com that allow you to all of the above and more. All you need is an internet connection and your creativity!
Maybe there’s a larger, philosophical statement to be made about Data Science in all this, but it’s amazing how far tech has come to allow us to explore, understand, and model data (and even deploy it!). Data Science has literally fallen into the laps of enthusiasts and professionals around the world. Access to a simple phone or tablet allows you to do so much more than what someone a decade ago could have dreamed of!
For those of you that use tablets or phones to learn Data Science, what do you use? Leave a comment below.