Getting started with Galaxy for Data Scientists, Machine Learning Engineers, and Statisticians.
Last week, I introduced you to Galaxy, an open-source platform, ideal for every user who loves data especially biomedical data. You can check out the post here.
The platform has tutorials to guide you through as well as a lot of inbuilt tools, within the platform for your use.
As a Data Scientist, I was curious to understand how the Data Science Platform within Galaxy works and am happy to say, you will not be disappointed either. There are a lot of models present for you to use from classification models to regression, to hyperparameter tuning, to cross-validation, and even deep learning.
The below snippet shows the graphs you can comfortably create online, clearly spoilt for choice with that one.
For Data Science and Machine Learning, what impressed me the most is how versatile the tools are and some even quite complex. For example, there are a lot of tools for use in your Data Cleaning as well as Exploratory Data Analysis and model creation process. Here is a snippet of the machine learning tools available. The number of algorithms, again, spoilt for choice.
Where to start.
I hear you. It can be confusing if you come across the platform as a newbie. Well, do you remember how you started with ‘ hello world’ when it came to writing your first code? Or how you used the Iris dataset to learn a classification algorithm? That is how you start with Galaxy as well.
This tutorial on Iris Dataset Analysis helped me not only figure out how the tools work but also, most of my way around Galaxy, such as creating a history, creating and editing a workflow as well as replicating the workflow for another dataset.
It was amazing! I encourage you to start here before diving deeper into the Machine Learning tutorials as well as the Deep Learning ones too.
Soon, you will be analyzing your very own datasets all by yourself! Have fun!
