About This Site

ExploringDataScience.com is about sharing knowledge and information on the vast subject of Data Science. Sometimes it’s difficult to know where to start or how to find things – that’s where we come in.

 

Not everyone can become a great artist;

but a great artist can come from anywhere.

Anton Ego, Ratatouille

 

So it is with Data Scientists – not everyone can be a Data Scientist, but a Data Scientist can come from any background.

Data Science is a huge subject and learning it is a big undertaking. It requires the student to understand a vast number of interrelated subjects. Note: I didn’t say that they had to be mastered, they need to be understood, so a Data Scientist can know which is the best to apply to a particular situation. That is the secret of a Data Scientist, they are a know a lot about a very wide range of subjects and can apply them together in new and novel ways to data.

There’s a excellent infographic that gives an idea of the range of subjects required at Pragmatic Perspectives. I would really encourage you to have a look at the original article if your interested in becoming a Data Scientist. If you do have a look at MetroMap in the article, you’ll realise that I’ve used a lot of the “lines” that Swami Chandrasekaran came up as the basis for the categories on this site.

The point of all this is, as it says in the About Me page, I’m currently doing an MSc in Data Science. This site’s really here to help me learn about it. I’m going to publish pieces as I learn, and also collect together the good stuff I find on the web, so it’s all in one place. I suppose in that respect it’ll be partly be a blog and partly an aggregator, which’ll make me part-time blogger, part-time curator, and of course, a full-time Data Scientist.

I’m trying to arrange all the information in a way that will help other people, who are also trying to learn Data Science, get started.

Being a Data Scientist requires a lot of skills – indeed the modern Data Scientist is a polymath.

polymath (Greek: πολυμαθής, polymathēs, “having learned much”) is a person whose expertise spans a significant number of different subject areas; such a person is known to draw on complex bodies of knowledge to solve specific problems. The term was first used in the seventeenth century but the related term, polyhistor, is an ancient term with similar meaning. Source: Wikipedia

This can seem daunting and overwhelming, but the secret, as with most things, is just to take it one step at a time and remember that everybody had to start somewhere.

The Quotes

You’ll notice that most of the articles have a quote at the end – they aren’t necessarily related to the articles, but hopefully will raise a thought or a smile.

The four qualities of a great data scientist are creativity, tenacity, curiosity, and deep technical skills.

Jeremy Howard, Kaggle

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