So you want to work in Data Science…
It’s a great time to be involved in data. There’s more of it than ever before and companies realize how valuable effective data can be. Meanwhile traditional decision support systems matured into business intelligence solutions which themselves are maturing into data science. Throughout this, data has gotten closer and closer to the end user making everyone an analyst.
And why wouldn’t you want to work in this field? Salaries are skyrocketing. Jobs are plentiful and increasing. Media loves to write about “the sexiest job of the 21st century”. Most importantly, when it’s done well data science can make a real difference.
But what does that even mean?
Data science is big phrase and depending on who you talk to and where they work the definition will be different. Sometimes very different. There’s no industry standard for when something is data science and not programming or analysis. But there are a lot of infographics and a lot of Venn diagrams. Lots of Venn diagrams.
I believe you can have a great career in data and make a difference with your work without an advanced degree in statistics. Does that make you a data scientist? Probably not. Should that hold any of us back from getting better? Definitely not! This is what Amateur Data Science is all about!
Let’s ground ourselves
Even within all these different definitions there are consistencies. Things that if you can learn them and do them well you’ll be a better data scientist. I bucket them into four general groups.
- Data Management and Programming
- Statistics
- Communication and Visualization
- Business Acumen and Domain Expertise
1) Data Management and Programming: Without data, and the skills to process it, no analysis can happen. We’ll talk about how to acquire data, how to manipulate to get what you need and how to store it so it’s fast (and easy) to access later.
2) Statistics: This the heart of data science and also the most difficult to come at from an Amateur’s perspective. I believe the concepts can be accessible to anyone and you will learn what the statistics are doing without diving deep into the mathematics.
3) Communication and Visualization: Once you’ve built something valuable you need to be able to tell the people who need to know. That means breaking down complex topics into simple concepts. It also means being able to turn large amounts of data into images that immediately resonate with the viewer
4) Business Acumen and Domain Expertise: This is the hardest one to cover in a blog like this because domain expertise varies so much across fields. There are management concepts that transcend fields and we’ll learn about those.
Few people can claim expertise in all four of these areas so don’t let that intimidate you. In reality most people are skilled in two of these areas and can hold their own in a third.
Where Do We Go From Here?
This is Amateur Data Science and we’re going to approach it as ‘amateurs’ – meaning with a passion to understand it and an interest to get better at it. We’ll get our arms around the concepts related, directly and indirectly, to Data Science.
Perhaps you’re a SQL coder who wants to understand machine learning. Or maybe you’re a statistician who’s trying to learn more about database design to better prep your population. Or maybe you’re a business user who want to better understand the results you see from your data science colleagues.
Sometimes we’ll leave the science behind and look at the art of visualizations and graphic design. At other times we’re going to focus on platforms and technology. Nearly all this information is available somewhere on the internet, but I’ve yet to see it combined. You’ll find that here! We’re going to mix up articles about concepts, how-to explanations and real examples.
I don’t know all these topics yet either and that’s a big reason I’m writing this. I’ve spent 15 years working with databases and programming languages. I feel comfortable communicating and visualizing the data. I’ve got a good understanding of my industry and am curious about what makes it work. I’ve even built a predictive model.
Even with all that, I have a lot to learn myself and one of the greatest benefits of writing these posts for me will be the learning that we will do together.
Let’s Get Started
If all that sounds exciting, let’s jump into some Amateur Data Science!
Amateur Data Science 