(Complete Table of Contents here: http://aka.ms/backyarddatascience)
In the September 2015 issue of the Communications of the ACM magazine, there is an article on the Automated Education and the Professional – highly recommended reading. It feeds in nicely to our journey in learning Data Science.
The article covers the conflict between traditional college-degree education and the newer competency-based learning systems. I prefer both – a degree is extremely useful not just for landing a good job, but more importantly for a well-rounded education as a human being. As we think about learning Data Science, however, this question becomes quite important.
The traditional (albeit a brief one since the profession is so new) route of a professional Data Scientist is a degree in higher math and/or programming, with a focus on newer technologies. This is complemented with a lot of experience, and self-learning using various methods of the latest programming and processing languages, along with Machine Learning, data visualization, and more.
In my case, I’m learning as I go. I have a few college courses in statistics and higher math, but I need a lot more. Happily, there are a lot of ways to learn the information I need to be an amateur Data Scientist, at little or even no cost. But before I dive in to the details, I had to find out how what I know now, and then how I learn new things. If you’re following along with me, you’ll need to understand that as well.
Hubert Dreyfus, mentioned in the article above, finds that there are various “levels” of learned skill. He divides them up into the following:
- Advanced Beginner
Each of these levels determines what you need to know and how you learn. For instance, when you’re brand-new to a skill, you learn by following rules. When you master that skill, you rarely rely on the rules. So in the first case you’ll need to learn the rules, and later you’ll learn the theory behind them.
To apply this to how I’m learning Data Science, I find that I’m in different places with the various skills I need. For instance, I’m quite good with a Relational Database Management System, and a little less familiar with the NoSQL variants, and fairly new to Predictive Analytics and Machine Learning. Even within those areas I may have something I’m quite good at and something I’ve done less often.
So my first step will be to find out the skills and areas I need to know about Data Science, and then document what I do and don’t know about each of those areas. I’ll cover those areas in my next post – for now, I know this step is something I need to do.
Next, I need to understand just how I learn. You’ll need to do the same – you’ll get frustrated quickly if you try to learn in a way that doesn’t suit you. There are a few places you can go online to find out how you learn, and I’d recommend you do that. “Know Thyself”.
There are lots of ways to learn, and as it turns out, I learn different things in different ways. Here are a few:
- Visual – Seeing a thing explained graphically
- Reading – Reading about a thing
- Experiential – Doing something to learn it
- Audial – Hearing someone talk about a thing
- Examples – Seeing a completed thing, and reverse-engineering how it was accomplished
I find myself mostly gravitating towards the Example-based learning style, but in fact the route that works best for me is to combine as many learning styles as I can. So that’s what you’ll see as I use the What, Why, How tag in the Field Notebook.
Armed with the information above, I now have the ability to design a plan that covers what I know, and what I need to know. That’s what you’ll see me do here in the Field Notebook. Understand that your plan will look different, since you’ll know things I don’t, and you won’t know things I do. You’ll create your own plan as we move along.
Before you begin – take this course: https://www.coursera.org/learn/learning-how-to-learn/