As a consultant, I get parachuted into difficult problems every day. Often, I figure it out because I have to, and I want to. Usually, nobody else can do it other than me – they are all keeping the fires lit. I get to do the thorny problems that get left burning quietly. I love the challenge of these successes!
How do you get started? The online and offline courses, books, MOOCs, papers, blogs and the forums help, of course. I regularly use several resources for learning but my number one source of learning is:
Doing the ‘do’ – working on practical projects, professional or private
Nothing beats hands-on experience.
How do you get on the project ladder? Without experience, you can’t get started. So you end up in this difficult situation where you can’t get started, without experience.
Volunteer your time in the workplace – or out of it. It could be a professional project or your ‘data science citizen’ project that you care about. Your boss wants her data? Define the business need, and identify what she actually wants. If it helps, prototype to elicit the real need. Volunteer to try and get the data for her. Take a sample and just get started with descriptive statistics. Look at the simple things first.
Not sure of the business question? Try the AzureML Cheat Sheet for help.
Working with dat means that you will be challenged with real situations and you will read and learn more, because you have to do it in order to deliver.
In my latest AzureML course with Opsgility, I take this practical, business-centred approach for AzureML. I show you how to take data, difficult business questions and practical problems, and I show you how to create a successful outcome; even if that outcome is a failed model, it still makes you revise the fundamental business question. It’s a safe environment to get experience.
So, if this is you – what’s the sequence? There are a few sequences or frameworks to try:
- TDSP (Microsoft)
The ‘headline’ of each framework is given below, as a reference point, so you can see for yourself that they are very different. The main thing is to simply get started.
It’s important not to get too wrapped up on comparing models; this could be analysis paralysis, and that’s not going to help.
I’d suggest you start with the TDSP because of the fine resources, and take it from there.
I’d be interested in your approaches, so please do leave comments below.