So, you're looking to get into Data Science!

Well, firstly, congratulations for taking the plunge. Data Science is an extremely exciting and future-proof industry to be in. With ever-increasing volumes of data being generated and collected, companies from all industries are looking to hire Data Scientists to help them stay ahead of the competition.

It is widely known that Data Science was labelled "The Sexiest Job of the 21st Century" and that the salaries in the field are among some of the highest. Salary insights company Payscale calculated that the median salary for a Data Scientist in the US is $96k.

Great right? Well, not so fast. As a result, the market has been flooded with aspiring Data Scientists - but due to the increased competition, many are struggling to land any role, let alone their dream role.

So what is it that separates the candidate that does get the job, vs. the ones who unfortunately miss out?

And before you even get to thinking about landing a role - what should you focus on learning, and how do you go about it?

Do you need a degree, a Masters, or perhaps even a PhD?

Let me try and help you out.

I've spent over 13 years in Data Science at companies including Amazon & more recently Sony Playstation. During my time at these companies, I was fortunate to interview & screen hundreds of Data Science candidates, and through this process have learned what can differentiate a stand out & successful candidate from the rest.

Since February of this year I've been focused on building out an online Data Science programme called DATA SCIENCE INFINITY, and in preparation I've spoken to hundreds of aspiring Data Scientists. The questions I mentioned above are very common, so if you're wondering about the answers to any of them then you're not alone. There are so many things you're told you need to learn and it can become quite confusing and de-motivating.

Let me give you a really high-level overview of how you need to approach your learning...

To be honest, at a very high level you'll need to know some programming...and my recommendation would be to learn SQL and also Python. I say Python over R based on the fact that it's listed more commonly on Data Science job descriptions - but R is an amazing language as well!

You'll often hear about all the mathematics you need to know to become a Data Scientist, but don't be scared by this. Yes, you do need to know some maths, but you don't need to spend a year reading textbooks before you're allowed to progress.

Quite the opposite!

Try to start learning maths as you start applying things like Machine Learning algorithms. It's so much more enjoyable learning while you're testing and modifying things and seeing what changes and why...

You'll also want to learn about exploring and understanding data, and you'll get better at this through practice across different scenarios. Make sure you learn the Python library Pandas as this has some really great functionality that makes this all much easier!

Machine Learning is a big part of Data Science, so learning how to apply the commonly used algorithms is worthwhile...but don't think you need to learn them all! You can solve the vast majority of business problems with a small subset of algorithms, so focus on getting a deep understanding of a few, rather than a high level understanding of them all!

And, should you delve into Deep Learning?

Well, heck yes you should, Deep Learning is amazing - but boy is she a temptress!

Don’t skip the foundational skills first. I have seen this time and time again, and it doesn’t translate well into the real world job market! Get the core skills first and then take on Deep Learning!

Now, something important to always keep in mind, is that Data Science is not all about the technical skills. From my experience working with and interviewing Data Scientists, the thing that can differentiate a good Data Scientist from a great Data Scientist is their ability to understand the business problem first and then work back to a Data Science solution from there, not the other way around.

When learning, and working on projects - try to always keep an actual business application in mind, in other words, ask "why does this problem need to be solved and what might the impact be?"

No one is going to pay you just to be good at coding, or just to be good at maths, or just to know a lot of machine learning algorithms....but they will pay you, and they'll pay you well, to add tangible value to their business.

So should you focus your learning within a particular industry?

This is another common question, and no I don't think this is necessary. Often the same skills are used across different industries, it's just the data is slightly different. While it can be useful to focus on one particular area, I'd advise against going too narrow to start with.

The basis of programming, statistics, machine learning, and most importantly a focus on understanding and solving actual business problems is the same across the board.

If you are looking to pick one area, I'd purely pick the one that you're most interested in rather than basing it on anything like the job market. Almost everything in Data Science is in-demand...so if you're passionate about an area, you'll much more easily become an expert in that area and you'll succeed no matter what!

The last question that I get asked is...

How do I get a role without any work experience?

Well, often you will have to pull together a portfolio of projects to try and showcase your way of thinking - and to show a hiring manager that you could add value to their team and to the business.

To stand out from the crowd - when choosing projects, go for ones where you can formulate a reason for doing it.

Imagine that you're working as a Data Scientist in a business rather than simply working on a project in isolation. Rather than just "predict who will survive on the Titanic" (to use a classic example in the field) frame everything in terms of what this model or solution would actually do, why would you be building it? Come up with a narrative, maybe look at it as "a luxury shipping company wants me to help predict those at risk in the event of an incident".

I might seem a bit silly at first, but it's not.

It means that when speaking to potential employers about your projects, you will shine out above other candidates. The reason for this is that you're going further than just what you did and you're showing them that you're always thinking about it holistically, you're thinking about actually adding value to a business or to an end-user or customer.

Projects don't exist in isolation in the real world. Like I said earlier...no one is going to pay you just to be good at coding, or just to be good at maths, or just to know a lot of machine learning algorithms....but they will pay you, and they'll pay you well, to add tangible value to their business using those skills.

It's worth documenting your work on projects as you go, and even trying to formulate blog posts or portfolio entries using them. When you do this, look to frame the problem from inception (Why are you looking to solve this?) to the actual work (How did you go about it? Why did you choose technique C over techniques A & B?) and then the results or impact (What did you see, What does this mean in real terms?)

Finally, pick projects that excite you. Sure, you need to include some that cover different skill-sets, but if you're passionate or interested in a project you'll be far more motivated to dig deeper into the data, or test different solutions. That motivation will shine through!

So good luck with your journey, it's an exciting and rewarding one!

Remember, learning Data Science isn't something you can do in a fixed period of time, it's genuinely a lifelong learning journey - so keep at it, stay positive, and just have fun!

Andrew Jones is the creator & lead instructor at DATA SCIENCE INFINITY which helps you learn the skills that hiring managers want, shows you how to prepare for and succeed in interviews, and provides unlimited guidance and support on any part of your Data Science journey.

If you want to accelerate your Data Science learning journey, get ahead of the competition, and land an amazing role in this exciting field - then enrol now!