Data science is growing more popular every year. The reasons why are pretty obvious:
- There are 100,000 new data science jobs created every year.
- There are a wide range of career paths available - this article on top data science career paths gives a good overview of the main options available.
But to be successful, you need more reasons than the simple fact that there are plenty of well-paying jobs available. You need to know that data science is right for you. That you're going to be spending your time as a data scientist doing tasks that you're good at and working on projects you find intrinsically interesting. After all, data science is a discipline where you'll always be learning and developing.
So, we've put together a simple checklist of 7 ways to tell if data science is really for you:
- You love being a detective and you're always analysing.
- You're learning coding or already know how to code.
- You can simplify complicated ideas and love explaining things to people.
- You're curious about business and want to figure out how the best businesses work.
- You enjoy maths and statistics and have some solid skills in this area.
- You're keen to work with AI and Machine Learning tools.
- You're a big picture person, and like to understand the architecture, not just individual tasks.
Let's take a look at these in more detail so you can figure out if data science is really for you.
You love being a detective and you're always analysing
Data science is an analytical discipline. So it's perfect if you're a wannabe Sherlock Holmes who just can't relax until you've figured out what the problem is and how to solve it.
You'll need to be able to think deeply about the problem you're presented with and understand it from multiple perspectives. Only then will you know what data to extract and what to ignore.
One great quality for a data scientist to have is the ability to interrogate their own assumptions and approach each problem with a fresh perspective. Of course, experience counts for a lot. But in data science, using the models that worked last time might not provide you with the right answers this time round.
And finally, just like Sherlock does when he has all his suspects in one room, you'll have to explain what the results are and why you have them.
You're learning coding or already know how to code
A good chunk of your working time as a data scientist is going to be spent coding. You'll need to write scripts, automations and programs to manage the large volumes of data you work with. There'll be unstructured data, and sometimes you'll have to work with data in real time.
Python is becoming the language of choice in data science, so it's great if you know it. If not, you'll need to at least have some knowledge of how to code, and be ready to learn.
But most importantly of all, you'll need to enjoy coding. As a data scientist, you're going to be spending plenty of time with just your monitor for company. So being able to sit and code by yourself is important.
You can simplify complicated ideas and love explaining things to people
You're going to need some pretty sophisticated communication skills to make it as a data scientist.
That's because it's not enough to simply find the right data and present it. You'll be expected to come up with models, insights and interventions. And these require explanation. Top management are not going to agree to an intervention if they don't understand it. And the whole point of the models you create is to help decision-makers make better decisions. If they don't understand how it works, it's a waste of time.
Another important point to understand is that everything you work on will be integrated with other systems, data, applications and people. So communicating effectively with a range of people from a range of teams will be necessary.
So, can you turn statistics into an actionable insight, and explain it? Can describe how and why an algorithm made a particular prediction? And more importantly, is this something you enjoy doing?
That lightbulb moment when you audience "gets it" can be one of the most rewarding parts of working in data science. But you need the communication skills to make it possible.
You're curious about business and want to figure out how the best businesses work
What is all this data science for, anyway? Well, for most of us, our data superpowers are going to be put to use making businesses work better.
Understanding how business works is going to be an important part of how successful you are as a data scientist. Having an intuition for business is invaluable - having curiosity is a great start:
- Why did this model work for this business?
- Why was that prediction not what happened in reality?
- Will this data project really create value for this business?
The ability to focus the in-depth data work you do on fundamental business goals will give your work meaning and purpose. It will also make you very popular with your managers.
There is an aspect of communication that is important here too. Being able to actively listen to your stakeholders and hear what they really need is important in data science. This will enable you to do what they actually need, not what they think they need. It all comes down to having an attitude of curiosity as to how your business works and what makes it successful.
You enjoy maths and statistics and have some solid skills in this area
Along with coding, the other set of hard skills you'll be using all the time in data science is maths and statistics.
You'll be working with statistical models like regression, optimization, clustering, decision trees, and random forests. If this isn't enough of a work out for your maths muscles, you'll also have to use complex equations and create algorithms to manage the data.
You don't need a background in maths or statistics to be a data scientist, but it definitely helps. At the very least, you'll need to be curious about these subjects and ready to learn if it's not your forte.
You're keen to work with AI and Machine Learning tools
Did you know that humans produced 2.5 quintillion bytes of data every day? A quintillion is a billion billion (or a million trillion if you prefer). It has 18 zeros! That's a lot of data.
In fact, it's way too much for humans themselves to process and makes sense of. So, data scientists use Machine Learning and AI to do some of the heavy lifting when it comes to breaking this data down into something manageable.
If you're interested in AI and Machine Learning then you'll have plenty of opportunities to get up close and personal in this job. At the very least, you'll need to apply your statistical skills to understand the assumptions different AI and Machine Learning tools make. This way, you can fully understand the insights they provide. And in some cases you might be working on developing these tools yourself.
You're a big picture person, and like to understand the architecture, not just individual tasks
Just taking care of your individual tasks is not going to be enough in your data science work. You're going to need to understand the big picture too.
We've already discussed the importance of understanding the overall business goals of the company you work for. But there's also seeing the big picture in terms of data. This means understanding data architecture.
In order to generate accurate actionable insights, you'll need to know the lifecycle of the data you're working with:
- How was it created?
- How has it been recorded?
- How is it managed and stored?
Then you have to apply this knowledge to your modelling, and ultimately to the business decision that comes out of your data work. You'll also need to be on top of any changes in the data architecture and how this could impact your modelling.
So, an ability to see the big picture is important. As is a desire to scrutinise the journey your data's been on and what this might mean for your work.
Being a data analyst or a data scientist?
Working with data is not always the same. So, one final consideration is whether you want to be a data scientist rather than a data analyst.
Here's a quick breakdown of the main differences between these two roles.
- Questions: Data analysts answer questions. Data scientists do this too, but they also generate questions that need answering.
- Predictions: Data analysts typically work on cleaning and sorting data, or visualising it so it's easier to understand. In contrast, data scientists look for trends and make predictions.
- Perspective: Data analysts typically have a narrower focus. While data scientists definitely need to know the details, they also need to have the helicopter view too.
If you already have experience as a data analyst, transitioning to become a data scientist can be a great way to take your career forward. Doing this may require you to develop your communication skills further, and work on your ability to understand business goals as well as data.
Is data science for you?
Hopefully, our checklist of 7 points has helped you to understand whether data science is the right career path for you or not.
Don't forget, you don't need to be an expert in all of these areas. If you're an ace coder but have relatively little experience working in business, that's OK. Likewise, if you're great at communicating about data but your maths skills need some polishing, no problem. But if you're serious about making it as a data scientist, you need to be ready to develop the areas you aren't so strong in and be constantly learning.
That's because in the end, data science is best suit to people who have a range of abilities and perspectives:
- Thinking macro and micro: You'll need to understand the macro level (business priorities, data architecture), but also play close attention to the details (code, algorithms, equations).
- Soft skills and hard skills: You'll need to be a good communicator who can work well in teams (soft skills), but you'll also need to understand statistics, mathematics and coding (hard skills).
These contrasts are what make data science jobs well paid and sought after. They're also what make this such an interesting field to work.
If you like the look of data science, you might be interested in knowing more about:
- How to switch to a career in data science: this guide compares 4 different ways to switch to a career to an IT-based subject like data science.
How much it costs to become a data scientist: in which case, check out this explanation of how data science courses at Turing College are paid for (spoiler alert: you don't have to pay anything upfront).