7 Ways to Tell if Data Science Is the Right Fit for You

Data science is one of the most trending career options right now. The reasons why are pretty obvious:

• There are 100,000 new data science jobs created every year.
• The average entry-level salary for a data scientist in the US is $113,900. 
• There are a wide range of career paths available.

But to make a successful career switch, you need more reasons than the simple fact that there is a huge demand for data scientists. You need to know that data science is the right choice for you. The responsibilities of a data scientist should involve 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 a data science career is really for you:

1. You have a passion for problem-solving.
2. You're learning coding or already know how to code.
3. You can explain things in a simple way.
4. You're curious about how the best businesses work.
5. You have solid math and statistics skills.
6. You're keen to work with AI and machine learning.
7. You're a big-picture person and see beyond individual tasks.

Let's take a look at these in more detail so you can figure out if you can really be a successful data scientist.

You Have a Passion for Problem-Solving

Data science is a discipline that requires analytical thinking. So it's perfect if you're a wannabe Sherlock Holmes and can't relax until you've figured out the problem and how to solve it.

If you pursue a career in data science, 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 important quality data scientists need 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 the field of 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

Coding is the backbone of data science, and you'll spend a lot of your working time on that. You'll need to write scripts, automations, and programs to manage large volumes of various data types. There'll be unstructured data sets, and sometimes you'll have to manipulate data in real time. 

Python is becoming the programming language of choice in data science, so it's great if you know it. If not, look for a data science program for beginners that will teach you how to code from the ground up.

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 Explain Things in a Simple Way

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 explore data and present what you found. You'll be expected to come up with models, insights, and interventions based on data. And these require explanation. Data science may offer great insights, but top management will only accept them if they understand them. The whole point of the models you create is to help decision-makers make better decisions. If they end up rejecting your insights because they don't understand them, it's a waste of time.

Another important point 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 you describe how and why an algorithm made a particular prediction? Can you create a crystal-clear data visualization? And more importantly, is this something you enjoy doing? 

That lightbulb moment when your audience "gets it" can be one of the most rewarding parts of working as a data scientist. But you need the communication skills to make it possible.

You're Curious About How the Best Businesses Work

What is all this data science for, anyway? Well, in many cases, it's about using data to find meaningful insights for business. 

Understanding how business works is going to be an important part of your success as a data scientist. Having an intuition for business is invaluable, and 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. Aspiring data scientists must be able to actively listen to their stakeholders. If you can do that, you'll be able to produce the result they actually need — not the result 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 Have Solid Math and Statistics Skills

Along with programming languages, math and statistics are the other hard skills you'll use constantly in data science projects.

You'll work with statistical models like regression, optimization, clustering, decision trees, and random forests. If this isn't enough of a workout for your math muscles, data scientists also use complex equations and create algorithms to manage the data.

You don't need a background in math 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

Did you know that humans produce 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 big data in a literal sense.

In fact, it's way too much for humans themselves to process and make sense of. So, many data scientists use machine learning and AI to break massive amounts of data down into manageable chunks. 

If you're interested in AI and machine learning, 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 See Beyond 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.

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 modeling and, ultimately, to the business decision that comes from your data work. You'll also need to be on top of any changes in the data architecture and how this could impact your modeling.

So, an ability to see the big picture is important, as is a desire to scrutinize the journey your data has taken and what this might mean for your work.

Should You Be a Data Scientist or a Data Analyst?

Working with data is not always the same. So, one final consideration is whether you want to be a data scientist or become a data analyst instead.

Here's a quick breakdown of the main differences between data analytics and data science.

• Questions: Data analysts answer questions. Data scientists do this too, but they also generate questions that need answering.
• Predictions: Analysts typically work on cleaning and sorting raw data or visualizing it so it's easier to understand. In contrast, data scientists look for trends and make predictions.
• Perspective: Analysts typically have a narrower focus. While data scientists definitely need to know the details, they also need to have the helicopter view.

If you already have experience in data analytics, 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 a Career in Data Science for You?

Hopefully, our seven-point checklist has helped you determine whether data science is the right career path for you.

Remember, you don't need to be an expert in all of these areas. If you're an ace coder with relatively little business experience, 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 must be ready to develop the areas you aren't so strong in and constantly learn.

That's because, in the end, data science is best suited to people who have a range of abilities and perspectives:

• Thinking macro and micro: You'll need to both understand the macro level (business priorities, data architecture) and pay close attention to the details (code, algorithms, equations).
• Soft skills and hard skills: You'll need to both be a good communicator who can work well in teams (soft skills) and 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.

Become a Data Scientist with Turing College

Have you answered "yes" to most of the checklist points but don't have a coding or analytics background? No problem! Turing College has you covered with our newly redesigned Data Science program.

We've revamped our curriculum from the ground up to be accessible to driven learners like you, even if you're starting from scratch. Our program will guide you through the essential skills you need, from programming in Python to applying machine learning models. You'll work on real-world projects to solidify your knowledge and build an impressive data science portfolio. Best of all, you'll receive continuous guidance from professionals who are a part of the data science community and can help you get an even better idea of what it's like to be a data scientist.