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How to become a Data Scientist

What exactly is data science, and why is it so popular these days?
How can you tell if studying to become one is the right career choice for you?
We’ve put together this data science roadmap to make life easier for you: find out what data science is, what data scientists do, and what you need to have – and learn – to become one.

What is Data Science?

What is Data Science?

The term data science has been causing quite a buzz over the last few years. Having earned the title of “sexiest job of the 21st century”, many people these days are considering a career in the field.

To start, let’s define what data science is. As the name implies, data science is a research field dedicated to collecting, analysing, and interpreting data. It works just like any other science, by using clearly defined research method to extract new learnings from various source materials.

What’s unique about it, is the nature of these source materials: big data. Thanks to ever-increasing technological developments, such as cloud computing and storage, companies all over the world can now collect massive amounts of data at affordable prices and with relative ease.

All these new riches offer real value to businesses. They can use them to better understand who their costumers are, what they’re interested in and what are the best ways to keep them coming back for more.

But they also pose a real challenge. How do you navigate massive amounts of data, from thousands or even millions of transactions? How do you determine what information is important, and how do you start making sense and learning something from pieces of scattered, seemingly random details?

Well, that’s where the science part comes in. Enter the data scientists – experts in extracting valuable, actionable artefacts of business information from the sands of company databases. Kind of like Indiana Jones, but with more programming skills and less life-threatening adventures (hopefully).

Working as a Data Scientist

What do Data Scientists do?

Data scientists are the mediators between the raw data collected and the various stakeholders who need to learn something from it. They act as translators, interpreting the business questions into digital commands that extract clear information from databases; and vice versa – presenting clear information from the mountains of data that they harvest.

Their tools are a combination of mathematical skills, statistical proficiency, and programming language proficiency. But to really excel, data scientists need some crucial soft skills: curiosity, the ability to ask questions, the patience to keep looking for new ways to interpret data, the willingness to constantly learn new skills and topics, and – maybe most importantly - good communication skills.

Because at the end of the day, for companies, government agencies or research institutions, data is a tool for making educated decisions. So, data scientists must work closely with different members of the organisation they work for and help them get the answers they’re looking for from the vast amount of data at their disposal.

The better a data scientist is at presenting their learnings in a clear and simple manner, the easier it is for stakeholders and managers to understand what to with those learnings. That’s why the ability to work well with others, and communicate clearly, is the secret ingredient to a successful data science career.

Data Scientist vs Data Analyst vs Data Engineer

If you’re somewhat familiar with data science, you might have heard about data analysis and engineering, as well. These are three different, distinct roles, all related to data collection and analysis, but each one dealing with different aspects of it.

Because data science is still a relatively young field, the distinction between all three is sometimes a little blurry. But to make things clearer, here’s a breakdown of the differences:

Data Analysts

The role where data scientists often start in, data analysts are responsible for the more basic aspects of harvesting and interpreting data. They usually work as part of bigger business units, like sales or marketing, and are given specific tasks or questions to focus on.

Data Scientists

Usually more experienced than data analysts, data scientists are tasked not only with processing data, but also with automating data harvesting tasks, and exploring new options for gaining insights from the database they work with. They tend to work in a more independent manner, and report directly to company leaders.

Data Engineers

Tend to come from more of a programming/computer science background. They’re the ones responsible for the infrastructure that lets data scientists and analysts work their magic, building and maintaining a solid foundation for complex databases.

The role you’ll end up working in depends on your background, passion, experience, and interests, as well as in the needs of your employer. Considering how dynamic the data research field is, and the various opportunities to constantly develop new skills within it, your role is likely to change and evolve as your career progresses.

That’s what’s so great about data science: the possibilities are almost endless.

Your career in Data Science roadmap

Decided that data science is your dream career? Fantastic. Now let’s cover how you can reach that goal, and the steps you need to take in order to become a successful data scientist.

How to start

What you need to know before you start

First off, you’ll need to consider your existing background. Have you studied for a degree before, or completed an online course? If so, was it in fields close to data science?

If the answer is yes, you can leverage that to your advantage, and spend less time on your professional training. Depending on your existing knowledge, you might only need to learn a new programming language or improve your statistics and math skills.

If the answer is no, and this will either be your first studies or a whole new field of expertise, then don’t be discouraged. Yes, you need to learn a whole new skill, but think of it this way: a whole new and exciting world is waiting for you to explore it!

Study Data Science online

The first step to becoming a data scientist is… (drumroll…)

 Learning data science.

Kidding aside, there’s no way around it – data science is its own unique discipline, with a specified set of tools, and regardless of your background or existing knowledge, if you come from outside the field, you will need to pick up a few new skills.

Due to its current popularity, there’s a wide range of educational options out there. Depending on your preference, you can take an online course, join a bootcamp or go the old-fashioned way and pick up some computer science books.

Another option, favoured by many, is to earn a recognised university degree in data science or a similar field, such as business intelligence. At IU, we offer both a Bachelor’s and a Master’s degree in data science, which you can study completely online.

Regardless of the educational path you choose, be prepared to work hard and commit yourself to sticking through it. Data science roles do pay well, but to get there, you’ll need to put in hard work, develop your expertise and build yourself up professionally. It’s not a walk in the park, but if you’re ready to put in the work – you will reach your goals.

Practice, practice, practice

You don’t have to wait for the end of your studies or training to start working on data science project. In order for you to really absorb all of these new terms and techniques, practical experience is key.

So start working on data science projects as early as you can, to see how things work outside of the classroom or virtual course. There are plenty of ways to do this – either through work exercises as part of your studies, free online projects, and online exercise boards.

By trying to apply what you learn from an early stage in your learning process, you can save valuable time – and avoid frustration – because it’ll help you find out what works best for you. Is studying on your own efficient for you? Or do you need more support and advice? Maybe you’d like to collaborate with other aspiring data scientists?

If you’ll wait for the end of your education process to start practicing what you’ve learned, you might realise that you’ve spent a lot of time working the wrong way for you. So do yourself a favour: overcome your insecurities, and practice, practice, practice!

Earn your certificate

Once you’re done with your studies, and completed your course or degree, you’ll have a certificate or diploma that you can add to your CV or LinkedIn profile and share with potential employers.

Though not necessarily indicative of your actual skills, having a certificate from a successfully completed course or degree shows employers that you have the basic knowledge and tools for working in the industry. It opens doors and makes it easier to find the right opportunity for an internship – or even a junior position.

That’s another advantage of studying data science in in a structured format: if you study independently, you’ll have to work harder in order to prove your skills to potential employers and create a portfolio of your work to make up for not having a recognised certificate.

Starting out: Networking and Internships

Learning data science – done.

Gaining experience by working on personal projects – check.

Earning a certificate – in the bag.

Now what? How do you go about finding your first job, and having the title “data scientist” attached to your resume?

In today’s job market, where competition is fierce but opportunities are available on a global scale, you need to be proactive when looking to build your career. Simply relying on sending out countless applications is not enough; you need to find a way to stand out from the crowd.

That’s where having a good professional network comes into the picture. Being connected to people who work in data science, or in similar roles in companies you’d like to work for, is a great asset to use. Don’t worry if you don’t know anyone in the field personally – you can start by finding data scientists in your area, reaching out to them, and building a relationship online.

Surround yourself with people who work in the industry, so when opportunities to apply for an internship or work on a project as a junior pop up, you’ll hear about them at an early stage – and have people in your network who can recommend you to potential employers.

And be ready for those opportunities: have a portfolio of projects that you can share and take on an internship or two. Build your experience out in the real world, form connections and get valuable feedback from mentors at an early stage in your career.

Invest in your skills, knowledge, and connections on your first steps, and reap the rewards later on.

Level up and advance: Career prospects

Powered through your internship and landed your first job? Well done. You are now a full-on data scientist (or analyst).

But the work doesn’t stop there. The data science field is constantly expanding and developing, and if you really want to move ahead in your career, you’ll need to develop as well.

Always stay curious to new ideas, technologies, and ways to improve. Never stop learning new techniques and methods. As you gain experience and build a better understanding of what aspects of the industry interest more, you can plan your next steps. Maybe you’d like to work more on infrastructure projects, and pivot to engineering role? Or maybe you’re more inclined to management responsibilities?

Whatever you choose, keep in mind that building a career a proactive, lifelong process. Don’t wait for opportunities to come your way: make your own chances. Invest in yourself, upskill, and make your skills future-proof.

Maybe even pursue a Master’s degree in data science, or, if you’re interested in moving to a managerial position, an MBA with a specialisation in Big Data can take your career to exciting new heights.

Why Data Science is a great choice

Now that you have your career in data science roadmap, and you know how to become a data scientist, let’s summarise and reinforce your decision to go down this exciting career path. You can go back to these points whenever you need extra motivation during your training process, so you never lose track of your goal.

  • Future-Proof

    The modern work world has seen some dramatic changes in recent years. The pandemic forced many of us to adapt, change the way we work and think about work, and explore new opportunities. To some, that meant the adoption of digital skills in order to keep up with a changing professional landscape. The need to plan for the future and develop a career that is as resilient to global crises as possible, as never been more urgent and clearer.

    This is why a career in data science is such a good choice. It’s not just because it’s the sexiest job of the 21st century – because, hey, what’s sexier than job security, right? – but also because it offers a path that can face major global turmoil with relative ease. It’s a job that can be done remotely, from anywhere in the world, so opportunities on a global scale are within your reach, and relocation becomes much easier.

    In addition, data science is constantly growing and is expected to keep on growing in the foreseeable future. Demand is high, and there is still a gap between the amount of qualified data scientists and open data science positions – meaning, there’s just not enough data scientists on the market right now. Not great for companies, but very good for you.

  • Salary

    This demand means that qualified, experienced data scientists can expect to be paid very well – salaries of six figures are common for senior and executive-level professionals.

    And because data is used across such a wide variety of industries and is so crucial for making the right managerial business decisions, expert data scientists can both choose to specialise in better paying industries (tech, automotive, etc.) and have a more direct path to managerial positions, thanks to their close relationships with decision makers. Use that to your advantage!

  • Learn something new every day

    Ok, so the money’s great, there’s plenty of jobs and great prospects for the future. But what about the actual work? The day-to-day?

    Well, there’s a lot to love about that as well. Data scientist is a fascinating role, that has real influence on how we live our lives: from algorithms that recommend what we should watch or buy, to medical information that can help combat pandemics. The possibilities are endless, and so is the data you’ll be working with – so you’ll need to be creative, innovative, methodical, and persistent in order to get the answers to the questions you or your team will have.

    It can be an exciting career choice, with huge amounts of professional satisfaction, because you’ll be focused with new challenges constantly, and you’ll need to muster all of your expertise to ace them. Once you do, you’ll be able to guide your colleagues through the maze of information and provide vital elements to the decision-making process, shaping how your company and products operate along the way.

     Data science is so great that even made statistics seem sexy. Statistics! What’s not to love?

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FAQ

Frequently Asked Questions About Becoming a Data Scientist