5 Tips for Starting Off in Data Science

Love data and wondering how you could get into a role in FinTech, Robotics or even Policing? Then a career in Data Science might be for you. With so many opportunities available, it's no wonder why it's becoming a buzzword amongst companies looking to hire tech talent.

Over the years, many of us have managed to master Excel and Tableau, but data technologies are progressing at break-neck speed, with Data Science being one of the most exciting emerging jobs in the tech sector, according to a report from LinkedIn. At hackajob, we regularly see roles in Data Science come up, and whilst many have heard of it, not all are aware of its purposes and processes.

So what exactly is Data Science?

Data science is interdisciplinary which means it makes use of various processes, programming, scientific methods, systems and even algorithms to extract knowledge and insights from many structural and unstructured data. This knowledge of interconnecting disciplines makes those who are in the field very attractive to

If you're an enthusiast looking to break into the world of Data Science, here are 5 of our top tips to help you start.

1. Love of learning

If you’re a curious person who loves to learn, Data Science may be a good fit for you. The exciting part is that no matter how many months or years you had put in studying data science, there will always be new algorithms, technologies and applications coming up. This means you have to be prepared to never stop learning. Not after 2 years, 5 or even 10 years in the field. We see this as a positive; if you like challenges, keeping up with trends and get excited about data then you'll likely enjoy the roles that use Data Science. And continuous learning means you'll always be at the top of your game.

A love of learning will also come in handy when it comes to extracting the data itself. Unlike in general software development, where you can start designing, implementing and testing as soon as you have a broad idea of what the requirements are, in a data science project you are hugely dependent on the availability of data. The data might be there, but the client might also have legal or technological obstacles in sharing it with you. Additionally, it might be low quality or full of ambiguity and inconsistency or there might even be no annotated data at all. This may mean you have to learn and prepare months in advance by gathering and annotating data, but this in itself ensures that when you eventually do get to the task, you know it inside and out.

2. Start with something particular

Going into data science – just like any new discipline – can be overwhelming, as numerous areas fall under the umbrella of 'data science’. The biggest question is always "Where should I start?" Our advice is to approach a specific subject that is of the greatest interest to you, or the greatest need on the market, and start with that – this will help you to stay motivated. For example, you may start with general statistics and machine learning to know the basics, and then focus on natural language processing, as there are currently many opportunities in the market for this type of role. For example, if you start with natural language processing projects, you can then gradually add skills in document extraction, developing chatbots and recommender systems.

3. Have a support group or mentor

You may already be working on a machine learning project, perhaps you're part of a team at a software company or you may have studied and researched Artificial Intelligence. Whichever walk of life you come from, be prepared for many people to question your decision. The truth is, learning data science can be hard, and there are many complicated mathematical and algorithmic concepts you will need to master, but if you have a support group or a mentor, it makes it so much easier to stay on track. The saying goes "a problem shared is a problem halved", so consider having at least one person you can ask a question when you feel stuck. You'll find that this helps you in the long run.

There are also plenty of ways to socialise with fellow data scientists. We recommend joining meet-ups (you can now join remote ones from all around the world), going to conferences, joining Facebook and LinkedIn groups, and being active on Twitter and Quora. Perhaps most importantly, we'd recommend finding at least one mentor - somebody that is already walking this path and can give you direction if you feel lost or you need a sounding board to talk through your ideas.

4. Every project counts

You may have worked on many projects or connected with data science before, and this can be to your advantage. We recommend that once you start doing educational data science projects, you treat every one of them as a way to help showcase your work to the world. And we say this because it will! Contrary to the work you do in a company or for direct clients, in most cases, educational projects are your intellectual property and your opportunity to shine. Putting effort into your projects such as ensuring that the code is clean and everything is well-documented means you can later show these projects to potential clients and employers and be one step ahead of other applicants. And, what is even more important is that new opportunities may come from people who saw your portfolio or GitHub account and found skills and expertise that align with their project’s needs. Trust us on this!

5. Be proactive and start as soon as possible

The good news is that you're reading this - so that's already a good start. Many people spend years in data science education, writing papers on the discipline and competing in Kaggle competitions, and yet they don't take their skills into a data science job. While having a solid general software engineering or other relevant experience can be a great plus, with years of waiting it is possible to lose the motivation to do the transition. So our advice is to start searching for jobs much before you are a qualified data scientist, maybe just a few educational projects might be enough to land your first job. It might be frustrating in the beginning, but you’ll be learning on the way, and in months you may be far ahead of a person that spent their time waiting.

We hope this article has been helpful. Data Science can seem like a tough nut to crack at first, but with these tips, you'll soon be on your way. If you're interested in working in a Data Science role, sign up to hackajob. It takes just 5 minutes, and companies reach out to you!

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