Getting Started with Data Science: A Beginner’s Guide to Landing Your First Job
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Foraying into the world of data science is not an easy feat. While it’s dubbed as the “sexiest job of the 21st century,” data scientists would tell you that the journey is going to be a long haul. For one, there is no standardized or proven way that leads to becoming a data scientist, meaning that everyone has had to dip their toes in the water before they eventually plunge right into it.
The good news–it’s not impossible, and anyone can be a data analyst as long as they put their heart and mind into it.
Data science is simply defined as a field aimed at threshing knowledge and insight from many types of data. It requires skill and dedication to succeed in this line of work. If you think you have what it takes, here are a few tips to land a job in the field:
1. Develop coding/programming skills.
If you are someone with a degree in Information Technology or Computer Science, then you can say you have a good head start. If not, you might want to consider learning a programming language that is commonly used by many companies–Python. Once you get the hang of it, you’ll be able to swiftly learn other languages such as R and SQL.
There are actually an abundance of books and online courses you can rely on to aid the learning process. Topics range from data coding to business intelligence, so do some research on what can benefit you the most.
You can go to bootcamps, too! They are a popular way of learning data analysis for beginners. Sure, most would say that advanced learning only comes in when you do actual work, but bootcamps can provide you with a good introduction to the world of data science, especially if you have no prior experience. They can be a good starting point for you to launch your career.
2. Join forums and groups for data scientists and aspiring data scientists.
You will get to know the field more when you read real experiences from like-minded people–whether they are actual data scientists or aspiring ones. If you find the right online communities, they could also be one of your strongest bastions of support when you are starting out. Everyone could use some tips, tricks, and pieces of advice when launching their career for the first time, right? If you’re lucky, you might even find a job there! You could find acquaintances who could be your employers or colleagues in the future!
3. Cultivate your love for math.
As we already have mentioned earlier, anyone can get into data science. Whatever degree you possess–may it be in physics, marketing, economics, or English–nothing can stop you from reaching your goals. However, do remember that there’s still a good chance your endeavor wouldn’t pan out the way you want it to if you do not have the slightest interest in math. Yes, that means you’ll have to know your way around algebra, calculus, statistics, and more.
4. Get into machine learning.
What is machine learning? Perhaps you can say that it’s the core of data science. Machine learning is a type of Artificial Intelligence that allows computer programs to solve problems without human intervention. As a data scientist, you’ll be working with insurmountable amounts of data during a project and you’ll want to come up with accurate predictions in an instant to make things easier for you. That’s where machine learning comes to play. Without knowing how to employ at least a few machine learning techniques, you’ll find data science a difficult work to pursue.
5. Learn soft skills.
Contrary to popular belief, companies do not only need someone who’s well-versed in the technical aspects of data science; they also need one who can present findings in an intelligible manner. Don’t use words that might confuse CEOs or stakeholders because those might just fall on deaf ears, thereby leading to a misunderstanding. In short, make sure that you have stellar communication skills, as well as a rich background in business. Remember, being a data analyst is more about helping businesses solve problems through data only you can understand.
6. Do some work.
This might sound cliché–and a bit sappy at that–but no one has really gone far by staying within their comfort zone. This very much applies to every aspiring data scientist out there. You can start developing a script language that makes life more interesting to you, even if it’s not for business. For example, Tomi Mester, in his Medium article, revealed that he built a script that monitored a real-estate website, so he could receive offers before everyone else. Clever, isn’t it? It’s one way to really put your skills to test.
7. Send your application to startups to gain experience.
Let’s be real: Applying to multinational companies might be a long shot. Perhaps it might be safe to say that you’ll just waste your resources trying to get in if you have no large trove of experience, at least not yet. But most of the time, such companies are already looking for someone who has been in the industry for a number of years. With small companies, however, you will be able to set up the groundwork and just work your way up.
Keep in mind that you have to choose the one where you can improve your craft through training and guidance from senior data analysts. Also, find a company where you would feel motivated, where your excitement would be tickled, and where your heart truly pulls you.
When it comes to data science, there’s actually no limit to what you can achieve. The key is just this: Always to be ready and raring to take on the mantle of a data scientist. As we said, it will not be a short and easy journey. It will be worth it nonetheless. If you’re still on the fence about it, try giving it some time because doing it half-heartedly isn’t going to help you achieve what you are after.
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