5 Ways to Land a Data Scientist Job Without Any Previous Experience

The amount of data generated daily is enormous. That’s why companies around the world are turning data into information and using it to optimize their strategies. But the challenge here is the fact that every business needs a professional with the relevant skills to extract information from the big data collected – a data scientist who now takes a seat at the big table.

Moreover, with the evolution of data and its increasing use in different types of businesses, people have started to see data science as super cool work. However, when it comes to becoming a data scientist, we notice that many professionals have dozens of MOOC courses and buzzwords on their CV or LinkedIn profile. And when a data science newbie sees these portfolios, it feels like data science isn’t their cup of tea. However, that’s not always the case – data science is about solving a real business problem, making the most of cluttered data. If you have the necessary knowledge, you can start your career in data science without any prior experience.

Here’s how:

Steps to follow

There are a lot of aspirants out there who want to be a part of the data science community, but they don’t know where to start, and there can be multiple reasons behind this – maybe they didn’t have a data science subject in their formal education, maybe they’ve never attended any data science conference, maybe there aren’t many faculties that are familiar with the field, etc. .

In this article, we’ll outline some of the important factors to keep in mind and prepare for a data science job without any prior experience.

1. Self-assessment

This is the first thing to do when you are starting your data science journey and have no previous experience. Ask yourself these questions: Why would a company hire you? If they don’t hire you, what could be the reason? What do you know about the field of data science? What else do you need to know about the domain? What additional skills do you need to learn to stand out from the crowd?

Plus, in addition to the skills and knowledge that data science professionals should have, learn about the latest industry trends – how the business works, what current positions are in demand, what are the latest programming languages, etc. Make a list of all the things you know, and you need to know and make a plan for how to go about it.

2. Skills you need to master

Mathematics: It is also considered to be one of the essentials in data science. This is very important in the field of data science because there are many concepts that help a data scientist with algorithms. In addition, concepts such as statistics and probability theory are essential for the implementation of algorithms. So, be sure to put a lot of effort into honing your math skills.

Programming: There are a lot of people who would suggest a huge bunch of programming languages ​​to learn if you want a career in data science. However, don’t overwhelm yourself with all the trending talk. When it comes to data science, Python and R are the two most important programming languages. Concentrate completely on these two languages ​​at the initial stage. Later, when you gain confidence with significant confidence, you can move on to the next one (Java could be one of them).

To learn how to program, you can always take short courses or online courses. Also, train a lot. The more you code, the better you become a coder.

Communication and visualization: Being on top of all technical aspects is one of them, but to be a successful data scientist you also need to have exceptional communication and communication skills. presentation skills. You shouldn’t just be a data scientist, but be a data storyteller too much. Why? Once you get the valuable insights from the cluttered data, your next job is to present it, and if you don’t have storytelling skills, how would you make others understand what that information is capable of and the value it is? they would bring.

3. Practice the problem statement in real time

Learning and mastering the skills is certainly mandatory, but to get the most out of your learning you need to practice – practice with real-time problem statements, you put value in your data science learning. The more you solve these problems, the more experience and confidence you gain and shorten the path to your dream scientific job. There is a lot of hackathons available on the internet – you can always pick one, participate, and see where you stand in this ever-competitive field of data science.

4.Connect with leaders

It is always considered a good practice to seek advice from someone who is already familiar with the field. And for that, you can make the most of platforms like LinkedIn to connect with some of the industry leaders.

Another better way to bond is to attend data science conferences, where you can not only attend lectures and masterclasses, but also meet many people in the industry who will help you get on the right path when you start your data science journey.

5. Accept reality

It’s no surprise that data science is currently one of the highest paying and reputable jobs in the industry. And no company would pay someone a handsome salary and give a high-level designation until they prove they are capable of dealing with some of the complex business issues. So accept the fact that when you start your career you might not even get the title of Data Scientist (you might get it in some exceptional cases). However, if you are determined and learn more and more about the field, the chances that you will land a higher position with a considerably high salary increase.

Don’t hesitate to ask for help from a fellow data scientist when you need it. Knowledge and skills are the keys to success.

Sean N. Ayres