5 Career Tips for Aspiring Data Scientists

5 Career Tips for Aspiring Data Scientists

According to a report by IBM, the demand for data scientists will soar 28% by 2020.

Also, the number of Data Science and Analytics job listings is projected to grow by nearly 364,000 listings to approximately 2,720,000.

Since the Harvard Business Review gave the title of ‘the sexiest job of the 21st century’ to Data Scientist, the demand for Data scientist has risen tremendously, and it has become one of the most demanded, fast growing and highest paying jobs in the world.

Choose the right role

There are a wide variety of roles in the field of data science industry to choose from, such as, data visualization expert, machine learning expert, the statistician, business analyst, data scientist, data engineer, etc.

You have to choose the job that is best suited for you depending upon your background, experience or interests that you have in other fields of study.

However, it is important to bear in mind that a related job position might require you to perform some of the tasks related to a different focus area.If you feel uncertain about which area to choose, talk to the professionals working in the industry to figure out what each of the roles entails

You can also request for mentorship from professionals and request them for a small amount of time, ask relevant questions and clear all your queries and doubts. Figure out what you want and what you are good at and choose the role that suits your field of study.

After deciding on a role,

the next logical step is to put in an effort to understand that role and for this you should join an accreditation program.

Links

When you take up a course, commit to it fully. Follow the coursework, assignments, and  all the discussions happening in the course.

Choose a tool/language and stick to it

The data science industry if filled with a wide variety of programming languages aiming to sort out the business complexities and bring technological innovations.

New programming languages, frameworks and technologies that emerge every year and due to these ever increasing developments, it becomes important for developers to constantly be learning new skills to stay relevant.

However, if you’re an aspiring data engineer, choosing the right programming language and tools to add to your skill sets can be daunting.

If you’re interested in data analytics, modeling and visualization, then Python, R, and Java should be considered. Python is, undoubtedly, the most popularly used language used by data scientists across the globe.

According to a report by Stack Overflow developers, Python is the most preferred language by professionals for Machine learning applications. However, if you’re interested in managing databases, SQL should be your option. Large companies tend to use SQL throughout their operations.

Choose wisely but bear in mind that it wouldn’t hurt if you develop a working knowledge of the other languages from different areas of data science to open more opportunities for you.

Improve your communication skills

Due to the fact that data scientist is a technical gig;

many people tend to ignore that it’s not all about crunching data.

One of the most important responsibilities of a data scientist is communicating deep business or customer analytics to business people. Regardless of the fact that you’ve the best ideas or the most creative findings but if you cannot communicate your ideas to your colleagues, manager, investors, or executives then your value to an organization is limited. Presenting your findings to a non-technical audience, such as the marketing team or the C-Suite, is a difficult but important part of being a data scientist.

You must be able to interpret data, tell the stories it contain, and in general communicate, write and present well.

You are also required to explain effectively how you came to a specific conclusion, and be able to rationally justify your approach. Even in the field of data visualization, communication plays an important role. Along with the “illustrations” you’re also required to convey the essence of the story during a presentation or through annotations.

Keep learning by building projects

Instead of spending most of your time searching for jobs, you should also focusing on learning. Although it is undeniable that you should put time and effort in searching for a job, but you also need to set time aside to work on projects. In this field new tools constantly come out and the skills defined as “data science skills” constantly change and shift.

Therefore, by learning and exploring you will be able to stay on top of these skills and improve your desirability to any potential employers. Theory is important, but you must also focus on project work. This will also help you to practice what you’ll be creating in a data science job. And it will also improve your portfolio and build your confidence when attempting to score an interview. Nowadays, recruiters evaluate a candidate’s potential by his/her work and don’t put a lot of emphasis on certifications.

Network

Networking is the key. Attend industry events, conferences, popular meet ups in your area and participate in hackathons. It will not only help you in forming connections with professionals in your field and give you knowledge about what’s currently happening in your field but also provides you mentorship support and keep you motivated to overcome hurdles.

Most importantly, it also helps you to search for a job and you may get instantly employed by companies attending these events. These events also provide you the opportunity to build your personal brand.

QUORA Answers on Data Scientist:

What advice would you give an aspiring data scientist?