Making a career in data science for freshers is very much like ticking the right boxes

Data Science is one of the happening areas in the job market with immense potential to absorb a large chunk of talented and trained freshers. The US Bureau of Labor Statistics puts the job growth in data science at 28% by 2026, which roughly equals 11.5 million new jobs. And therefore, pursuing a career in data science shouldn't be a matter of concern for this very reason. Or do you think so?  Indeed, data science jobs and the ones in adjunct technologies like artificial intelligence and machine learning are in quite a demand but here is the caveat. Data Science has almost become a discipline and numerous courses from well-reputed institutes are out of reach for many students. IITs, IIMs and Central Universities are rapidly introducing well-structured courses resulting in enormous competition in the market for a fresher to launch a career in data science. Does it mean there is no scope for a Bachelor's degree holder or a diploma holder to make a career in data science as a fresher? A McKinsey report published in 2011 predicts that the US alone will have a shortage of about 190,000 data scientists and 1.5 million managers. In order to grab this opportunity, there is nothing better a formula than to stack up the skill set.

Are Data Scientists Number Crunchers?

Well, yes or maybe no. The day-to-day duties of Data Scientists involve analyzing huge chunks of data and looking for insights that guide businesses in improving their key metrics. The data analytics process need not always necessarily involve only looking at numbers. Finding patterns and recognizing outliers to make a unique observation constitutes the major part of a data scientist's functionalities. They work with key stakeholders of businesses to find solutions to their present and futuristic business problems. In other words, a business challenge becomes a data scientist's problem statement. Data Science jobs fall in a typical spectrum with a wide range of jobs including junior data scientist, data engineer, data analyst, database administrator, machine learning engineer, data architect, business analyst, data and analytics manager, etc. Though most of the fresh data science graduates start off as junior data scientists, whose responsibilities involve data processing, and building data models and algorithms, with time they develop advanced skills in building analytic systems and predictive models leveraging natural language processing, regression analysis, deep learning, and analytical thinking.

Practice == Skills

Now that it is clear what a Data Scientist or data engineer would be up to, here comes the important part: How to get to the point where no one can reject you? Having a degree is definitely necessary. And equally necessary is landing a good job. Certifications, for sure, will not suffice to ensure you will have a smooth transition from the learning phase to earning phase. Most companies, though look for candidates with well-rounded profiles, they lean toward people who can demonstrate exceptional skills in data analytics and model building. For eg., learning programming languages like Python and R is one thing, and the way they should be approached for data science projects, quite another. The programming that is required here will be math and data-intensive unlike conventional programming meant for a single user. Joining programming communities like GitHub or Bootcamps offered by data science companies will help to a great extent. It will motivate you into taking up new challenges and learn the nuances of programming.

Invest Time in Building Projects

This can be achieved in two ways. Either join an internship or start working on your own projects. Fortunately, some of the biggest companies like Google and Microsoft are offering data science internships where the interns are involved in meaningful projects and acquire knowledge in every kind of process. Even if you do not get the opportunity to work as an intern, there is no reason to get disheartened. Building projects individually is a possibility too, provided you can get hold of the right resources. Some of the free data repositories include Google Cloud Public Datasets, Amazon Web Services Open Data Registry, Kaggle, and UCI Machine Learning Repository. 

Participate in Data Science competitions

 When studying and practicing are done, it's time for locking horns. Yes, participating in competitions is an important part for not only freshers but also experienced data science professionals. They expose them to entirely different networks and new employment opportunities and they end up teaching you best practices that years of experience cannot imbibe. The reason being you get instant feedback as you compete and cooperate in solving real-world data science problems.