In a world where a vast trove of data is being collected every single second, companies are increasingly trying to make sense of this data and use it to increase their profits and customer service.
This is where data scientists and automation come into play. A quick glance at the google trends for people searching for data science shows a rapid increase in the number of searches over the past 5 years.
Being one of the hottest jobs in the past decade, data analytics is a huge field with jobs ranging from business analytics to natural language processing. If you are someone who has graduated or pursuing a degree in college, here are the secrets that you need to know to kick-start a successful career in data science.
Automation is the only constant trend when it comes to data science
Different trends may come and go, but automation is the only trend that stays constant.
According to a recent study by McKinsey, around 800 million data science jobs might be automated by 2030 and for data scientists who rely on objective data to make sense of trends, learning the latest technologies when it comes to automation goes a long way in staying ahead of the curve.
Making your data science career, your job is not likely to get automated any time soon, but it is important to understand the areas which will likely be affected by automation. Data cleansing, data integration, data visualization, and data delivery are some of the areas that are at the risk of automation due to its repetitive nature.
As companies, the main objective would be to maximize profits and reduce redundancies when it comes to personnel. So it is vital that you keep a check on the areas that will likely get automated and update your skills constantly.
Data science is all about using the right piece of information to solve the puzzle/challenge the company has been facing
It is a well-known fact that the least favourite part of a data scientist job would be data collection, organization and cleaning up the irrelevant data, with a study saying nearly 78% agreeing with the opinion.
As a data scientist, your role is not only about dealing with the computer. It also should extensively involve interacting with the various teams in the company like marketing, DevOps, sales, etc.. to learn more about the different facets of the business and understanding the critical pieces of data that is needed to be analyzed to make an impact on the business.
So in order to become a good data scientist, you should not only master machine learning, but you should also interact extensively with the different internal teams to get a sense of the type of data that would be useful for them to increase their efficiency and help them close a deal faster/understand the customer better.
Get a mentor in the form of a good boss or register in an online learning platform which can provide you a medley of opportunities to learn and grow
If you are a fresher, one of the best things you can do is get a data science job in a well funded early-stage startup that can provide you ample opportunities to grow and hone your skills in data science.
Being an early-stage startup, the processes would be still preliminary and you will get a huge opportunity to test out your skills and contribute majorly to the growth of the business.
One more good option you can do as a fresher in python data science is that you can join an online learning platform like Edugrad that provides exclusive Data science projects and courses with dedicated mentorship from Industry experts who can train you in sharpening your data science and machine learning skills.
Remember that as a data scientist, your job is not redundant. Constantly sharpening your skills by undertaking online courses will help keep you ahead of the curve and help you become a successful Data Scientist.
Data science is a mindset
If you ought to really master the field, you need to understand the insights of the industry you work in and have a clear understanding of the business and the nature of the problems to be solved. The skills you learn in theory when implemented in practical needs to take into account the environment, the unique challenges your company faces and the goals of your teammates.
A data scientist should not work in isolation and coordinate with different team members in helping them achieve their goals and increase their efficiency. This is where Edugrad comes into play. With a variety of practical applications and mentors who give you practical problems to solve, we build a bridge and helps our pupils to shine in their professional careers.
If you are an aspiring data scientist/upcoming data science professional, check us out here to hone your skills and advance in your career.
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