The world is moving towards complete digitisation and is expected to generate copious amounts of data in the future. However, to make sense of this data we need specialists who can read, model and organise data in coherent detail. Data science has emerged as an effective means of handling data to extract meaning out of random numbers and figures.
Clearly, Harvard Business Review wasn’t bluffing when they suggested data scientists to have the “sexiest job of the 21st century” – not only is it one of the most crucial profiles in the market today but is also among the highest paid ones. If you are wondering what it takes to become one, we have laid down the steps to become a data scientist. 
Step 1:
A career in data science requires constant learning and upskilling and it cannot be an impulsive decision. If you are planning a long sprint in this direction, make sure you have a suitable background and aptitude. Start by asking yourself the following questions to find out if this path is for you.
– Do you have an educational background in computer science, information technology, mathematics, statistics or a similar branch of study?
– Do programming languages excite you?
– Are you a proactive learner who is willing to pick up the tricks of the trade ahead of the market?
– Do you enjoy handling complex data sets to understand patterns?
Data science might be a rewarding career choice but it requires concerted efforts. A course in data science can help you master the essentials and make you industry-ready. 
Step 2:
If you are from a non-technical background and still want to pursue a career in data science, fret not. You can up your chances of becoming a data scientist by developing skills in the field of applied mathematics and statistics. Market research shows that a considerable number of data scientists hail from a business or economics background. If you are an aspiring candidate with a similar educational background, brush up your skills in mathematics and statistics as a preparatory step.
Step 3:
Master the basics of machine learning as it is one of the most crucial components of data science. It is used for a number of data science applications, ranging from reporting forecasts to identifying data modelling patterns. Familiarity with machine learning tools and techniques will help you to master other data science tools with ease. Once you pick up the basic machine learning tools and functionalities, designing and using algorithms for data modelling will become easier. 
Step 4:
Programming is one of the main requirements in a data science profile. Learn to code so that you can read and analyse data sets. Pick up programming languages like Python, R, SAS and more. Python remains one of the most widely used programming languages owing to its flexibility. Among the querying languages, SQL is prominent, so learning both these programming languages will help you launch your data science careers successfully.
Step 5:
The next step to become a data scientist should be learning data munging. It is a process of looking through messy data sets to identify and discard redundant data. This cleanup process is a preparatory step towards data analysis. Data munging helps data scientists to analyse and present data in a readable format.
Step 6:
For a data scientist, if data analysis is half of the job, the other half is reporting. Business decision-makers refer to data reports to drive business and generate revenue. But for the data to make sense, it must be put into data visualisation tools like charts, Tableau, d3.js, Raw and more. Data scientists must familiarise themselves with the principles of data communication systems and visual encoding to present data in an easy and readable format.
Step 7:
The best way to fine-tune your skills in data science is by applying that knowledge to practice. Once you have mastered all the theoretical knowledge, start working on projects that replicate real-world data complexities faced by companies. Alternatively, you can also intern at leading data science companies or join bootcamps to get hands-on experience on real data science applications. 
Step 8:
Stay updated on the recent developments in the field of data science. The amount of data generated by the world is increasing each day and in keeping with this exponential growth, data science is also evolving. Data scientists must learn ways of enhancing data tracking and analysing applications to ensure resource optimisation. Constant learning is crucial for data scientists to stay on top of their game. Look for educational and professional development opportunities that will advance your career in data science. 
Step 9: 
Once you have completed your education in data science and gathered experience working on projects and as interns, it’s time to create a portfolio showcasing the same. Update your resume, highlighting your data science skills adequately and start applying for relevant openings. You can prepare for interviews by referring to the most popular data science questions and answers
After you have followed these nine aforementioned steps, your data science career will be all set to take off. With an arsenal full of data science skills, landing a relevant role won’t be difficult, especially if you have worked on projects and have industry-relevant experience. However, in order to keep growing in the field, you must constantly seek challenges and keep learning. Start viewing all kinds of business circumstances as scopes for studying data – start thinking like a data scientist. Courses and certifications will help you stay updated about the latest technologies in the field and give you an edge over your competitions. Great Learning, one of India’s premier education institutes offers courses that cover all the essentials of data science and make professionals industry-ready. Check out a data science program to get a better understanding of the curriculum.
If you found our steps to become a data scientist helpful and enlightening, check out the online course, PG program in Data Science and Business Analytics, to learn seamlessly with the comfort of your own place and time.  

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