Step By Step Guide To Become a Data Scientist

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The recent trendsetting career option: Data science has been dubbed as ‘the sexiest job of the 21st century’ by Harvard Business Reviews. The introduction of high-level technologies like machine learning and artificial intelligence have paved way to countless opportunities in the data handling sector. The need for data storage, managing, sorting, interpreting and analysing is a necessity in every sector ranging from supermarket management to million dollar turn-over businesses. 

Adding value to the IBM prediction ‘Demand for Data Scientists sill soar 28% by 2020’, is the hike in the number of Data Science and Analytics job listings from nearly 364,000 listings to approximately 2,720,000 by the end of 2020.

Data-run industries are all praises for data science for a number of business insights it uncovers. Over the past decade, the consumption of information online has remarkably shot up and has led to a stage where all our basic activities are carried out online. With so much data produced every day, there is a huge demand for data scientists today. Statistics speak that the US leads the data science market, requiring 190,000 data scientists by next year. India too, joins this elite bandwagon, requiring Data Scientists across a diverse range of industries. It is estimated that by 2025, the Big Data analytics sector in India is to grow eightfold, reaching $16 billion.

From recent surveys it is predicted that in 2021, companies will demand more from their hired data scientists, and these top-tier analysts will be viewed as “wizards of all business solutions.” The speculated annual demand for Data Science job roles, which covers data engineers, data analysts, data developers and related employees will hit the 700,000 mark next year.

Data Science is the process of probing through huge chunks of data, processing and analysing them for meaningful inputs that can help businesses get insights on concerns, customer experience, supply-chain and other beneficial data that would complement their business operations.

Data scientists can come from a wide range of educational backgrounds and practical experience, but what you need to set milestones is the right guidance to take you to your career goal.

Here are the steps to follow on your path to becoming a Data Scientist!

1. Train yourself in the core subjects of Data Science: Algebra, Statistics, ML

Firstly, understand how Data Science is different from Statistics. Statistics uses conventional theories in organising and simplifying data. Data Science on the other hand is an advanced field and employ computers and automated technologies in solving modern day data queries. If Math and Algebra excite you, consider yourself on the right track already. Invest in application-level study of the core subjects: Algebra, Stats and ML.

2. Familiarise with the game-changer: BigData.

BigData is not a buzzword anaymore. Data scientists handle a massive volume of segregated and non-segregated data on which analysis cannot be performed using a single machine. Big data software like Hadoop, MapReduce, or Spark are made used for distributed processing.

3. Set your Qualification bar right:

Since we are talking about a technical job and considering the competitive world we live in, you can never be better than the next applicant if your degree weighs less. To keep up with other aspirants, earn a bachelor’s degree in any Data science related subjects to begin with. You may then proceed towards a master’s degree. Data science requires you to know the fundamentals of analytics. You need to be capable of working on analytics tools and understand the basics of data processing to get started. Working it out by yourself taking help from online platforms providing training in Data analysis with abundant application-level experience could be a boon too.

4. Polish up your Communication skills:

Apart from technical skills, Data Science jobs also look out for excellent communication skills. With the analytical expertise you have a thorough understanding of the extracted insights. But when an outsider sees your strategy for the first time, he or she would stand puzzled. Hence, you must also be an efficient communicator of your insights and have fine skills at working on presentations, spreadsheets and documents. So, invest your time in bettering your communication alongside working on your technical skill-set.

5. Be ready to present yourself as a skilled candidate:

Data science requires you to have or develop skills in statistics, data science tools, communication skills, commendable knowledge in quants and business acumen. ML is another cognitive tool you should be working on. ML deals with the development of systems that can learn, adapt and improve depending on the data that is fed to them. Machine Learning requires you to assert command over crucial algorithms. Some of which include Random Forest, Neural Networks, SVM, Logistic Regression and more. Understand that every company follows a distinct approach to data science. It is next to impossible to master everything in data science. The right strategy would be to upskill yourself in some universally recognized and adopted technologies like SAS/R, Python coding, SQL database and Hadoop platform will help you switch to data science accordingly.

6. Network your way through the job recruitment pool:

Shared interests bring people together and hence working on enhancing your networking skills should be your next target. Get in touch with people from your same field, currently working, seeking job or researching on related subjects. We have an abundance of web-based tools to help you connect with people digitally and stay updated about new advancements and opportunities that come along.

7. More isn’t enough: Experience gain hustle:

Be it an assistive position, or a top-tier high paid job, one thing any recruiter would demand for is experience. Go easy on demanding a company job right from the beginning. While embarking on your Data Scientist career, kickstarting with start-ups will let you grow and help you weigh your experience scale up. More than learning, Data Science is better understood effectively by practicing it. If you intend to take up a course in Data Science, make sure it offers capstone projects, case studies and enough real-time data sets to work on. More than the theory, it’s your hands on experience that counts.