“How to Become a Data Scientist” Answered!

According to Harvard Business Review, Data Science is the Hottest Job today. With data pouring in at an exponential rate, the demand for skilled professionals in the field of Big Data and Data Science. IBM maintains that by 2020, the demand for data scientists will increase by 28%.
If you’re even remotely associated with the tech/marketing domain, an important question arises at this point – “how to become a data scientist?”.
That’s precisely what we’ll be talking about. But before we get to talking about how to become a data scientist, let’s first understand who data scientists are.

So, what exactly is Data Science and who are Data Scientists?

Data Science is the branch of computer science that involves harnessing vast amounts of data and analysing it by leveraging tools such as automation, statistics, modelling, analytics, and mathematics to extract valuable insights from them for optimizing business growth. Precisely, data science deals in researching and enquiring into the source of information, decoding the patterns hidden within, and eventually transforming it into a useful resource for organisations.
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A Data Scientist is an amalgamation of a mathematician, a computer scientist, and an explorer. They are the new generation of data experts involving the best of both worlds – technology and business. Apart from having a research mindset, data scientists also possess an extensive range of technical and analytical skills that help them to find efficient solutions to complex problems. Because of such a comprehensive skill set required, many beginners are often stuck with the question “how to become a data scientist?”.

With such as vast and booming demand for data scientists, it will be a wise decision to choose a career in Data Science. But the question is, where and how to start? Worry not, we’ll look at how to become a data scientist in the next section.

Here is a comprehensive list of 9 steps that’ll answer the question – how to become a data scientist?

  1. Make Statistics and Applied Mathematics Your Strong Points

Having a strong foundation of Mathematics and Statistics is a must for any data scientist. Especially if you aren’t from a CS/Mathematical background, it is an absolute necessity to brush up on your math and statistical skills to even get started with answering the question “how to become a data scientist?”. Although the most obvious talent of a data scientist is usually analytics, he/she needs to complement this skill along with statistical tools.

  1. Develop A Knack For Coding

When you’re dealing with data, learning to code is a must, irrespective of whether you are a data scientist, a data analyst, or a data architect. It is expected of data scientists to have a good knowledge of statistical programming languages such as Python, R, and SAS.
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  1. Think Big Data

When you are on the path to becoming a data scientist, you have got to be a data-driven professional. So, expand your ‘data’ base by learning and exploring Big Data tools such as Hadoop, MapReduce, Hive, and Spark. Since data scientists have to analyse and process massive amounts of data, it cannot be run on a single machine. Having good knowledge about Big Data technologies will help you accomplish distributed data processing.  To answer the question “how to become a data scientist”, you need to start thinking in terms of Big Data.

  1. Become Accustomed With Databases

A data scientist needs to understand how databases work thoroughly. Most business organisations use MySQL or Cassandra as their database management software to store and analyse the data. So, becoming familiar with the working of databases like MySQL, Cassandra, PostgreSQL, and MongoDB, to name a few, will give you an edge over your competitors in the industry.

  1. Invest Your Time In Multivariable Calculus And Linear Algebra

While many of you may be frowning at this suggestion, data science heavily relies on ML tools and techniques. To use ML tools successfully, one needs to have a comprehensive knowledge of Calculus and Linear Algebra. The more knowledge you have on these platforms, the better will be your way to come up with out-of-the-box solutions for complex problems.

  1. Learn Data Wrangling

Data Wrangling better known as ‘Data Munging’ is the process by which raw, unstructured data is transformed into more convenient and valuable formats to facilitate data analysis.
This is one of the most critical points while answering the question “how to become a data scientist?”. This is one of the most important responsibilities of a data scientist. Data scientists need to use the right tools and skill sets to process unstructured data, thereby unravelling the meaningful patterns in them. Only by doing this can a data scientist bring to light the useful insights hidden within the data that can positively influence the decision-making strategies of organisations.
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  1. Master Data Visualisation

Another crucial responsibility of a data scientist, data visualisation and presentation are the two aspects of data analysis that drive business growth. Hence, data scientists should be familiar with data visualisation tools such as Tableau, Raw, D3.js, Visual.ly, NVD3, etc. however, this is not enough.
Apart from visualizing the data into presentable and handy formats, data scientists should also be aware of the principles and practices of visually encoding data.

  1. Gain Experience. Work On Real Projects

Once you’ve got a solid grasp on all the theoretical aspects of data science, it’s time to get down to the field. Expose yourself to the industry and try to find real data science projects on the Internet. Google Quandl can be an excellent place to start looking for projects.
When you start working on projects in real-time, you get to know your strengths and weaknesses. As you keep working on new projects, you’ll get a chance to work on your flaws and improve them over time.

  1. Compete

The Internet is buzzing with websites that allow data scientists to connect to the data science community and find peers with whom they can engage in productive learning and competition. Kaggle is an excellent training platform for aspiring data scientists.
Being a part of a community, you get exposure to a pool of talent. It gives you a chance to learn from your peers and mentors to sharpen your skills.
These nine steps are all you need to know how to become a data scientist. In conclusion, it can be said that data scientists have to be quite versatile, merging within themselves an array of traits borrowed from various fields. Yes, it will take time to master so many skills, but once you do, you’re in for the job of a lifetime.
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We hope we have answered the question of the day – “how to become a data scientist?”. Don’t think twice, go and get started! Happy learning!

Abhinav Rai

Abhinav is a Data Analyst at UpGrad. He's an experienced Data Analyst with a demonstrated history of working in the higher education industry. Strong information technology professional skilled in Python, R, and Machine Learning.
Abhinav Rai

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