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 be a data scientist?”. That’s precisely what we will be talking about. But before we get to talk, let us 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 analyzing it by leveraging tools such as automation, statistics, modeling, 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 organizations.
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 will 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?
Make Statistics & Applied Mathematics
Having a strong foundation of Mathematics & Statistics is mandatory to be a data scientist. Especially if you are not from a Computer Science / Mathematical background, it is an absolute necessity to brush up on your math and statistical skills. Although the most obvious talent of a data scientist is usually analytics, he/she needs to complement this skill along with statistical tools.
Develop A Knack For Coding
When you are dealing with data, learning to code is a necessary, 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.
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.
Become Accustomed With Databases
A data scientist needs to understand how databases work thoroughly. Most business organizations use MySQL or Cassandra as their database management software to store and analyze 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.
Invest Your Time In Multivariable Calculus And Linear Algebra
While some of you may be frowning at this recommendation, data science heavily relies on Machine Learning tools and techniques. To use Machine Learning 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 without of the box solutions for complex problems.
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Learn Data Wrangling
Data Wrangling, also 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 ‘how to be a data scientist?’. This is one of the most important responsibilities of a data scientist.
Data scientists need to use the right tools and skillsets to process unstructured data, thereby unraveling 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 organizations.
Master Data Visualisation
Another crucial responsibility of a data scientist, data visualization and presentation are the two aspects of data analysis that drive business growth. Hence, data scientists should be familiar with data visualization 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.
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 data science 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.
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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 9 steps are all you need to know for understanding the journey of being a data scientist.
In conclusion, it can be said that data scientists have to be quite versatile, merging within themseupGrad’s Exclusive Data Science Webinar for you –
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lves 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. Don’t think twice, go and get started! Happy learning!
What is the right path to become a data scientist?
Data science is a skill and there is no one right path to learn and master a skill. Let’s discuss some options which you can consider.
1. Obtain a bachelor’s degree, a master’s degree or even a PhD in computer science, information technology, math and statistics.
2. Alternatively, you can learn on the job with entry level positions such as data analyst or junior Data Scientist, where you can be trained and get certified in parallel for data visualization, business intelligence applications or even relational database management. This will help you gain a good experience before you get your first job.
3. After mastering Data Science skills, it’s time to bag a high paying job.
What are some must-have skills to become a Data Scientist?
Data science as a discipline is very vast and a good data scientist must master the following :
1. Programming : This is the most fundamental skillset for a data scientist. It helps to augment your statistical knowledge, analyse large databases, work with tools (like GitHub, IDE, Kaggle, etc), develop a knack for coding and statistical programming language (like Python, R, SAS) .
2. Quantitative Analysis : This holds the core of data scientist’s skillset. Quantitative Analysis skills are needed for Experimental design and analysis, modeling of complex economic or growth systems, and Machine learning.
3. Product intuition : Having product knowledge helps understand complex systems that generate all the data analysed by a data scientist. It includes the generation of hypotheses, defining metrics, debugging analyses.
4. Communication : Good communication is indispensable to a data science role as it helps you communicate insights, visualize and present your findings effectively and collaborate with cross-functional team.
5. Teamwork : Data Science works best in an integrated environment where you can leverage cross-functional expertise.
What are the different job roles for Data Scientists?
Data scientists are highly demanded in almost all the major sectors including technology, FMCG, logistics and more. Companies like Google, Amazon, Microsoft, Apple and Facebook have employed almost one-half of the world’s data scientists. Starting from Data Scientist to Data Analyst, Data Engineer to Data Architect, Machine Learning Engineer and Applications Architect there are a variety of roles that one can aspire for.