How to Become a Data Scientist- The Skills, Certifications and Education Required for the Trendy Job

by July 26, 2018

Data science is a bouquet of big data, data analytics, business intelligence, machine learning, artificial intelligence and much more. With a paradigm change in technologies that has made driverless automobiles and voice-controlled chatbots a reality, Data science is becoming an important component of IT. The business application of data science and its components enables business enterprises extract the maximum value through intelligent insights and profitable revenues.

Data science has its applicability in multiple domains, in the supply chain, data science helps to understand effective ways to acquire and manage supply components. For financial market followers including those dealing in commodities, derivatives and stocks data science algorithms assists in developing more accurate and insightful financial models. The applications for data science are limited only by our ability to devise intelligent solutions and processes to which the data may be put as input in other words limitless!

Large amounts of information are collected, stored, and analyzed through data every-day. Data scientists are the professionals who examine and frame the requisite kind of queries against that data collected. This explains the increasing value of data scientists for most companies and organizations. Data scientists are in vogue by all the recruitment agencies, how to become a data scientist? Here are all the guidelines:


Education Background

To pursue the trendy job of a data scientist, the basic requirement is a bachelor’s degree in something computing related. A degree in computer science, computer engineering, informatics, MIS or something similar will do the job. This stream witnesses transition in from other domains. The more math and science degrees under one’s belt before the transition, the easier the adjustment will be.

A strong mathematics background, particularly in statistics and analysis and an equally strong academic foundation in computing is strongly recommended to make a start as a data scientist. Those who have a master’s or Ph.D. degree before entering the workforce may find data science as an attractive and remunerative career option as they may move directly into mid or expert/senior level career opportunities in the beginning.


Work Focus and Career Experience in the Beginning

The long-term experience working with data paves way for exciting innings ahead. Data science is an amalgamation of IT professionals’ crossing over from programming, analyst or database positions where the majority of the interest in data science comes from working with the unstructured data or information that is accumulated through security logs, e-mail messages, customer feedback responses, other text repositories and so on. Technologies like NoSQL and data platforms such as Hadoop, Cloudera and MongoDB are used extensively in data science. In the beginning, data science professionals may expect exposure to text-oriented programming and basic pattern-matching or query formulation. Early stage job responsibilities may also bring coding, testing and code maintenance experience into the table. In the beginning, it is a good idea to work on basic soft skills in oral and written communications, and familiarity with basic business intelligence, analysis principles and practices.


Certifications and Learning Opportunities in the Beginning

To acquaint into data science, basic training is available through massively open online courses or MOOCs. The reputed names include courses from Duke (Coursera), MIT, Caltech, Stanford, and many more. Sensing opportunity, Microsoft has since formulated a Microsoft Professional Program in Data Science which includes nine courses on a variety of related topics and a capstone project. This course aims to instill an introductory curriculum in data science to the applicants. These data science MOOC courses are not free, but give you an opportunity to learn from the comfort of your office or home, which is not a bad bargain. However, if you’re looking for something to “warm up” and get familiar with the data science topic, you can also check out individual instructor-led online courses which can be taken for free on such websites like Udemy or BitDegree by using Udemy coupons.


Mid-career Experience and Work Focus

Data science is a massive subject domain, so by the time you have gathered an experience of three to five years in the work and taken up multiple of projects you will now narrow your career options into on one or more data science specialties and platforms. The mid-career transition may focus into big data programming, analytics, and business intelligence and so on which may promote you to a front-line data science job.

After sending considerable years, you will focus on increasing technical skills and knowledge. This stage will see gain seniority and responsibility among your peers in projects and client calls. This is a stage where soft skills become a more important ingredient of success as you will have to communicate, lead or guide others during this career phase.


Mid-career Certifications and Learning Opportunities

Mid-career as a data scientist gives an immense opportunity for professional growth and specialization. To expand an expats technical knowledge base, there are a broader array of topics and specializations to consider in data science certifications. These certifications relate to big data platform or a specific toolset like Certified Analytics Professional, MongoDB, Dell/EMC, Microsoft, Oracle or SAS certifications.

Cloudera MOOC offers a host of certifications for Data Scientist, Data Engineer, Spark and Hadoop Developer and Administrator for Apache Hadoop credentials. This is a stage where the data science professional may want to master big data programming for Hadoop, Cloudera or MongoDB or try analyzing and interpreting results from big data sets on the cloud.


Work Focus and Career Experience at the Expert Level

After you devote 10 or more years in the data science workforce you will be termed as an expert and will reach the senior levels. At this stage, it is time to get serious about data science and big data technologies.

This is a stage where you will be offered roles such as senior data analyst, senior business intelligence analyst, senior data scientist, big data platform specialist, senior big data developer and so on. Expert or senior level data science expert’s often spearheading project teams of varying sizes balancing the management and technological sides. This is a stage where soft skills and people management become an important part of your daily routine.


Mid-career Certifications and Learning opportunities

Being a senior and an expert into data science, you will go for higher edge technical certifications which include SAS “Advanced Analytics” credentials (four at present). The SAS Institute and Dell/EMC, have designed data science certification programs targeting senior-level experts. Database platform vendors, like Oracle, IBM and Microsoft have added specializations in their certification programs.


Data Science Professionals- Effective skills required

For making a mark into the exciting field of data science, one should be the master of communication skills, project management techniques, critical thinking and problem-solving abilities. The art of being a data scientist also includes being able to influence decision-makers! However, the roadblock occurs when such skills are often completely out of reach for a single person. Thus, instead of looking for the proverbial individual unicorn, companies should build “a team unicorn” to harness the maximum benefits from data science technology.



If you have chosen or are planning to go for data science as your career field you need to buckle up and gather all the deep understanding of principles and practices dominant in the field. You must have the necessary skillsets to understand business impact and revenue that comes through data science applications. Work on your soft skills at the highest level, practice people management because at the senior level, as a data scientist or big data expert you must be able to lead teams of high-level individuals, managers, technical experts and consultants.