Data Science is the new game, and everybody wants to play. What is important, however, is the degree and specialization that you obtain for your CV, and this will have a significant impact on how far you will go in your data science careers. For a simple example, let us take the spreadsheets of tow decades ago. Due to auto calculation and record storage capabilities, they were universally used. However, with the explosion of data, many sophisticated platforms and software came into play, and this is not limited to just databases. Now databases can incorporate and store any file types, and all these different formats must be analyzed as per the requirements of the organization. This is where data science using sophisticated technologies like R and Hadoop come into the picture. Students and professionals alike are seeking data science certifications to enhance and enrich their data science careers. Furthermore, employers are also seeking certified professionals from reputed data science institutes and paying higher compensation for the same, too.
While data scientist qualifications are offered by a lot of training institutes today, it is important to have a proper data science certification that is accredited and widely accepted by industries globally. The very definition of the data scientist job function is fairly broad. Linkedin, the world’s largest professional networking platform, has hundreds and thousands of profiles of data scientists in fields as diverse as biology, finance, genomics, clinical research, social media and even marketing and advertising.
Because of the very nature of the data science profession, continuous upgrades in technology knowledge and skills, proficiency in new and emerging technology platforms and computer based languages are often required for remaining relevant to new job requirements and industry trends in this ever changing profession.
Another important factor is the experience. Most data scientists start out as analysts. Analytics, in fact, can largely be considered the root of data science and something that enables an easy and well-founded entry into the world of big data and data science. Analytics provides the foundation and represents proficiencies in the statistical aspects of data science to prospective employers. This, of course, immensely increases the value of the prospective candidate to an employer.
Being a programmer, a coder gives one the advantage of knowing exactly how data models are to be designed and what scripts are to be used to extract meaningful insights for the employers. Data cleansing and data preparation, two of the most important aspects of modeling big data, and, subsequently data science, comprise the other functions that help in the formation of a good skill set in big data technologies, data science and data analytics.
Proper industry exposure and networking, as with all professions, is also important.
Globally relevant certifications and experience, therefore, form the basis and the foundation for an entry into the data science profession, no matter which domain. If both of these factors are looked into, then one can not only start but also rapidly growing in the data science profession as a whole.