on 03 May, 2019 by Bobby Vohra, Author for Datafloq
Data science, is it a piece of cake? A data scientist is still deemed as the sexiest job of the 21st century. Though the profession had been ranked on the top of the job lists three years in a row, the deficit for such professionals is on a constant rise.
As estimated by the European Commission, it is projected that 100,000 new data jobs will be available by 2020. While the truth still prevails, will there be enough supply for data science professionals? The organizations now have awakened to the fear that the demand will outpace the supply. With businesses on the rise, it is evident that data science has the potential to drive organizations to a different level altogether. IBM projects that the data science jobs will account 28% of all other digital jobs by 2020. On another report, it is stated that on an average, most of the unfilled data science jobs remained vacant for nearly 45 days, since those who were applying for the job role do not have the relevant skills to become a data science professional.
Over the past years, technology skills such as machine learning, big data, and data science have given birth to plenty of job opportunities. But again, these are the job roles that take a prolonged time to recruit. And if these jobs are found to remain vacant, it can be a challenge.
To become a data scientist, one must often come from certain background. Here is a roadmap to how one can become a professional data scientist.
The educational skills
Most of the data scientists often come with a Ph.D. degree and are highly educated. Around 88% of the data scientists have a master’s degree, while 32% have a degree in mathematics and statistics, 19% in computer science degree, and 16% in an engineering degree. A degree in any of these fields will give you a head start to process and analyze data. However, just by earning these degrees will not make you a data scientist.
Although you have entered the realm of the data science field, the truth is, this will not suffice to earn a data science career. The professionals of today are undertaking online reskilling programs in the form of certifications from credible resources. Therefore, being a certified data scientist, the skills you have learned through the certification programs will enable you to grasp a job in the data science field sooner than you realize.
Apart from educational skills, these are the other technical skills you need to learn to enable your data science career.
R and Python programming
In-depth knowledge of R programming is necessary to solve statistical problems. Using R, one will be able to solve any kind of data science problems. It is said that 43% of the data scientist use R.
Python, on the other hand, is the most used programming language used by a data scientist. Because of the versatility feature that it offers, Python is used generally. Python takes up all formats of data making it possible for the data to be imported in SQL tables in the code. This enables one to create different datasets and further allows one to find any type of dataset required from Google.
As a data scientist learning machine learning algorithms such as linear regression, logistic regression, K – means cluster and many others will help one solve different data science related problems that are based on predictions.
Data needs to be presented in such a manner where it is easy for a layman to understand. For such purposes, data scientists must be aware of tools like ggplots, RapidMiner, Tableau, etc. These tools help convert complex results into formats in the form of images, graphs or charts.
A certified data scientist should also be able to have a clear practical approach with the tools and technologies used in data science. Apart from the above skills, one should have extensive knowledge in databases such as SQL and MongoDB, predictive analytics, business acumen, intellectual curiosity, and communication skills.
As businesses increases, the job role will get competent as more professionals hop into a data science career. Nonetheless, to remain relevant in the industry a data science professional should keep his ears alert and stay in sync with the current industry trends.