Data scientists, business analysts and data engineers are among the most sought after positions in the American market—and it’s not hard to understand why.
Basically, data scientists are think-tanks perceived to know everything. From statistics, computer science, mathematics, machine learning, communication, data visualization to deeper learning, they have the brains to deliver solutions to anything.
This year has seen a continued surge in demand for Data Scientists. Even though “data scientist” Job Trends keyword started peaking from 2013, the changing titles and Data Scientists positions are increasingly making this role a mainstream job.
Generally, data scientists are very educated with 88% of them having at least a post-graduate degree and about 46% having PhDs. Even though there are remarkable exceptions, a strong educational background is mandatory to form the depth of knowledge and understanding that is required of a data scientist.
The basic requirement for becoming a data scientist could be a Bachelor’s degree in Computer science, Statistics, Engineering and or Statistics. The main fields of study are Statistics and Mathematics (32%), followed by Computer Science (19%) and Engineering at a distance with 16%. Having a degree in one or more of these courses provides you with basic skills to help you analyze and process big data.
Apparently, a bachelor’s degree is just the beginning as more specialized skills such as Hadoop and Big Data querying will be required. Hence, you can enrol for a Master’s degree program in Mathematics, Data Science, Astrophysics or related field.
Notably, the skills acquired during your bachelor’s degree will allow you to transition easily to data science.
Now, the demand for analytics and data science jobs is high in two different domains within an ecosystem—data science and analytics-enabled families.
The typical analytics-enabled roles include the Chief Executive Officer, Director of IT, Chief Data Officer, Financial Manager, Human Resource Manager and Marketing Manager. Generally, the direct payoff for increasing the analytics IQ in these positions is higher output and operational efficiency.
These people are considered to have the know-how to identify client wants and needs using social analytics and or extraordinary network activities displayed by real-time dashboards or even forecasting inventory based on predictive analytics.
It’s no wonder that 67% of job opportunities are analytics-enabled, demanding domain or functional expertise besides data science. Analytics-enabled jobs also require hands-on experience with visualization and reporting software to assist in the gathering and testing of data.
Requirements for analytics and data science jobs are generally multi-disciplinary and require one to have great capabilities to use analytics to create value for their organization.
Data scientists are also armed with additional skills including problem-solving to help at the workplace and soft skills like creativity, communication and teamwork.
Hence, analytics and data scientist candidates bring on the table a holistic skill set that is not only rare but highly sought-after by many organizations and businesses.
Today, data scientists are popularly referred to as T-shaped individuals as they pose principal competencies and a range of well-honed skills to help them deliver real value across a myriad of domains and functions. This simply explains why they are a gem and a most sought-after skill.