Who is a Data Scientist?
People using computers or smartphones continuously generates datas: just think about all the social network posts or video uploaded daily on the broadcast platforms. Each device connected to the Internet continuously generates data, tons of data!
Today, in the Internet of Things (IoT) era, there are a lot of people that daily use computers or connected smartphones and there are a lot of smart things and devices (from the smart watches to the smart cities) that are able to generate datas. It has been estimated that each day, in the world, 2,5 quintillion of bytes are generated, that is the storage capacity of 5 billion of the latest flagship smartphones, and the expected trend is that this data volume, will double each 2 years.
Such impressive quantity of data has an even more impressive value once transformed in information, i.e. to understand market dynamics, to define business forecast and strategies, to predict future scenarios, etc.
To effectively handle those huge volumes of data, cutting edge technologies are needed, but also human skills are required to manage such technologies and data in order to extract meaning and useful information from it, and to properly present and interpret insights. These skills are typically belonging to Data Scientist, the current most in-demand, hottest and trendy profession, that is expected to rise in the coming years.
A Data Scientist is a professional with hard skills in math, statistics, computer science (programming, databases, tools), etc. and soft skills in analysis, problem solving, effective communication as well as managerial competences but, more than the others, passionate about challenges, curious about the world and able to understand and dialogue with the Business at ease.
The path to become a Data Scientist starts from curiosity and passion. Then a solid educational background is required, that is a bachelor’s degree (BSc or BA) typically in Computer Science, Engineering, Mathematics, Statistics or other quantitative field. But also Finance, Economics, Marketing, etc. are a good starting point if the missing skills are later acquired through post-graduate education.
During my professional career, I’ve met many colleagues currently working as Data Scientists, that started in Technical departments, i.e. as Information Technology Engineers, then have turned towards the business following their passion and curiosity for analysing data, dynamics, trends, etc.; as well I have meet excellent Data Scientist working in Business departments (Marketing, Business Units, etc.), that have learned how to manage technical tools and the technology to pursue the same goals of managing Datas and extracting valuable information and insights.
To complete the educational path, a Master degree in the field of Data Science is highly recommended as it completes the base education with all the needed skills that are required to succeed in a professional environment: when evaluating the Master degree don’t focus just on the hard, technical skills and knowledge that the course will provide; do focus especially on the soft and the managerial skills, as they are more difficult to master than the hard ones and so, once acquired, they would make the difference. Therefore, when evaluating a syllabus or a didactic program, check if there are classes on managerial, business and transversal competences (i.e. legal aspects), team working activities, projects that require to work together with the other students, etc.: definitely all the kind of activities that can foster the development of the soft and the managerial skills.
Finally, to become a valuable Data Scientist, it is strongly suggested to acquire a deep knowledge of the environment (industry, market, etc.): an effective way is to practice and to work within the environment of interest, in order to learn the business idioms, the dynamics, the main issues, pain points and challenges, etc.
A personal suggestion: when choosing the field, for the Bachelor, for the Master degree or for the job experiences, don’t follow only the job market (i.e. trends, vacancies, salaries, etc.): instead, follow your passion!!!
The motivational lever coming from inside (passion, attitude, etc.) is always stronger than the external one (job, salary, etc.) In conclusion, if you feel to have an analytical brain, if you are curious to better understand how things happen and if you like to continuously deal with new stimulating challenges, just follow your passion and build your path to become a successful Data Scientist!
Article curated by Francesco Amendola, Program Director of the Rome Business School Master in Data Science.