Nowadays, everything around us is data related, from using a mobile phone to travelling in a vehicle and from watching the news to working on a laptop. With more than 2.5 quintillion bytes produced each day back in 2014 (Devakunchari, 2014) and nearly 5 billion internet users today (DATAREPORTAL, 2022), it is undeniably true that we live in the data era. With the scale of data being prominent, interpreting the data is more needed than ever. Data Science is the field that undertakes this interpretation, provides meaning to otherwise meaningless data, and creates knowledge by bringing together different fields such as computer science, statistics and last but not least personal thinking and environmental context (Longbing, 2017) and implementing such interpretation to a wide range of sectors.

 One of the sectors in which data science is extensively used is the one of business. It assists in solving business problems, creating context understanding, making well-informed decisions, and eventually fulfilling the business' operational purpose of creating revenue. With the shifting from the typical customer creation approaches to electronic means, such as online ads, e-shops, data collection for data-centric businesses and many others, it became of utmost importance to utilise the collected data in favour of the businesses. Moreover, during the transition to the data era, businesses started understanding the meaning and application of data science in their daily operations. Data-centric approaches such as business intelligence (Chen, et al., 2012), results-based management, revenue projection, customer profiling, and collection and evaluation of metrics, such as online impact and customer engagement (Popescu, 2018), are some of the direct applications of data science in the business sector, which also affect decision making. As an effect, data-driven decision making is making businesses more productive and eventually creating more revenue than those that are not data-driven (Brynjolfsson, et al., 2011). From the above, it is clear that data scientists are much needed and can assist businesses to thrive. Having a master's degree, apart from hands-on expertise, can help boost acquired skills and widen the knowledge spectrum, while supporting one's work and at the same time boosting a professionals' income (Ma, et al., 2019).

 Apart from corporations that use data science with the sole aim to boost income, directly or indirectly, through their day-to-day operations, data science is also used extensively in sciences, with astronomy, medical sciences, oceanology, environmental sciences, economics, and computer science, being only some of the said scientific fields. All the fields of study mentioned above use extensive data to extract valuable conclusions. Examples vary and differ, such as the presence of stellar clusters, the form of genomes, the wave patterns and the changes in the stock market. However, all have one in common, their uninterpreted and often raw nature. Data science can assist in collecting and organizing such data points and eventually analysing them by providing valuable insights such as the possible impact of astral clusters with the earth (Zhang & Zhao, 2015), the presence of tumours using predictive methods (Yi, et al., 2016), early warnings for potential tsunamis and stock value predictions (Gurav & Sidnal, 2017). The specialized methods needed for such data science implementations signify its importance as a whole and, simultaneously, highlight the value of postgraduate degrees in data science. Such degrees provide a solid theoretical background, which, combined with hands-on expertise, would provide better and scientifically appropriate results. It is important to note that extensive use of data science in experimental sciences may disrupt the scientific method and sequence of "first hypothesize, then experiment" (Ozdemir, et al., 2011), which should occur with due regard. Nonetheless, one can think of data science as the fourth paradigm of sciences, bringing change in the traditional scientific processes (Hey, et al., 2009).

 At the same time, data science can support in aiding people worldwide, protecting the world's most vulnerables and providing insights on evolving (complex) humanitarian emergencies. Data science in the humanitarian context is somewhat unique, as data are often collected and analysed under pressure in an emergency context, which boosts the need for data quality assurance (Cai & Zhu, 2015) and, most importantly, refer to people, and not gains or losses; thus, the correct application of data science principles is critical in order to preserve the humanitarian "do no harm" principle (UNHCR, 2019). Additionally, the digitalisation of humanitarian aid adds further needs for data scientists in the humanitarian sector, contrary to traditional methods of assistance, such as cash in hand, which are less data-producing. Data in the humanitarian sector are utilised in several stages of assistance, such as data collection, in order to have "ready-to-use" data in hand for the case of onset emergencies (common operational datasets) (OCHA, 2021), projections on needed amounts (being budget or materials) to cover affected populations, or analysis aiming to understand trends in the population aiming to provide appropriate solutions. The emerging issues, such as data protection and big data (Bell, et al., 2021), signify the additional impact that a master's degree can have in tackling said problems by providing the appropriate academic knowledge. The importance of data science, and data in general, is highlighted by one of the leading United Nations organisations, as an element that plays a crucial role in "... enabling actions that protect, include and empower" (UNHCR, 2019).

 As stated in the introduction, data science undertakes the interpretation of data and the yield of insights in different sectors. Throughout the essay, the significance of data science in different sectors and the importance of postgraduate academic education is presented in a subset of possible implementations. The existing implementations are numerous, and the data science field in general, as well as the possession of a postgraduate degree, will have a significant impact on the community, which will have another trained professional undertaking essential tasks, that may bring change and provide knowledge, while having technical and theoretical knowledge.