An Opinion
Being a Data Scientist in the News and Media Industry
New Roles Emerges as Technology Changes Expectations
In the last decade, data scientist roles have increased enormously. Nowadays, almost every company is looking for a data scientist for their businesses. This role’s importance comes from its social and technical skills. A data scientist should have both these type of attributes in his/her toolbox. Especially, I believe that both social and technical skills are equally important in the News and Media industry. Even sometimes, social skills can be more important than technical ones. I would say %51 social, %49 technical.
First of all, two of the most important social aspects of the News and Media industry are presenting an idea and human interaction at a higher level. Of course, these aspects can be found in many sectors, but people are more aware of having them, especially in this area. For example, you need to have good presentation skills as a data scientist if you work in this field. Moreover, being a data-storyteller can be expected from you. Having a social skill as such dictates you more in believing in what you are doing. Needless to say, you cannot present something you do not believe. Furthermore, being a data scientist in this area may require more awareness about human interaction concepts. For instance, understanding expectations in reports you prepare is fundamental as a data scientist. Besides your day-to-day job activities, studying human behaviour, emotions, even psychology, can add great value to your work. It is also obvious at COVID-19 pandemic times that distance working and presentations over video conference tools gained importance. Practising with these tools is inevitable in the sense of improving social skills as a data scientist.
Secondly, there are two important technical aspects of the News and Media space: visualisation and data inspection tools. When it comes to data visualisation, there are popular tools for that such as Tableau, Power BI, etc. However, MS Excel can also be added to this tool list. To give an example, it is no surprise that data scientists deal with a huge amount of data in their jobs, and being a data scientist in the News and Media industry means that they still have that amount of data, maybe more. Although every data scientist’s job description can include preparing visualising data in the reports, management’s demands in the News and Media industry may become more challenging. It is because naturally, all people in this area tend to think more visually actually. Reasons can be dealing with tv broadcast, social media data all the time. Additionally, being familiar with user interface concepts can bring great achievements for a data scientist in this industry. Besides visualising tools, a data scientist in this area needs to have programming skills, especially Python, R and SQL languages. These languages are the top ones when it comes to dealing and getting familiar with big data. The more strength a data scientist has in these languages, the easier and faster he/she can grasp industrial concepts and terms from data.
In conclusion, data scientists are life-long learners, and they need to improve themselves both socially and technically. Clearly, these social and technical skills play a great role in all data scientists’ lives. Being a data scientist in the News and Media industry can only ask more social skills at the end of the day. In data space, I believe both social and technical skills equally important. At the end of the day, if data scientist cannot use these two wisely and efficiently in their job, then the magic of being a data scientist is lost, unfortunately.
