Data Science and Analytics

The job market is seeking candidates with interdisciplinary competence

If you have an interest in data science, technology, statistics or mathematics, your options have traditionally been to deepen your knowledge and become a specialist in your field. But like all other fields, these are also affected by the job market and its changing needs. Although we still have a great demand for specialists, there is a growing need for candidates with interdisciplinary abilities and competence. The World Economic Forum estimates that the adjustment relating to the division of labour between humans and machines will result in 97 million new roles by 2025. As an educational institution that works in close cooperation with the business world, we receive feedback from different industries, wanting the best of both worlds.

This is a trend we have seen grow over a long time, and BI has therefore developed new, innovative programmes that combine technical science with business. If you are comfortable diving deep into data, studying graphs or measuring probability, but are also motivated by using that expertise to help businesses and society succeed, then an education from BI Norwegian Business School can give you opportunities that make it possible to combine your interests. In a job market where fresh graduates often apply for the same jobs, you can now build a profile that makes you stand out from the crowd.

Data Science and Analytics: Applicable and beneficial to society

For data science to have the transformative effect that it has the potential to, it must be seen in the light of other disciplines as well. One must understand and know where and how it can be useful. Researchers, engineers, specialists and stakeholders within the business, the public sector and professionals working within governance, must work together to optimise the use of the tools we have and are developing for the future. Examples of key industries and societal challenges where this interdisciplinary expertise is important:

• Law enforcement: The police's responsibility is, among other things, to protect us and detect and prevent crime. It is a big task that requires enormous resources. But with what kind of resources, when and where? A critical factor for the police is having the necessary resources available at the right time. This is an optimisation challenge that can be solved through the right use of data. By analysing data, it is possible to find out where and when there is the greatest likelihood of crime occurring and based on that information allocate personnel where it is most needed. See the example of how researchers from BI and external consultants collaborated to design an optimisation model that would help the Norwegian police prioritise their resources:

• Healthcare: The human body generates enormous amounts of data. It is complex and advanced, made up of many different systems that are constantly changing. Data science is a central part of the healthcare system and of monitoring patients' health using registered data. But with the help of, for example, machine learning applications, we can now envision a future healthcare system that is more flexible and efficient. Where previously you have not been able to handle several patients at the same time, you can now, through the use of innovative tools, monitor patients' health in real-time, without them having to be physically present. Hospitals and doctors will be notified in real-time and can prioritise patients to a greater extent, regardless of location, and they will be able to detect symptoms of illness earlier and thus also start treatment earlier.
• Business: All businesses work with data in one way or another. The insight that the business has is the key to the opportunities that can unfold if it’s used correctly. It is crucial to have effective ways of collecting, organising and analysing this data so that businesses can make good decisions for the future. Handling data is becoming more and more complicated, both in terms of volume and methodology. The scepticism of artificial intelligence has turned into excitement, and most businesses today use AI in one way or another, in the form of, for example, simple solutions such as chatbots or more advanced drone technologies. Machine learning is essential in the development of future innovations and it is therefore a valued competence to have.
• Climate Change: It is one of the biggest global threats we face today. Climate research and forecasts tell us the status quo and what these changes are doing to us, but we also need tools that can help us fight the changes, and find solutions for the future. Data science has already come a long way and we see more and more large companies using Artificial intelligence (AI) and machine learning algorithms to collect and analyse data material. Like the technology company IBM, which has enabled, via AI-powered software, organizations to anticipate business risks created by climate change, so that they can act and adapt to the changes.
  
  The research communities are also important contributors. A greener economy is, among other things, part of what is required to combat climate change. But this transition can be costly and risky, especially for commodity producers. Researchers at BI have taken a closer look at this, and by using big data, news and machine learning algorithms, they have developed a method to assess this risk and how they affect exchange rates. This is an important tool for us to have greater control over the risk with the transition to a greener economy. By using the tools of data science, statistics, coding, modelling, and AI, we can contribute to the fight against climate change and make a real difference.
• Time of crisis: Regardless of the reasons, times of crisis have economic implications. Data science can then be of great help. It is important to be able to stay ahead of the challenges so that the government, society and businesses can prevent or prepare for setbacks. During the Covid-19 pandemic, The central bank of Norway and the Ministry of Finance used, for example, an index model, the Financial News Index, developed by researchers at BI. In order to obtain reliable real-time data, data from the quarterly GDP growth was composited with textual information from the news feed. In this way, they could better understand and predict the economic development in Norway during the pandemic.