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Data Science as an Asset For the Environmental Sector

Data Science is the extraction of meaning from complex data to enable decision making in a more synthesized manner. This involves recording, storing, and analysis of data that could be either structured or unstructured. A slogan I once read said that the next generation of environmental scientists are data scientists and I really couldn’t agree more.

The environmental field has been one of heightened interest given the ongoing environmental concerns such as climate change and global warming. The growing numbers of discoveries and undertakings within the environment begs the high need for evaluation of various former activities related to the ecosystem. Currently, with the technological development ongoing in the world, the presence and ability to collect data is not as hectic a task, as was in the past. Therefore, data is not quite the problem anymore in environmental activities, its utilization is the major concern at hand.

Surging numbers of environmental-related disasters as well as degradation are all found from patterns recorded over time, especially related to human activities. Global warming for instance being related to pollution rates (i.e. Release of Greenhouse gases) and the increasing consumption needs vis a vis natural resource availability. In ideal, how do we monitor these underlying environmental issues and come up with proper predictions and mitigation measures for environmental conservation? Data science.

Among the great benefit that arises from this integration includes:

  1. Accurate predictions. The analysis process of data both historical and present is based solely on the algorithms present in data science. This allows for the identification of patterns present to allow for predictions. This includes pollution rates in the ecosystem with the increasing industrial activity given patterns over time, thus its prediction in the future given the continuous lifestyle activities.
  2. Sustainable use of natural resources. The mushrooming population dynamics include the utilization of resources that surround them. This, coupled with the heterogeneity of usage calls for sustainable consumption patterns. A study into the data enables environmentalists to identify resources prone to depletion.
  3. Informing policy. Analysis and identification of environmental patterns allow accurate prediction which translates to more informed mitigation measures to reduce environmental calamities. This influences decision-making processes and policy creation based on a well calculated and well thought out analysis of data.
  4. Data transformation. Data in its raw form is not as useful to the people who need it the most. Rather, the ability to transform data in knowledge is essential in keeping people informed on environmental trends taking place around them.

This makes it clear that data science a necessary tool for environmental enthusiasts and professionals. The diversity of interactions in the environment shows just how massive the potential that data science has in the environmental sector. This is mainly due to the end of the age of single-discipline focus in a field but instead, there is the collaboration of various disciplines to combat global concerns.