When data meets nature
With the threat of mother earth casting us into oblivion looming large, it is high time we looked for new ways to reduce all the harms we have been doing to her. Since it is the age of information, data science might be that one tonic that could do the trick
"The environment is where we all meet; where all have a mutual interest; it is the one thing all of us share." - Lady Bird Johnson
Nature is the only thing that every single living being here on this earth shares. Because of nature's care, we are where we are now. But because of human ambition and ignorance, we are slowly breaking down our very cradle of life. There are a number of environmental challenges facing the world today, including climate change, deforestation, air and water pollution, and the loss of biodiversity.
The problem is, mother earth will survive these changes and recover after a few hiccups, but we will not. If we keep harming nature, it will sooner or later cast us into oblivion. This is why we should constantly look for ways to reduce these harms done by ourselves.
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The current age is the age of information. The development of computers in the mid-20th century paved the way for the collection and analysis of large datasets, whereas the emergence of big data has broken ground for enormous data being analysed in multiple ways. Development of "Machine learning", which is a type of artificial intelligence that allows computers to learn from data without being explicitly programmed, has become an important tool in data science. Therefore, as data is becoming our biggest tool in the current age, we have to find out how to use this tool to at least minimise the harm that we humans have done to our only home – Earth.
Data science is an interdisciplinary field that uses scientific methods, processes, and systems to extract knowledge and insights from structured and unstructured data. It combines techniques from fields such as statistics, computer science, and domain expertise to draw conclusions and make predictions from data.
Data science is also a key part of many industries, including finance, healthcare, and e-commerce. For example, a data scientist working in the finance industry might use data to develop algorithms for predicting stock prices, or to identify fraudulent transactions. In healthcare, data science might be used to develop models for predicting the likelihood of a patient developing a particular disease or to identify trends in patient behaviour.
To give a specific example, consider a data scientist working for an online vendor. The data scientist might use data science techniques to analyse customer data, including purchase history, demographics, and browsing behaviour. This analysis could reveal insights such as the most popular products, the average customer lifetime value, or the factors that influence customer loyalty.
These insights could then be used to inform decisions about product development, marketing, and other aspects of the business. For example, the seller might decide to focus its marketing efforts on the demographics that have the highest customer lifetime value, or to develop new products based on the most popular items. Overall, data science is a powerful tool for understanding and making sense of large and complex data sets. By applying advanced analytical and statistical techniques, data science can help organisations to unlock valuable insights and make more informed decisions.
Data science is a rapidly growing field that uses advanced analytical and statistical methods to extract insights from data. It has a wide range of applications, including in the environmental sector. For instance, data science can be used to monitor environmental conditions and track changes over time. Sensors and other monitoring devices can collect data on air and water quality, temperature, and other factors. This data can be analysed to detect trends and patterns, allowing for early detection of potential problems and timely intervention.
Data science can also be used to model and predict the impact of human activities on the environment. In particular, scientists can use data on emissions, land use, and other factors to create models that simulate the effects of different scenarios on air and water quality, biodiversity, and other aspects of the environment. These models can be used to evaluate the potential impact of different policies and actions, and to identify the most effective strategies for protecting the environment.
Moreover, data science can be used to support the development of new technologies and solutions for environmental challenges. Data on the performance of different materials and processes can be used to optimise the design of renewable energy systems and other technologies. Data on the behaviour of wildlife and ecosystems can be used to support the development of conservation strategies.
Last but not least, data science can be used to engage the public and build support for environmental protection. Data on the state of the environment and the impact of different policies can be visualised in interactive maps, graphs, and other formats, making it easier for people to understand and engage with these issues.
Overall, data science has a lot to offer in terms of protecting the environment. By providing new ways to monitor, model, and understand environmental systems, data science can support the development of effective policies and solutions for addressing some of the most pressing environmental challenges of our time.
Shafin Haque Omlan is Research Associate, Bangladesh Institute of Governance and Management (BIGM).
Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the opinions and views of The Business Standard.
