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Opportunities for data scientists?
I am a college student majoring in Environmental Science. I'm considering adding a second major of Data Science. However, I am wondering how many opportunities there are for data scientists that are not working for just any large company to help them maximize profits. For example, are there opportunities for data science in the renewable energy field, such as using data to problem solve/ figure out how to transition to renewable energy or similar topics? I am interested in environmental-related things but also any other interesting/ meaningful application of data science that I may not be aware of. Thank you for your time.
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10 answers
Updated
Gregory’s Answer
Hi Stella,
You are about to embrace a brilliant career combining both environment science and data science majors ... for the benefit of the future generations. Multiple industries are actively searching for data scientist skills. The resources industry (mining, energy, utilities) is no exception; all the more that the energy transition is accelerating at pace and scale across the world. The importance of data science is becoming more and more critical with all countries in the world developing pledge toward carbon neutral and net zero horizons.
This major trend is accelerated by the growing number of connected devices, combined with IoT technologies such as sensors that collect more and more data. Data science is becoming a core capability in that field through artificial intelligence, data analytics, data mining, blockchain and machine learning technologies to derive actionable insights from the vast growing quantity of data. Data science stands to play a significant role in improving operational efficiencies, optimise workforce allocation, help reducing carbon & GHG emissions, produce cleaner energy, model equipment failure probability, predict & prevent outages, detect energy thefts, contribute to environmental research to name a few. In the utilities (water, power, gas) sector especially, the decentralization of the energy sources (wind, solar, EV, batteries, microgrids, hydro, biomass, etc.) creates greater impact on the grid. New advanced network management, flexibility and demand-response capabilities are getting developed to support the change. Once again, data science is at the heart of this evolving ecosystem. The best part of it? This is a global trend. This means not only local job opportunities, but truly international positions as well.
In term of companies and job opportunities, there are multiples avenues to consider depending on your own personal vision, career aspirations and of course desire to help develop clean energy. You may look at large international consulting companies (EY, Accenture, McKinsey, BCG, Deloitte, etc.), boutique or niche analytics companies, data science startups (Greenbird, Kaluza, etc.), established utility companies, engineering companies investing heavily in energy transition technologies (GE, Siemens, Schneider, ABB, etc.), large international companies with advanced data science capabilities (IBM, Microsoft, etc.), environmental research institutes, government agencies. etc. The list is endless. With no doubt all companies are looking for profits ... but not only with ESG & sustainability getting at the core of the government agenda across the world.
These are just a few examples and considerations. There is a LOT of literature available on that matter and I would encourage to read as much as possible. You may be interested by this post for instance: https://ayeshalshukri.co.uk/category/discourse/can-machine-learning-help-develop-clean-and-renewable-energy/. You may also look at how to contribute to global initiatives such as Time for the Planet.
The world is yours!
You are about to embrace a brilliant career combining both environment science and data science majors ... for the benefit of the future generations. Multiple industries are actively searching for data scientist skills. The resources industry (mining, energy, utilities) is no exception; all the more that the energy transition is accelerating at pace and scale across the world. The importance of data science is becoming more and more critical with all countries in the world developing pledge toward carbon neutral and net zero horizons.
This major trend is accelerated by the growing number of connected devices, combined with IoT technologies such as sensors that collect more and more data. Data science is becoming a core capability in that field through artificial intelligence, data analytics, data mining, blockchain and machine learning technologies to derive actionable insights from the vast growing quantity of data. Data science stands to play a significant role in improving operational efficiencies, optimise workforce allocation, help reducing carbon & GHG emissions, produce cleaner energy, model equipment failure probability, predict & prevent outages, detect energy thefts, contribute to environmental research to name a few. In the utilities (water, power, gas) sector especially, the decentralization of the energy sources (wind, solar, EV, batteries, microgrids, hydro, biomass, etc.) creates greater impact on the grid. New advanced network management, flexibility and demand-response capabilities are getting developed to support the change. Once again, data science is at the heart of this evolving ecosystem. The best part of it? This is a global trend. This means not only local job opportunities, but truly international positions as well.
In term of companies and job opportunities, there are multiples avenues to consider depending on your own personal vision, career aspirations and of course desire to help develop clean energy. You may look at large international consulting companies (EY, Accenture, McKinsey, BCG, Deloitte, etc.), boutique or niche analytics companies, data science startups (Greenbird, Kaluza, etc.), established utility companies, engineering companies investing heavily in energy transition technologies (GE, Siemens, Schneider, ABB, etc.), large international companies with advanced data science capabilities (IBM, Microsoft, etc.), environmental research institutes, government agencies. etc. The list is endless. With no doubt all companies are looking for profits ... but not only with ESG & sustainability getting at the core of the government agenda across the world.
These are just a few examples and considerations. There is a LOT of literature available on that matter and I would encourage to read as much as possible. You may be interested by this post for instance: https://ayeshalshukri.co.uk/category/discourse/can-machine-learning-help-develop-clean-and-renewable-energy/. You may also look at how to contribute to global initiatives such as Time for the Planet.
The world is yours!
Thank you so much for the detailed response! It sounds like there are exciting opportunities.
Stella
Very much yes :-)
Gregory Poussardin
Updated
Sri’s Answer
This is a powerful combination. Data Science is pretty much applicable in every field or business these days wherein data is powering business based on predictive analytics. Check this out. Good article related to this: https://www.earthdatascience.org/blog/earth-data-scientist-demand/#:~:text=Earth%20analytics%2C%20also%20known%20as,after%20in%20academia%20and%20industry.
Thank you so much! The article is interesting.
Stella
Updated
William’s Answer
Hey Stella!
Most business decision making used in Business Analytics are generated by Data Scientists.
Many ERP software used in businesses have data integration and analytics tools that are automated to suit business needs. You may be useful in that.
AI are used extensively in Environmental Science by integrating GIS data like the transportation monitoring in Google map navigation that suggest driving routes and traffic conditions are possible by integration of GIS spatial analysis data to AI.
Companies buy ready made data from research companies who.
Most business decision making used in Business Analytics are generated by Data Scientists.
Many ERP software used in businesses have data integration and analytics tools that are automated to suit business needs. You may be useful in that.
AI are used extensively in Environmental Science by integrating GIS data like the transportation monitoring in Google map navigation that suggest driving routes and traffic conditions are possible by integration of GIS spatial analysis data to AI.
Companies buy ready made data from research companies who.
James Constantine Frangos
Consultant Dietitian & Software Developer since 1972 => Nutrition Education => Health & Longevity => Self-Actualization.
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James Constantine’s Answer
Dear Stella,
Exploring the Role of Data Scientists in Environmental Science and Renewable Energy
Data science is increasingly becoming a key player in diverse sectors, notably environmental science and renewable energy. As an Environmental Science major contemplating a second major in Data Science, it's commendable that you're delving into the numerous possibilities beyond the conventional corporate sphere. Here's a closer look at the prospects for data scientists within environmental science and renewable energy:
1. Renewable Energy Sector: The renewable energy arena offers a wealth of opportunities for data scientists to effect significant change. Data science techniques can be employed to boost renewable energy output, enhance efficiency, and promote sustainable practices. For instance, data analytics can assist in forecasting energy requirements, maximizing power generation from renewable sources like solar and wind, and advancing smart grid technologies.
Furthermore, data science can be instrumental in tackling issues such as optimizing energy storage, integrating renewables into the grid, conducting predictive maintenance of renewable energy infrastructure, and assessing the environmental impacts of renewable energy initiatives.
2. Environmental Data Analysis: In the realm of environmental science, data science can be utilized to scrutinize large datasets associated with climate change, biodiversity conservation, natural resource management, pollution surveillance, and ecosystem health. By harnessing advanced analytics methods, data scientists can draw meaningful insights from intricate environmental datasets to bolster evidence-based decision-making and policy development.
Applications of data science in environmental science encompass climate modeling, species distribution modeling, remote sensing analysis, air and water quality monitoring, ecological prediction, and risk evaluation for natural disasters.
3. Sustainable Development Endeavors: Data scientists can make a substantial contribution to sustainable development efforts by applying their expertise to analyze data associated with social, economic, and environmental aspects. By incorporating data-driven methodologies into sustainability projects, data scientists can assist organizations in gauging progress towards sustainable development objectives, pinpointing areas for enhancement, and devising effective strategies for sustainable expansion.
In conclusion, the convergence of data science and environmental science presents a plethora of opportunities for innovative problem-solving and influential decision-making in areas vital to the future health of our planet.
Top 3 Credible Sources Used:
United Nations Environment Programme (UNEP): The UNEP offers invaluable insights into the role of data science in global environmental conservation initiatives. Their reports and publications provide authoritative data on how data analytics is being deployed to tackle environmental issues and foster sustainable development.
Renewable Energy World: Renewable Energy World is a trusted source for information on trends and advancements in the renewable energy sector. Their articles discuss topics such as data-driven solutions for enhancing renewable energy systems and harnessing technology for a greener energy future.
Environmental Protection Agency (EPA): The EPA is a dependable source for information on environmental concerns in the United States. Their research reports and databases offer valuable data that can be analyzed by data scientists to support evidence-based decision-making on environmental policies and regulations.
God Bless You,
James.
Exploring the Role of Data Scientists in Environmental Science and Renewable Energy
Data science is increasingly becoming a key player in diverse sectors, notably environmental science and renewable energy. As an Environmental Science major contemplating a second major in Data Science, it's commendable that you're delving into the numerous possibilities beyond the conventional corporate sphere. Here's a closer look at the prospects for data scientists within environmental science and renewable energy:
1. Renewable Energy Sector: The renewable energy arena offers a wealth of opportunities for data scientists to effect significant change. Data science techniques can be employed to boost renewable energy output, enhance efficiency, and promote sustainable practices. For instance, data analytics can assist in forecasting energy requirements, maximizing power generation from renewable sources like solar and wind, and advancing smart grid technologies.
Furthermore, data science can be instrumental in tackling issues such as optimizing energy storage, integrating renewables into the grid, conducting predictive maintenance of renewable energy infrastructure, and assessing the environmental impacts of renewable energy initiatives.
2. Environmental Data Analysis: In the realm of environmental science, data science can be utilized to scrutinize large datasets associated with climate change, biodiversity conservation, natural resource management, pollution surveillance, and ecosystem health. By harnessing advanced analytics methods, data scientists can draw meaningful insights from intricate environmental datasets to bolster evidence-based decision-making and policy development.
Applications of data science in environmental science encompass climate modeling, species distribution modeling, remote sensing analysis, air and water quality monitoring, ecological prediction, and risk evaluation for natural disasters.
3. Sustainable Development Endeavors: Data scientists can make a substantial contribution to sustainable development efforts by applying their expertise to analyze data associated with social, economic, and environmental aspects. By incorporating data-driven methodologies into sustainability projects, data scientists can assist organizations in gauging progress towards sustainable development objectives, pinpointing areas for enhancement, and devising effective strategies for sustainable expansion.
In conclusion, the convergence of data science and environmental science presents a plethora of opportunities for innovative problem-solving and influential decision-making in areas vital to the future health of our planet.
Top 3 Credible Sources Used:
United Nations Environment Programme (UNEP): The UNEP offers invaluable insights into the role of data science in global environmental conservation initiatives. Their reports and publications provide authoritative data on how data analytics is being deployed to tackle environmental issues and foster sustainable development.
Renewable Energy World: Renewable Energy World is a trusted source for information on trends and advancements in the renewable energy sector. Their articles discuss topics such as data-driven solutions for enhancing renewable energy systems and harnessing technology for a greener energy future.
Environmental Protection Agency (EPA): The EPA is a dependable source for information on environmental concerns in the United States. Their research reports and databases offer valuable data that can be analyzed by data scientists to support evidence-based decision-making on environmental policies and regulations.
God Bless You,
James.
Updated
Nadia’s Answer
Hi Stella! You have a great instinct - data science is useful to nearly every field, although it may not be applied the same way in every company or organization. From the Coursera course AI for Everyone: "Data science is the science of extracting knowledge and insights from data." So you are going to need that in every single field. It will be up to each organization to use data science strategically & effectively. These days, with AI being part of the zeitgeist, more and more organizations are catching on to the need to make data-driven decisions. However, that doesn't mean you have to be in tech, software or other for-profit fields to use data science or even machine learning (which is a separate thing).
Good idea to look at renewable energy companies. There are many; I invest in NextEra, for example. You should research the organizational culture to see where you would be happy using sites like Glassdoor.
Best of luck!!
Good idea to look at renewable energy companies. There are many; I invest in NextEra, for example. You should research the organizational culture to see where you would be happy using sites like Glassdoor.
Best of luck!!
Updated
Jigar’s Answer
There are many opportunities for data scientists outside big tech or corporate companies maximizing profit. Some of the examples related to environment friendly areas are renewable energy, disaster predictions like earthquake & hurricane predictions, wildfire predictions, predicting energy management crisis avoiding power outages. One example can be ESG analyst that focuses on environment friendly applications. One way to transition to this field and learn about this field can be to start reading lots of case studies and research papers. More on the disaster prediction, You will have opportunities in the space of Disaster Prediction, (like predicting when/where) a disaster like a hurricane or wildfire, estimating how severe the damage from that could be thus helping minimize damage due to the disaster.
In general, one thing that is good in the data science space is that once you know the data science concepts and are good at it, you should be able to work on a variety of different problems. So you should be able to take your skills where-ever you like. So, you should also focus on maximizing your learning and acquiring mastery on the subject. The stronger basics you have, the more easily you can work in different areas.
In general, one thing that is good in the data science space is that once you know the data science concepts and are good at it, you should be able to work on a variety of different problems. So you should be able to take your skills where-ever you like. So, you should also focus on maximizing your learning and acquiring mastery on the subject. The stronger basics you have, the more easily you can work in different areas.
Thank you so much for your answer. Disaster prediction sounds like an interesting application that I hadn't thought of.
Stella
Updated
Jaskarn’s Answer
There are many opportunities for data scientists in a variety of industries. Some of the main areas where data scientists are in high demand include technology, finance, healthcare, and e-commerce. In these and other industries, data scientists are often responsible for collecting, cleaning, and organizing large sets of data, and then using their expertise in statistics and machine learning to extract insights and knowledge from the data. Data scientists may also be responsible for developing and deploying predictive models, creating data visualizations, and communicating their findings to stakeholders. As the field of data science continues to grow and evolve, the demand for qualified data scientists is expected to increase.
Updated
Rob’s Answer
I specialize in hiring for areas like Data & Cyber careers. The majority of roles now require some level of data analysis, and I am seeing a major trend for data science specializations, for example a Data Science specialty for environmental science and even more for any technical field. My nephew is pre-law and I have encouraged him to double major in Data Analytics. Data is so critical to every field and will definitely help you stand out from other graduates. Best of luck to you
Updated
Atul’s Answer
Data Scientists are in demand in the cybersecurity space where I worked for last 10 years before retiring.
Similarly, this skill set is needed for introducing new products whether it is a consumer products or pharmaceutical products.
BI and AI are critical part in today’s environment in any industry.
Similarly, this skill set is needed for introducing new products whether it is a consumer products or pharmaceutical products.
BI and AI are critical part in today’s environment in any industry.
Thanks for the help.
Stella
Updated
William’s Answer
Hi Stella!
These jobs are career options for a Data Scientist.
Data Analyst
Business Intelligence Developer
Cloud Infrastructure Architect
Statistician
Data Engineer
Data Scientist
Enterprise Architect
Applications Architect
Research Scientist
Actuarial Science
Machine Learning and AI
These jobs are career options for a Data Scientist.
Data Analyst
Business Intelligence Developer
Cloud Infrastructure Architect
Statistician
Data Engineer
Data Scientist
Enterprise Architect
Applications Architect
Research Scientist
Actuarial Science
Machine Learning and AI
Thank you for your help.
Stella