4 answers
Tonny
Student
Mombasa, Mombasa County, Kenya
1
Question
Asked
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How to start studying AI and data analytics as a marine biology graduate with limited knowledge in mathematics and computing ?
I would eventually like to merge my expertise in marine science with the ever evolving AI and data science sector, but I am basically a beginner at computing and mathematics.
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4 answers
Greg Fazekas
Technical Writer
15
Answers
Pécs, Hungary
Updated
Greg’s Answer
Great question, Tonny, and you're in luck! The world of AI is increasingly more open.
I recommend looking for some good beginner courses wherever you can find them (Udemy has a great selection) or books (HumbleBundle has a lot of data analytics bundles up for sale often).
But the best way to learn is by doing. Set up an environment and start using it. Look for how-tos and documentation on data analytics setups, and try them on your own. Think of problems you think could be solved and see if there are any answers or guides, then try to implement them. Approach from the problem, not from the perspective of wanting to learn, and you'll find actionable answers that will teach you something specific each time.
For AI, download Ollama and an open source LLM model or two, and you'll find setting it up and using it will create challenges and questions you can find the answer to, then more questions that'll lead to more answers.
Best of luck, this is an exciting field that is increasingly accessible!
I recommend looking for some good beginner courses wherever you can find them (Udemy has a great selection) or books (HumbleBundle has a lot of data analytics bundles up for sale often).
But the best way to learn is by doing. Set up an environment and start using it. Look for how-tos and documentation on data analytics setups, and try them on your own. Think of problems you think could be solved and see if there are any answers or guides, then try to implement them. Approach from the problem, not from the perspective of wanting to learn, and you'll find actionable answers that will teach you something specific each time.
For AI, download Ollama and an open source LLM model or two, and you'll find setting it up and using it will create challenges and questions you can find the answer to, then more questions that'll lead to more answers.
Best of luck, this is an exciting field that is increasingly accessible!
Martha Kramer
Corporate Training, Communications
480
Answers
Stamford, Connecticut
Updated
Martha’s Answer
What a great question, Tonny, and I am here to encourage you. My company, IBM, has terrific AI training that is free and even offers the opportunity to earn a badge - see link below. I think the same resource - IBM SkillsBuild - also offers training on data analytics. I believe other companies, such as Google, also offer free or low-cost training. In addition, online training companies, such as Coursera and edX, offer low-cost but informative courses, sometimes with certifications.
A key part of AI is assuring ample and accurate data that has minimal bias to train the AI. You use both the discipline you have learned as a marine biologist and perhaps even your content knowledge to build and monitor datasets in biology and evaluate AI search results. These are ongoing tasks that need human involvement.
I hope this helps and wish you good luck.
IBM free training - https://skillsbuild.org/students/course-catalog/artificial-intelligence
A key part of AI is assuring ample and accurate data that has minimal bias to train the AI. You use both the discipline you have learned as a marine biologist and perhaps even your content knowledge to build and monitor datasets in biology and evaluate AI search results. These are ongoing tasks that need human involvement.
I hope this helps and wish you good luck.
Martha recommends the following next steps:
Patrick Meyer
Executive - Data Analytics and AI
1058
Answers
Naperville, Illinois
Updated
Patrick’s Answer
Tonny, in my opinion as a marine biology graduate looking to transition into AI and data analytics, you're actually starting from a stronger position than you might realize. Your scientific background has already equipped you with critical thinking skills, research methodology, and domain expertise that are highly valuable in data science applications. The key is to build your technical foundation systematically while leveraging your marine science knowledge as a competitive advantage.
From my point of view you should begin with foundational mathematics by focusing on statistics and basic linear algebra, as these form the backbone of data analysis and machine learning. Online platforms like Khan Academy, Coursera, or edX offer excellent introductory courses that explain concepts in accessible language. Don't try to master advanced calculus immediately. Instead, concentrate on understanding descriptive statistics, probability, and basic matrix operations that you'll actually use in data science work. Many successful data scientists come from non-mathematical backgrounds and learn these concepts gradually through practical application.
Many of the individuals that I work with say that for computing skills, start with Python, which has become the standard language for data science and offers extensive libraries specifically designed for scientific research. Begin with basic programming concepts through beginner-friendly resources like Codecademy or Python.org's tutorial, then progress to data manipulation libraries like Pandas and NumPy. The beauty of Python is its readability and the vast ecosystem of pre-built tools that handle complex mathematical operations behind simple commands. Focus on learning to clean and analyze data before diving into machine learning algorithms.
Your marine biology background positions you perfectly for specialized applications where AI and data science are desperately needed. Ocean monitoring, species population analysis, climate change research, and marine ecosystem modeling all require professionals who understand both the scientific domain and analytical techniques. Consider starting with projects that analyze marine datasets. This approach allows you to learn technical skills while working with familiar subject matter. Organizations like NOAA, marine research institutions, and environmental consultancies increasingly seek professionals who can bridge the gap between marine science and data analytics.
This information is strictly from my point of view, but feel free to seek advice from others. Nonetheless, the transition will take time and consistent effort, but your scientific training gives you a significant advantage in understanding how to approach complex problems systematically. Focus on building skills gradually through hands-on projects rather than trying to master everything at once. Within 12-18 months of dedicated study, you can develop sufficient competency to begin applying these skills professionally in marine science contexts, eventually positioning yourself as a valuable specialist at the intersection of two critical fields.
From my point of view you should begin with foundational mathematics by focusing on statistics and basic linear algebra, as these form the backbone of data analysis and machine learning. Online platforms like Khan Academy, Coursera, or edX offer excellent introductory courses that explain concepts in accessible language. Don't try to master advanced calculus immediately. Instead, concentrate on understanding descriptive statistics, probability, and basic matrix operations that you'll actually use in data science work. Many successful data scientists come from non-mathematical backgrounds and learn these concepts gradually through practical application.
Many of the individuals that I work with say that for computing skills, start with Python, which has become the standard language for data science and offers extensive libraries specifically designed for scientific research. Begin with basic programming concepts through beginner-friendly resources like Codecademy or Python.org's tutorial, then progress to data manipulation libraries like Pandas and NumPy. The beauty of Python is its readability and the vast ecosystem of pre-built tools that handle complex mathematical operations behind simple commands. Focus on learning to clean and analyze data before diving into machine learning algorithms.
Your marine biology background positions you perfectly for specialized applications where AI and data science are desperately needed. Ocean monitoring, species population analysis, climate change research, and marine ecosystem modeling all require professionals who understand both the scientific domain and analytical techniques. Consider starting with projects that analyze marine datasets. This approach allows you to learn technical skills while working with familiar subject matter. Organizations like NOAA, marine research institutions, and environmental consultancies increasingly seek professionals who can bridge the gap between marine science and data analytics.
This information is strictly from my point of view, but feel free to seek advice from others. Nonetheless, the transition will take time and consistent effort, but your scientific training gives you a significant advantage in understanding how to approach complex problems systematically. Focus on building skills gradually through hands-on projects rather than trying to master everything at once. Within 12-18 months of dedicated study, you can develop sufficient competency to begin applying these skills professionally in marine science contexts, eventually positioning yourself as a valuable specialist at the intersection of two critical fields.
Sharven Rane
Data Scientist
15
Answers
Chicago, Illinois
Updated
Sharven’s Answer
The best way to start is by finding like-minded people already working at the intersection of marine science and AI/data science. Look at the kind of research or projects they’re doing follow them on LinkedIn, read their papers, check out GitHub and Kaggle profiles. Write down 10 exciting ideas where AI could support or enhance marine science. Then, search to see if anyone is already working on similar problems. Reach out, ask questions, and learn from them this early networking and research will shape your learning journey and give you direction.
AI, computing, and mathematics are vast fields, but you don’t need to master everything to begin. Start with one programming language Python is ideal and focus on learning the basics. In parallel, build your foundation in algebra, probability, and statistics. Skip expensive courses plenty of great free resources exist on YouTube, Coursera, freeCodeCamp, and Khan Academy.
Once you’re comfortable, return to your 10 ideas and build three variations for each simple, intermediate, and advanced. Over a year, that’s 30 projects focused on marine science and AI/data science a unique, niche portfolio. Document them, share your code, and create a basic portfolio site. Trust me, this will put you in the top 1% of candidates in this field.
One final tip: always choose projects that solve real-world problems ones that someone in marine science would actually pay for or use at scale. Start with 10 beginner-level projects to build confidence, but the remaining 20 should aim for quality and real-world impact. That’s how you’ll truly stand out.
Lock in you got this!
AI, computing, and mathematics are vast fields, but you don’t need to master everything to begin. Start with one programming language Python is ideal and focus on learning the basics. In parallel, build your foundation in algebra, probability, and statistics. Skip expensive courses plenty of great free resources exist on YouTube, Coursera, freeCodeCamp, and Khan Academy.
Once you’re comfortable, return to your 10 ideas and build three variations for each simple, intermediate, and advanced. Over a year, that’s 30 projects focused on marine science and AI/data science a unique, niche portfolio. Document them, share your code, and create a basic portfolio site. Trust me, this will put you in the top 1% of candidates in this field.
One final tip: always choose projects that solve real-world problems ones that someone in marine science would actually pay for or use at scale. Start with 10 beginner-level projects to build confidence, but the remaining 20 should aim for quality and real-world impact. That’s how you’ll truly stand out.
Lock in you got this!