Military Geopolitics using Data Science
What you'll learn
- Use Data Science for Military & Intelligence
- Gain insights into Military analyses with Python
- Conduct analytical Population analyses using Data Science
- Gain insights into military expenditure on a global scale
- Analyse a long-term geopolitical scenario
- Use Data Science for global military alliances
- Part of the giannelos dot com official certificate
- The only prerequisite is to take the first course of the "giannelos dot com" program , which is the course "Data Science Code that appears all the time at workplace".
What is the course about:
Every country and every alliance (e.g. NATO, SCO, CSTO, etc ) has designed possible geopolitical scenarios that may play out in the next 5 years (short-term), 15 years (mid-term), and 50 - 100 years (long-term). Examples of alliances/organizations include the SCO (Shanghai Cooperation Organisation), the CSTO (Collective Security Treaty Organization), NATO, the European Union, etc, each of which has a number of geopolitical scenarios in place in order to be in a state of readiness for future outcomes.
In this course, we show how Data Science is used in real-world Geopolitical, Military, and Intelligence Scenarios. We begin with a complete tutorial on geolocation, which is widely used for military applications. Then, we move on to alliances, such as NATO and SCO, and apply Data Science principles to gain greater insights.
We also conduct military expenditure analyses as well as population analyses. Also, this is the ONLY course online, and the first time that a course is dealing with the analysis of a long-term geopolitical military scenario.
Learn how to apply Data Science to military geopolitics!
I am a research fellow at Imperial College London, and I have been part of high-tech projects at the intersection of Academia & Industry for over 10 years, prior to, during & after my Ph.D. I am also the founder of the giannelos dot com program in data science.
Doctor of Philosophy (Ph.D.) in Analytics & Mathematical Optimization applied to Energy Investments, from Imperial College London, and Masters of Engineering (M. Eng.) in Power Systems and Economics.
Prerequisites: The course Data Science Code that appears all the time at Workplace.
Every detail is explained, so that you won't have to search online, or guess. In the end, you will feel confident in your knowledge and skills.
We start from scratch so that you do not need to have done any preparatory work in advance at all. Just follow what is shown on screen, because we go slowly and explain everything in detail.
Who this course is for:
- Working or interested in Military (planning)
- Working or interested in Intelligence (planning)
- Risk management (geopolitical risk / macro) and investments
- Members of the highly googled giannelos dot com program
- Investment Bankers
- Academics, PhD Students, MSc Students, Undergrads
- Postgraduate and PhD students.
- Data Scientists
- Energy professionals (investment planning, power system analysis)
- Software Engineers
- Finance professionals
Dr. Giannelos is a Research Scientist at Imperial College London leading Mathematical Optimization & Data Science projects at the intersection of Energy, Finance, and Data Science (Optimization, Machine Learning). He holds a Doctor of Philosophy (Ph.D.) in Mathematical Optimization applied to Energy Investments and Economics, from Imperial College London. He is also the founder of the research-scientist dot com program.