Object Oriented Programming in Data Science
What you'll learn
- Exactly the Object Oriented Programming needed for Data Science applications
- Build the data structures actually needed in practice
- Learn to use dataclasses effectively along with dictionaries
- Learn when to use private and protected variables
- Learn to use lambda functions with custom objects
- Learn how to use iterators with lists of custom objects
- Learn important decorators
- Learn when and why to use Inheritance and polymorphism in Data Science
- Learn the use of Metaclasses and Superclasses
- The subtitles are manually created. Therefore, they are fully accurate. They are not auto-generated.
- 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:
The course teaches that part of Object Oriented Programming that is actually needed in Data Science. And we learn how to use it and when.
We start with classes and dataclasses and we learn how and when to use dataclasses along with custom objects. We also go through lambda functions and use them with lists and dictionaries of custom objects.
We go through real-world examples and understand concepts such as metaclasses, superclasses, inheritance and polymorphism.
This is a necessary course for building efficient Data Science applications.
============== THE COURSE IS BEING UPDATED ============================
==============YOU MAY SEE SOME VIDEOS TAKEN DOWN FOR A WEEK OR SO =====
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:
- 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.