Object Oriented Programming in Data Science

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Dr. Spyros Giannelos

1:24:56

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1 - Classes and OOP

1 - Private & Protected variables.mp4

14:43

2 - Functions and OOP

2 - Lambdas & custom objects.mp4

03:52

3 - Lists and OOP

3 - How to unite concatenate lists of custom objects.mp4

07:00

4 - How to do list comprehension with custom objects.mp4

10:19

5 - How to read the next element of a list using next.mp4

12:32

4 - Dictionaries and OOP

6 - Dictionary of dataclass objects.mp4

08:38

7 - From a list to a dictionary of custom objects.mp4

12:16

8 - How to use an Ordered Dictionary to group objects having a common attribute.mp4

15:36

Description

We are using Python, and explain OOP in Data Science in detail.

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

Who is this for?

Enterpreneurs

Economists.

Quants

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

What You Need to Know?

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".

More details

Description
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 =====

Who:
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.
Important:
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.Â Just follow what is shown on screen, because we go slowly and explain everything in detail.
Who this course is for:
Enterpreneurs
Economists.
Quants
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

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Dr. Spyros Giannelos, is a Research Scientist, leading energy projects using Mathematical Optimization & Data Science. Specifically such projects have been around energy investments with a focus on electricity. He holds a Doctor of Philosophy (Ph.D.) in Analytics & Mathematical Optimization from Imperial College London. His research interests include energy investments, optimization, data science, machine learning and quantitative finance.

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