Software Design for Data Science

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Dr. Spyros Giannelos

4:07:51

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1 - Introduction

1 - Overview of course contents.mp4

00:37

2 - Analysis on principles.mp4

03:08

2 - Details about running Python models

3 - 1-the-most-important-command-part1.rar

3 - The most important command if name main part1.mp4

16:49

4 - 2-the-most-important-command-part2.rar

4 - The most important command if namemain part2.mp4

20:44

4 - analysis.zip

5 - 3-why-jupyter-isnt-best-for-large-models.rar

5 - Why Jupyter Notebook isnt really good for large models.mp4

10:10

3 - Python Loggers and Annotation

6 - Logging module Detailed analysis of how to create a logger.mp4

15:29

6 - analysis.zip

6 - logger.rar

7 - The Logger in action Passing it over to filesclassesfunctions.mp4

19:31

7 - my-learning.rar

8 - Adding graphics inside logging messages running special cases theory.mp4

26:46

8 - detailed logging documentation.zip

8 - extra logging documentation.zip

8 - extra unicode symbols.zip

8 - logging handlers.zip

8 - new unicode symbols.zip

8 - unicode graphics.zip

8 - with-logging-module.rar

9 - How Python hints annotations are used.mp4

13:02

9 - extra resource on type hints.zip

9 - the package needed for annotations.zip

10 - Python annotations and custom types stepbystep.mp4

22:38

10 - analysis.zip

11 - Function annotations Optional parameters Union Optional Any Sequence.mp4

25:26

11 - resources on the annotation type any.zip

11 - resources on the annotation type union.zip

12 - Using Callable and Generic Types Calling staticmethods using custom types.mp4

16:01

13 - Always conduct Static Code Analysis Which Python Checker to use.mp4

13:50

4 - The Single Responsibility Principle for Efficient Software Design

14 - The Single Responsibility Principle Part I.mp4

22:21

14 - analysis.zip

14 - part-1.rar

15 - The Single Responsibility Principle Part II.mp4

05:51

15 - part-2.rar

16 - The Single Responsibility Principle Part III.mp4

09:34

16 - part-3.rar

17 - The Single Responsibility Principle Part IV.mp4

05:54

17 - part-4.rar

5 - Bonus

18 - Discount-Coupons-october-2022.pdf

18 - Extras.html

18 - my personal website with great offers for you.zip

Description

Fundamental Programming principles for Developing Data Analysis applications

What You'll Learn?

Learn how to structure the code for writing Data Science applications
Gain confidence in writing efficient code
Learn the fundamental software design principles for Data Science
Learn how to use custom Annotations, and which ones to use
Build your own Annotations and place them exactly at the best places in the code
Develop your own logger and configure it in the optimal way
Part of the giannelos dot com official certificate for high-tech projects.

Who is this for?

Entrepreneurs

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

More details

Description

===================================================

The course content , which is very old, is radically updated!

Original course content was 10 hours. Now it has been reduced to 2 hours.

Currently it is being updated.

Please bear with me, as IÂ am updating the course content at my limited free time .

I offer refunds

=====================================================

What is the course about:
This online course teaches you how to actually write code for developing Data Science Software.
The principles of software design depend on the program we have in mind. If we aim for Data Science applications then the Software Design must apply a different set of principles than if we aim for developing software for web applications.
Software Design for Data Science needs to be able to handle the data structures encountered in Data Science.
This course goes through the most important and fundamental practices of Software Design used in practice and explains them using intuitive examples, to ensure you truly comprehend the material.

Â 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:
Entrepreneurs
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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