Software Design For Data Science

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.

Requirements

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".

Description

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.  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.  Just follow what is shown on screen, because we go slowly and explain everything in detail.

Overview

Section 1: Introduction

Lecture 1 Overview of course contents

Lecture 2 Analysis on principles

Section 2: Details about running Python models

Lecture 3 The most important command: if __name __ = main() (part1)

Lecture 4 The most important command: if __name__==main() (part2)

Lecture 5 Why Jupyter Notebook isn't really good for large models

Section 3: Python Loggers and Annotation

Lecture 6 Logging module: Detailed analysis of how to create a logger.

Lecture 7 The Logger in action. Passing it over to files/classes/functions

Lecture 8 Adding graphics inside logging messages, running special cases, & theory

Lecture 9 How Python hints & annotations are used

Lecture 10 Python annotations and custom types, step-by-step

Lecture 11 Function annotations. Optional parameters. Union, Optional, Any, Sequence

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

Lecture 13 Always conduct Static Code Analysis. Which Python Checker to use?

Section 4: The Single Responsibility Principle for Efficient Software Design

Lecture 14 The Single Responsibility Principle ( Part I)

Lecture 15 The Single Responsibility Principle (Part II)

Lecture 16 The Single Responsibility Principle (Part III)

Lecture 17 The Single Responsibility Principle (Part IV)

Section 5: Bonus

Lecture 18 Extras

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