Published 9/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 485.88 MB | Duration: 1h 25m
Learn techniques used in real world Data Science
What you'll learn
This course includes video lectures, each of which focuses on a single, specific task on OOP that very often appears
Great for Quants/ Economists, Data Scientists/ Software Engineers: the skills shown here, come up all the time.
This is your Help Resource when you are under heavy pressure! No need to look at stackoverflow. Get job-ready skills.
You will not need to google-search to find answers. You will need to perform tasks, but you will already have known the skills required.
Simplify OOP , and learn only what is really needed for building Data Science programs
The subtitles are manually created so they are fully accurate. They are not auto-generated.
No pre-requisites. Out of all Python, you just need this knowledge. All yours.
Requirements
No experience required. You learn python by doing what is shown in the course.
Description
What is the course about:Object Oriented Programming is fundamental when creating new software for data science. You do not have to learn ALL the Data Science and ALL software engineering in order to write efficient programs on Python.Rather, try to learn only what is necessary - the commands that are in this course are really fundamental and keep appearing all the time. And keep practicing on them over and over again until you master them!And this course can be your encyclopedia - even at workplace. When you don't remember how something is done, you just resort to this course. Every video is a building block. Once you know these building blocks you can do anything with large programs on data science. You might have realized that Python and Data Science are like an ocean.. you can keep learning and learning, but in the end, at work, you will need to perform, as quickly as possible. And this comes down to knowing the skills , the techniques, taught in this online course! And Object Oriented Programming , as a difficult subject, needs to be approached in a careful way, otherwise it may create confusion.Who:I am a research fellow and I lead industry projects related to energy investments using mathematical optimisation and data science. Specialized in the Data Science aspect of the Green Energy transition, focused on algorithmic design and optimisation methods, using economic principles. Doctor of Philosophy (PhD) in Analytics & Mathematical Optimization applied to Energy Investments, from Imperial College London , and Master of Engineering (M. Eng.) degree in Power System Analysis (Electricity) and Economics . Important:No pre-requisites and no experience required.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 understand everything in detail.
Overview
Section 1: Classes and OOP
Lecture 1 Private & Protected variables
Section 2: Functions and OOP
Lecture 2 Lambdas & custom objects
Section 3: Lists and OOP
Lecture 3 How to unite/ concatenate lists of custom objects
Lecture 4 How to do list comprehension with custom objects
Lecture 5 How to read the next element of a list using next()
Section 4: Dictionaries and OOP
Lecture 6 Dictionary of dataclass objects
Lecture 7 From a list to a dictionary of custom objects
Lecture 8 How to use an Ordered Dictionary to group objects having a common attribute
Enterpreneurs & Quants,Software engineers,Data Scientists,Anyone preparing for interviews with software engineering component.,Anyone involved in Data Science projects
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