Quant Finance Essentials

What you'll learn
The coding practices are focused on Matlab and on C++ (check promo video for all info )
Understand what Stochastic Optimization is, and specifically what scenarios are and what is "uncertainty" and sources of uncertainty.
Understand the different levels of Uncertainty
Implement Monte Carlo Simulations
Model the histograms of different distributions
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
=====THE COURSE IS RADICALLY UPDATED ==Who I am:I am a Research Fellow, leading the Research in industry projects in Mathematical Optimization & Data Science applied to Energy Investments, at Imperial College London.I have a PhD in Analytics and Mathematical Optimization, applied to Energy Investments, from Imperial College London.The old energy landscape is steadily being replaced by a new energy landscape that produces and consumes Big Data. To understand the new energy landscape we therefore need to adapt and make use of state-of-the-art Big Data algorithms and methods based on the latest advances on Data Science & Optimization.I offer specialized education and consultancy on Data Science, Optimization, Finance, all focused on energy investments.Make sure you sign up to be informed about my regular free webinars, to participate in quizzes, and to download publications and extra material.Important:No pre-requisites are needed: You do not have to know Programming (eg Python or MATLAB or C ++) at ALL because we go through all the commands needed , in great detail and with many examples. 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 as we explain everything in detail. Nothing for you to guess or search online because we go slowly, and fully explain what is shown on screen. If you are an experienced programmer, then you may find that the videos go very slowly. This is true because I break down every command, especially the complex ones. There are other online courses on Udemy that simply give the code, and give you an overall description of it, and then you have to figure out what it does. In my courses, we do the opposite: we go very slowly and examine every command. This is why some videos may be 30 minutes, and this is because we go in depth , and fully describe the code. In this course, there is NOTHING for you to search around on google because every line of code is explained in detail. So in the end of the course you will feel confident that you OWN everything that has been taught! For the contents of this course, please watch the promo video and also read the contents and the reviews. This course has received very high reviews, on a consistent basis. This course has also been designed based on interview material (banks, energy companies/ organisations, software engineering roles etc) so by the end of it, you will be fully covered and confident that you will do well.As you can read in my profile, I am Head of Research so I have extensive experience in this field. So you are in good hands. Good luck, and anything you need, I am and will be here to help you.
Overview
Section 1: Basic Maths
Lecture 1 Representing infinity
Lecture 2 Division / integer division / modulo
Section 2: Intro - Financial Data
Lecture 3 Download & Read Financial Data on Python
Lecture 4 Read data in Python from online sources
Section 3: Transforming Non-Normal datasets to Normal datasets on Python
Lecture 5 Python code for converting Lognormal datasets to Normal ones
Lecture 6 Proof on Python: Does log() convert any dataset to a normal one?
Lecture 7 Python code for converting any non-Normal dataset to Normal via Box-Cox
Section 4: MATLAB applications
Lecture 8 Uncertainty
Lecture 9 Construct scenarios & determine their probabilities
Lecture 10 Deep insight into Uniform Distribution with MATLAB & Python
Lecture 11 Normal Distribution interpretations
Lecture 12 Monte Carlo and simulated Probabilities
Section 5: C++ applications
Lecture 13 Installation and building basic program
Lecture 14 Probability evaluation using vectors
Lecture 15 Bernoulli process with biased trials (part 1)
Lecture 16 Bernoulli process with biased trials (part 2)
Lecture 17 Bernoulli process with biased trials (part 3)
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

Homepage

https://www.udemy.com/course/uncertaintymc/

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