This course is about data structures and algorithms. We are going to implement the problems in Python, but I try to do it as generic as possible: so the core of the algorithms can be used in C++ or Java. I highly recommend typing out these data structures and algorithms several times on your own in order to get a good grasp of it.

In the first part of the course we are going to learn about basic data structures such as linked lists, stacks, queues, binary search trees, heaps and some advanced ones such as AVL trees and red-black trees.. The second part will be about graph algorithms such as spanning trees, shortest path algorithms and graph traversing. We will try to optimize each data structure as much as possible.

In each chapter I am going to talk about the theoretical background of each algorithm or data structure, then we are going to write the code step by step in Python.

Most of the advanced algorithms relies heavily on these topics so it is definitely worth understanding the basics. These principles can be used in several fields: in investment banking, artificial intelligence or electronic trading algorithms on the stock market. Research institutes use Python as a programming language in the main: there are a lot of library available for the public from machine learning to complex networks.

My name is Balazs Holczer. I am from Budapest, Hungary. I am qualified as a physicist and later on I decided to get a master degree in applied mathematics. At the moment I am working as a simulation engineer at a multinational company. I have been interested in **algorithms** and **data structures** and its implementations especially in Java since university. Later on I got acquainted with **machine learning** techniques, **artificial intelligence**, **numerical methods** and recipes such as solving differential equations, linear algebra, interpolation and extrapolation. These things may prove to be very very important in several fields: software engineering, research and development or investment banking. I have a special addiction to quantitative models such as the Black-Scholes model, or the Merton-model. Quantitative analysts use these algorithms and numerical techniques on daily basis so in my opinion these topics are definitely worth learning.

When does the course start and finish?

The course starts now and never ends! It is a completely self-paced online course - you decide when you start and when you finish.

How long do I have access to the course?

How does lifetime access sound? After enrolling, you have unlimited access to this course for as long as you like - across any and all devices you own.

What if I am unhappy with the course?

We would never want you to be unhappy! If you are unsatisfied with your purchase, contact us in the first 30 days and we will give you a full refund.