Algorithms and Data Structures in Python
What you'll learn
- Have a good grasp of algorithmic thinking
- Be able to develop your own algorithms
- Be able to detect and correct inefficient code snippets
Requirements
- Python basics
- Some theoretical background ( big O notation )
Description
This course is about data structures and algorithms. We are going to implement the problems in Python. I highly recommend typing out these data structures and algorithms several times on your own in order to get a good grasp of it.
Section 1:
setting up the environment
data structures and abstract data types
Section 2:
what is an array data structure
arrays related interview questions
linked list data structure and its implementation
Section 3:
stacks and queues
related interview questions
Section 4:
what are binary search trees
practical applications of binary search trees
Section 5:
problems with binary trees
balanced trees: AVL trees and red-black trees
Section 6:
what are heaps
heapsort algorithm
Section 7:
associative arrays and dictionaries
how to achieve O(1) constant running time with hashing
ternary search trees as associative arrays
Section 8:
basic graph algorithms
breadth-first and depth-first search
Section 9:
shortest path algorithms
Dijkstra's algroithm
Bellman-Ford algorithm
Section 10:
what are spanning trees
Kruskal algorithm
Section 11:
sorting algorithms
bubble sort, selection sort and insertion sort
quicksort and merge sort
non-comparison based sorting algorithms
counting sort and radix sort
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.
Thanks for joining the course, let's get started!
Who this course is for:
- This course is suited for anyone who has some basic knowledge in Python
Featured review
![David Read](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
Course content
- Preview02:21
- 00:11Complexity theory basics
Instructor
- 4.3 Instructor Rating
- 20,006 Reviews
- 163,508 Students
- 33 Courses
Hi!
My name is Balazs Holczer. I am from Budapest, Hungary. I am qualified as a physicist. 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.
Take a look at my website if you are interested in these topics!