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Algorithms and Data Structures in Python

A guide to implement the most up to date algorithms from scratch
4.5 (161 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
1,918 students enrolled
Created by Holczer Balazs
Last updated 2/2017
English
$10 $50 80% off
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Includes:
  • 12.5 hours on-demand video
  • 4 Articles
  • 4 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What Will I Learn?
Have a good grasp of algorithmic thinking
Be able to develop your own algorithms
Be able to detect and correct inefficient code snippets
View Curriculum
Requirements
  • Python basics
  • Some theoretical background ( big O notation )
Description

Hi!

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. The course takes approximately 4 hours to complete. 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, heaps and some advanced ones such as ternary search trees. The second part will be about graph algorithms. 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 LiClipse, 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.

Who is the target audience?
  • This course is suited for anyone who has some basic knowledge in python
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Curriculum For This Course
Expand All 124 Lectures Collapse All 124 Lectures 12:17:05
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Introduction
4 Lectures 10:03

Why to use data structures
03:54

Data structures and abstract data types
03:58

Complexity theory
00:05
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Setup
2 Lectures 07:01

Installing LiClipse
03:43
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Data Structures - Arrays
3 Lectures 23:02
Arrays introduction - basics
05:55

Arrays introduction - operations
05:53

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Data Structures - Linked Lists
8 Lectures 44:12

Linked list introduction - operations
09:32

Linked list theory - doubly linked list
01:40

Linked list introduction - linked lists versus arrays
06:36


Linked list implementation II - traverse
04:07

Linked list implementation III - remove
03:29

Linked list implementation IV - testing
04:43

Lists Quiz
3 questions
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Data Structures - Stacks & Queues
6 Lectures 35:43
Stack introduction
04:00

Stacks in memory management ( stacks, heaps )
07:23

Stacks and recursive method calls
07:02

Stack implementation
06:31

Queue introduction
05:12

Queue implementation
05:35
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Data Structures - Binary Search Trees
11 Lectures 58:15
Binary search trees theory - basics
10:23

Binary search trees theory - search, insert
04:25

Binary search trees theory - delete
06:08

Binary search trees theory - in-order traversal
04:25

Binary search trees theory - running times
02:10

Binary search tree implementation I - Node class
02:40

Binary Search Tree implementation II - insert
06:55

Binary Search Tree implementation III - traverse, min, max
06:11

Binary Search Tree implementation IV - testing insertion
02:30

Binary Search Tree implementation V - deletion
08:45

Binary Search Tree implementation VI - testing deletion
03:43

Binary Search Trees Quiz
3 questions
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Data Structures - Balanced Binary Trees
13 Lectures 01:22:23
AVL trees introduction - motivation
04:13

AVL trees introduction - basics
05:21

AVL trees introduction - height
08:44

AVL trees introduction - rotations cases
10:17

AVL trees introduction - illustration
10:50

AVL trees introduction - applications
03:38

AVL tree implementation I - Node
03:39

AVL tree implementation II - height, balance
04:55

AVL tree implementation III - rotations
06:43

AVL tree implementation IV - insertion
03:01

AVL tree implementation V - violations
07:31

AVL tree implementation VI - testing insertion
05:39

AVL tree implementation VII - remove
07:52

Balanced Trees Quiz
3 questions
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Red-Black Tree
8 Lectures 38:47
Red-black trees introduction - basics
10:38

Red-black trees rotations- cases I
05:10

Red-black trees rotations- cases II
04:25

Red-black trees rotations- cases III
03:17

Red-black trees rotations- cases IV
02:46

Red-black trees introduction - example I
04:47

Red-black trees introduction - example II
04:19

Red-black tree versus AVL tree
03:25
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Data Structures - Heaps
10 Lectures 57:12
Priority queues introduction
08:13

Heap introduction - basics
08:14

Heap introduction - array representation
09:17

Heap introduction - remove operation
04:43

Heap introduction - heapsort
05:13

Heap introduction - operations complexities
05:05

Other types of heaps: binomial and Fibonacci heap
02:59

Heap implementation I
04:05

Heap implementation II - heapsort
06:06

Heaps in Python
03:17

Heaps Quiz
3 questions
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Data Structures - Associative Arrays / Dictionaries
5 Lectures 27:31
Associative array ADT
02:37

Hashtable introduction - basics
09:04

Hashtable introduction - collisions
06:59

Hashtable introduction - dynamic resizing
06:03

Dictionaires in Python
02:48

Dictionaries Quiz
4 questions
7 More Sections
About the Instructor
4.4 Average rating
1,922 Reviews
20,913 Students
19 Courses
Software Engineer

Hi!

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.

Take a look at my website and join my email list if you are interested in these topics!

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