Algorithms and Data Structures in Python (INTERVIEW Q&A) from Udemy

This course is about data structures, algorithms and graphs. We are going to implement the problems in Python programming language. I highly recommend typing out these data structures and algorithms several times on your own in order to get a good grasp of it.

So what are you going to learn in this course?

Section 1:

setting up the environment
differences between data structures and abstract data types

Section 2 - Arrays:

what is an array data structure
arrays related interview questions

Section 3 - Linked Lists:

linked list data structure and its implementation
doubly linked lists
linked lists related interview questions

Section 4 - Stacks and Queues:

stacks and queues
stack memory and heap memory
how the stack memory works exactly?
stacks and queues related interview questions

Section 5 - Binary Search Trees:

what are binary search trees
practical applications of binary search trees
problems with binary trees

Section 6 - Balanced Binary Trees (AVL Trees and Red-Black Trees):

why to use balanced binary search trees
AVL trees
red-black trees

Section 7 - Priority Queues and Heaps:

what are priority queues
what are heaps
heapsort algorithm overview

Section 8 - Hashing and Dictionaries:

associative arrays and dictionaries
how to achieve O(1) constant running time with hashing

Section 9 - Graph Traversal:

basic graph algorithms
breadth-first
depth-first search
stack memory visualization for DFS

Section 10 - Shortest Path problems (Dijkstra's and Bellman-Ford Algorithms):

shortest path algorithms
Dijkstra's algorithm
Bellman-Ford algorithm
how to detect arbitrage opportunities on the FOREX?

Section 11 - Spanning Trees (Kruskal's and Prim's Approaches):

what are spanning trees
what is the union-find data structure and how to use it
Kruskal's algorithm theory and implementation as well
Prim's algorithm

Section 12 - Substring Search Algorithms

what are substring search algorithms and why are they important in real world softwares
brute-force substring search algorithm
hashing and Rabin-Karp method
Knuth-Morris-Pratt substring search algorithm
Z substring search algorithm (Z algorithm)
implementations in Python

Section 13 - Hamiltonian Cycles (Travelling Salesman Problem)

Hamiltonian cycles in graphs
what is the travelling salesman problem?
how to use backtracking to solve the problem
meta-heuristic approaches to boost algorithms

Section 14 - Sorting Algorithms

sorting algorithms
bubble sort, selection sort and insertion sort
quicksort and merge sort
non-comparison based sorting algorithms
counting sort and radix sort

Section 15 - Algorithms Analysis

how to measure the running time of algorithms
running time analysis with big O (ordo), big Ω (omega) and big θ (theta) notations
complexity classes
polynomial (P) and non-deterministic polynomial (NP) algorithms
O(1), O(logN), O(N) and several other running time complexities

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.

What's inside

Learning objectives

Understand arrays and linked lists
Understand stacks and queues
Understand tree like data structures (binary search trees)
Understand balances trees (avl trees and red-black trees)
Understand heap data structures
Understand hashing, hash tables and dictionaries
Understand the differences between data structures and abstract data types
Understand graph traversing (bfs and dfs)

Understand shortest path algorithms such as dijkstra's approach or bellman-ford method
Understand minimum spanning trees (prims's algorithm)
Understand sorting algorithms
Be able to develop your own algorithms
Have a good grasp of algorithmic thinking
Be able to detect and correct inefficient code snippets
Show more
Show less

Understand arrays and linked lists
Understand stacks and queues
Understand tree like data structures (binary search trees)
Understand balances trees (avl trees and red-black trees)
Understand heap data structures
Understand hashing, hash tables and dictionaries
Understand the differences between data structures and abstract data types
Understand graph traversing (bfs and dfs)
Understand shortest path algorithms such as dijkstra's approach or bellman-ford method
Understand minimum spanning trees (prims's algorithm)
Understand sorting algorithms
Be able to develop your own algorithms
Have a good grasp of algorithmic thinking
Be able to detect and correct inefficient code snippets
Show more
Show less

Syllabus

Introduction

Complexity theory basics

Installation and Environment Setup

Installing Python

Installing PyCharm

### DATA STRUCTURES ###

Why do we need data structures?

Data structures and abstract data types

Data Structures and Abstract Data Types Quiz

Data Structures - Arrays

What are array data structures?

What are lists in Python?

Arrays introduction - operations

Lists in Python

Lists in Python - advanced operations

Lists in Python - list comprehension

Arrays and Lists Quiz

Interview Questions - (Arrays)

Reversing an array in-place exercise

Reversing an array in-place solution

Palindrome exercise

Palindrome problem solution

Integer reversion exercise

Integer reversion problem solution

Anagram problem exercise

Anagram problem solution

Data Structures - Linked Lists

What are linked lists?

Linked list introduction - operations

Pros and cons of linked lists

Linked list implementation I

Linked list implementation II

Linked list implementation III

Revisiting remove operation

Comparing linked lists and arrays

Practical (real-world) applications of linked lists

Linked Lists Quiz

Data Structures - Doubly Linked Lists

What are doubly linked lists?

Doubly linked list implementation

Running time comparison: linked lists and arrays

Doubly Linked Lists Quiz

Interview Questions (Linked Lists)

Finding the middle node in a linked list exercise

Finding the middle node in a linked list solution

Reverse a linked list in-place exercise

Reverse a linked list in-place solution

Data Structures - Stacks

What are stacks?

Stacks in memory management (stacks and heaps)

Stack memory visualization

Stack implementation

Practical (real-world) applications of stacks

Stack Quiz

Data Structures - Queues

What are queues?

Queue implementation

Queues Quiz

Interview Questions (Stacks and Queues)

Max in a stack problem overview

Max in a stack problem solution

Queue with stack problem

Queue with stack problem solution

Queue with stack problem solution - recursion

Data Structures - Binary Search Trees

What are binary search trees?

Binary search trees theory - search, insert

Binary search trees theory - delete

Binary search trees theory - in-order traversal

Pros and cons of binary search trees

Binary search tree implementation I

Binary search tree implementation II

Stack memory visualization - finding max (min) items

Stack memory visualization - tree traversal

Binary search tree implementation III - remove operation

Practical (real-world) applications of trees

Binary Search Trees Quiz

Interview Questions (Binary Search Trees)

Compare binary trees exercise

Compare binary trees solution

Data Structures - AVL Trees

Motivation behind balanced binary search trees

What are AVL trees?

AVL trees introduction - height

AVL trees introduction - rotations

AVL trees introduction - illustration

AVL tree implementation I

AVL tree implementation II

AVL tree implementation III

AVL tree implementation IV

AVL tree implementation V

Practical (real-world) applications of balanced binary search trees

AVL Trees Quiz

Data Structures - Red-Black Trees

What are red-black trees?

The logic behind red-black trees

Red-black trees - recoloring and rotation cases

Red-black tree illustrations

Red-black tree implementation I

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Covers core fundamentals of data structures and algorithms, essential building blocks for software engineering

Emphasizes hands-on coding in Python, widely used in industry and research

In-depth exploration of binary search trees, AVL trees, and red-black trees, critical for optimizing data storage and retrieval

Introduces graph traversal algorithms, graph search techniques, and their applications in real-world scenarios

Covers sorting algorithms, a fundamental skill in computer science and software development

Suitable for beginners and intermediate learners with some programming experience

Reviews summary

Great course for interview prep

Learners say this course is a great resource for preparing for coding interviews. It provides clear explanations, a good balance of theory and practice, and engaging quizzes. Students find the content to be well-structured and not too difficult.

Engaging quizzes.

"It also has quizzes"

Good course for interview prep.

"Very clear, meaningful explanation."

"Not hard, not easy."

"Nicely structured: theory-practice-examples."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Algorithms and Data Structures in Python (INTERVIEW Q&A) with these activities:

Review Python fundamentals

Show steps

Refresh and strengthen basic Python skills to ensure a solid foundation for the course.

Browse courses on Python

Show steps

Review basic syntax, data types, and control flow.
Complete coding exercises or practice problems.
Familiarize yourself with Python libraries related to the course.

Introduction to Algorithms

Show steps

Gain a deeper understanding of fundamental algorithms and their applications by reviewing a classic textbook.

Show steps

Read the selected chapters relevant to course topics.
Solve practice problems and exercises.
Discuss concepts and findings with peers or the instructor.

Seek guidance from experienced programmers

Show steps

Connect with experienced individuals who can provide valuable insights, support, and guidance throughout your learning journey.

Browse courses on Mentorship

Show steps

Identify potential mentors through online platforms, local meetups, or personal networks.
Reach out to mentors with a clear request.
定期交流，讨论相关主题，并寻求反馈。

Five other activities

Expand to see all activities and additional details

Show all eight activities

Complete practice problems on arrays

Show steps

Strengthen understanding of fundamental array operations and data structure concepts.

Browse courses on Arrays

Show steps

Identify an online platform or textbook with practice problems.
Solve at least 25 practice problems.
Review solutions and identify areas for improvement.

Explore graph algorithms using NetworkX

Show steps

Enhance understanding of graph algorithms and their practical applications through guided tutorials and hands-on exercises.

Browse courses on Graph Algorithms

Show steps

熟悉NetworkX库的基本功能.
Implement graph traversal algorithms (BFS/DFS).
Analyze and visualize the results of graph algorithms.

Create and traverse binary search trees

Show steps

Develop proficiency in implementing and manipulating binary search trees for efficient searching and retrieval.

Browse courses on Binary Search Trees

Show steps

Implement a binary search tree in Python.
Insert, search, and delete elements from the tree.
Analyze the performance of the tree for different input data sets.

Build a simple Python program using data structures

Show steps

Apply data structure concepts to a practical project, solidifying understanding and fostering problem-solving skills.

Browse courses on Data Structures

Show steps

Identify a small project idea that involves data structures.
Design the data structures and algorithms to be used.
Implement the program in Python and test its functionality.

Design and implement a priority queue

Show steps

Gain hands-on experience in building and utilizing priority queues, a critical data structure for real-world applications.

Browse courses on Priority Queues

Show steps

Understand the concept of priority queues and their applications.
Implement a priority queue using a heap data structure.
Test the priority queue with various input data and analyze its performance.

Career center

Learners who complete Algorithms and Data Structures in Python (INTERVIEW Q&A) will develop knowledge and skills that may be useful to these careers:

Algorithmic Trader

Algorithmic traders design, implement, and monitor computer programs that execute a series of trading orders based on predetermined rules or algorithms. The goal of using these algorithms is to trade stocks, bonds, or other financial instruments in an automated and profitable way. This course can be particularly helpful for algorithmic traders interested in improving their understanding of data structures and algorithms, as these concepts are essential for developing effective trading strategies. The course also covers graph algorithms, which can be useful for analyzing market trends and identifying trading opportunities.

See salaries and explore the career path for Algorithmic Trader

Data Engineer

Data engineers design, build, and maintain the infrastructure that stores and processes data. They work with data analysts and other stakeholders to ensure that data is accessible and usable for analysis.

See salaries and explore the career path for Data Engineer

Data Scientist

Data scientists use a variety of techniques to extract insights from data. They work with data engineers and other stakeholders to develop machine learning models and other predictive analytics solutions.

See salaries and explore the career path for Data Scientist

Data Analyst

Data analysts collect, clean, and analyze data to help businesses make informed decisions. They use a variety of statistical and programming techniques to identify trends and patterns in data.

See salaries and explore the career path for Data Analyst

Quantitative Analyst

Quantitative analysts use mathematical and statistical models to analyze financial data and make investment decisions. They work with portfolio managers and other stakeholders to develop trading strategies and manage risk.

See salaries and explore the career path for Quantitative Analyst

Investment Analyst

Investment analysts evaluate the financial performance of companies and make recommendations about whether to buy, sell, or hold their stocks. They use a variety of financial models and analytical techniques to assess a company's financial health and prospects.

See salaries and explore the career path for Investment Analyst

Research Analyst

Research analysts conduct research on companies and industries to provide insights to investors. They use a variety of financial and economic models to analyze company performance and make recommendations about whether to buy, sell, or hold stocks.

See salaries and explore the career path for Research Analyst

Statistician

Statisticians collect, analyze, and interpret data to help businesses make informed decisions. They use a variety of statistical techniques to identify trends and patterns in data.

See salaries and explore the career path for Statistician

Software Developer

Software developers design, build, and maintain software applications. They work with a variety of programming languages and technologies to create software that meets the needs of users.

See salaries and explore the career path for Software Developer

Technical Analyst

Technical analysts use charts and other technical indicators to analyze market trends and identify trading opportunities. They work with portfolio managers and other stakeholders to develop trading strategies and manage risk.

See salaries and explore the career path for Technical Analyst

Teacher

Teachers plan and deliver instruction to students in a variety of settings. They use a variety of teaching methods and materials to help students learn.

See salaries and explore the career path for Teacher

Database Administrator

Database administrators design, build, and maintain databases. They work with a variety of database technologies to ensure that data is stored and accessed efficiently.

See salaries and explore the career path for Database Administrator

Computer Scientist

Computer scientists research and develop new computer technologies and applications. They work with a variety of programming languages and technologies to create new and innovative software solutions.

See salaries and explore the career path for Computer Scientist

Web Developer

Web developers design and develop websites. They work with a variety of programming languages and technologies to create websites that are user-friendly and meet the needs of users.

See salaries and explore the career path for Web Developer

Data Architect

Data architects design and build the infrastructure that stores and processes data. They work with data engineers and other stakeholders to ensure that data is accessible and usable for analysis.

See salaries and explore the career path for Data Architect