Data Structures and Algorithms in Python from Udemy

Building reliable and highly performant software requires knowledge that goes beyond a certain programming language or framework. It requires a solid understanding of how data is organized in memory, how it can be manipulated, sorted or searched into. There’s a reason why all the big tech companies such as Google, Amazon or Netflix focus their technical interviews on those topics. Whether you do mobile apps, websites, games, machine learning or any other work that involves coding, you need a good grasp of Data Structures and Algorithms.

Many self taught developers and aspiring engineers often feel they lack the knowledge when having to decide on the right data structure or the right approach for solving a problem. If you ever felt that way, this material is the right choice for you. This course packs months of Computer Science subject matter to get you on the same level of proficiency as someone with a Computer Science degree.

What sets us apart ?

Every video begins with an in depth analysis of the topic at hand. At this stage we won’t write any code yet, but rather learn how to approach the problem, think of ways we could solve it and build a mental model of the solution.

We then go on to code the algorithm step by step. But we don’t stop there. We take one or more examples and walk through the code line by line. And we mean that literally. You will see how the code runs from top to bottom and how data flows and changes during execution. We find this method the absolute best way to really understand the inner workings of an algorithm.

We’ll also analyze the time performance and space utilization of every algorithm and method we write using the Big O Notation. We’ll talk about the strengths and weaknesses of each data structure and discuss their real world usage. Apart from all of that, you’ll also learn things like recursion, how computers work under the hood, problem solving techniques, common programming patterns and much more.

What does this course cover ?

How computers work under the hood
What a data structure is
What an algorithm is
Problem solving techniques
Big O Notation - how to analyze the time performance and space utilization of algorithms. This is done for every single function/algorithm we write.
Gain a deeper understanding of how code works
Recursion

Data Structures:

Arrays
Hash Tables
Singly Linked Lists
Doubly Linked Lists
Stacks
Queues
Binary Search Trees
Tree Traversal
Heaps
Graphs

Sorting Algorithms:

Insertion Sort
Selection Sort
Bubble Sort
Shell Sort
Heap Sort
Merge Sort
Quick Sort
Radix Sort

Thanks for considering, and I hope this course will help you in your journey. Happy learning.

What's inside

Learning objectives

Understand how computers store and work with data
Analyze the space and memory utilization of any algorithm using the big o notation
Dive deep into the mechanisms of function execution and demystify the concept of recursion
Implement from scratch the most common 9 data structures
Understand how the best known 8 sorting algorithms work

Learn the searching / traversing algorithms for trees and graphs
Improve your problem solving skills
Learn the behind the scenes of the basic data structures (arrays and hash maps)
Understand common programming patterns
Learn everything you need to know for the technical interview

Understand how computers store and work with data
Analyze the space and memory utilization of any algorithm using the big o notation
Dive deep into the mechanisms of function execution and demystify the concept of recursion
Implement from scratch the most common 9 data structures
Understand how the best known 8 sorting algorithms work
Learn the searching / traversing algorithms for trees and graphs
Improve your problem solving skills
Learn the behind the scenes of the basic data structures (arrays and hash maps)
Understand common programming patterns
Learn everything you need to know for the technical interview

Syllabus

Introduction

Course objectives, structure and more

How computers work

What are algorithms

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Provides a comprehensive foundation in data structures and algorithms, which are essential for building reliable and performant software

Taught by Glassbyte Team, who are recognized for their expertise in data structures and algorithms

Covers a wide range of data structures, including arrays, hash tables, linked lists, trees, and graphs, as well as sorting algorithms

Includes in-depth analysis of algorithms and their performance, using the Big O Notation

Offers hands-on practice through coding examples and step-by-step walkthroughs

Designed for self-taught developers and aspiring engineers who want to enhance their knowledge in data structures and algorithms

Reviews summary

In-depth python ds&a fundamentals

Students say this course offers a thorough and methodical approach to Data Structures and Algorithms in Python. It's particularly praised for its in-depth conceptual analysis before coding, helping to build a strong mental model. Learners value the line-by-line code walkthroughs, which clarify how data flows and changes during execution. The comprehensive Big O Notation analysis for every algorithm is a significant strength, equipping students to analyze performance. It's seen as a practical course for technical interviews, providing foundational knowledge akin to a Computer Science degree. While comprehensive, some may seek additional challenging practice exercises.

Pacing may vary in suitability for different learner levels.

"As a beginner, I found some sections moved a bit quickly, requiring me to rewatch videos to grasp the concepts fully."

"For someone with some prior programming experience, the pace felt just right and efficient for a quick review or learning new topics."

"The course assumes a basic understanding of Python, which is fine, but absolute beginners might need to supplement their Python knowledge."

Covers a wide array of fundamental data structures and algorithms.

"The course's comprehensive coverage of common data structures and sorting algorithms is excellent for building a strong foundation."

"I found the detailed sections on linked lists, trees, and graphs particularly useful and well-explained, covering all the essentials."

"It covered everything from basic arrays and hash tables to advanced sorting algorithms, making it a complete resource for DS&A."

Tailored for technical interviews and real-world application.

"This material directly addresses what I needed for technical interviews, covering all the essential DS&A topics."

"The focus on real-world usage of data structures helped me see their practical applications beyond theory."

"I gained valuable problem-solving techniques that are applicable not just to algorithms but to general coding challenges."

Thoroughly covers time and space complexity for all algorithms.

"Learning Big O Notation for every single function and algorithm was incredibly valuable for understanding performance implications."

"The course clearly explains how to analyze space and memory utilization, a crucial skill for writing efficient code."

"I now feel much more confident applying performance analysis techniques to my own algorithms, a huge benefit for interviews."

Provides meticulous, line-by-line code execution explanations.

"The line-by-line code walkthroughs are fantastic; I can literally see how the code runs and data changes, which solidifies understanding."

"I find the step-by-step coding and detailed examples to be the absolute best way to grasp complex algorithm inner workings."

"It was incredibly helpful to visualize how data flows and transforms during execution with the live code tracing."

Deep dives into concepts before moving to implementation.

"I really appreciate how the course breaks down complex topics with in-depth analysis before writing any code, helping me build a strong mental model."

"The instructor's method of explaining how to approach problems and think of solutions first is incredibly effective for understanding."

"It's great that they don't jump straight into coding; the pre-coding conceptual discussions are a massive plus for true understanding."

Could benefit from more challenging hands-on coding exercises.

"While the explanations are clear, I would have liked more hands-on coding challenges or exercises to solidify my understanding."

"I felt the need to seek additional practice problems outside the course to truly master some of the more complex concepts."

"More opportunities for self-paced coding assignments beyond just following the instructor's examples would enhance retention."

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 Data Structures and Algorithms in Python with these activities:

Master Insertion Sort

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Inserting a value into its correct position in a sorted list is a fundamental operation of sorting algorithms. This will familiarize you with the Insertion Sort algorithm in particular.

Browse courses on Insertion Sort

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Create an array of unordered numbers.
Pick an element in the array.
Compare the element to the ones on its left.
Shift the sorted elements to the right until the correct position is found.
Insert the element into the correct position.

Explain Recursion to Yourself

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Give yourself a foundation in recursion by creating content that explains its mechanism and how it relates to functions and the call stack.

Browse courses on Recursion

Show steps

Define recursion and explain how it works.
Explain the use of the call stack during recursive calls.
Illustrate recursion with a simple example, such as finding the factorial of a number.

Learn Binary Search Tree Traversal Algorithms

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BinaryTree traversal algorithms are key to the effective use of this data structure, as it helps you search, insert, and delete data. You will familiarize yourself with depth-first search and breadth-first search techniques.

Browse courses on Binary Search Tree

Show steps

Create a binary search tree with sample data.
Implement a depth-first search algorithm and test it on the tree.
Implement a breadth-first search algorithm and test it on the tree.
Compare the results and time complexity of both algorithms.

Show all three activities

Career center

Learners who complete Data Structures and Algorithms in Python will develop knowledge and skills that may be useful to these careers:

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Data Structures and Algorithms in Python

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What's inside

Learning objectives

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Reviews summary

In-depth python ds&a fundamentals

Activities

Career center

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