Python Programming: The Complete Course for Success

Name: Python Programming: The Complete Course for Success
Rating: 4.3 (336 reviews)

Become a Python Expert: Comprehensive Course Covering Fundamentals, Advanced Techniques & Practical Success Strategies

Created bySara Academy

Last updated 5/2025

English

What you'll learn

Overview of algorithms and their applications
Basic Python programming refresher
Operations on lists and tuples
Use cases and applications
Applications of stacks (e.g., backtracking, browser history)
Introduction to queues and types (FIFO, priority queues)
Implementing linked lists in Python
Binary trees, binary search trees, AVL trees
Basics of graph theory and types of graphs
Applications of hash tables
Bubble sort, selection sort, insertion sort
Merge sort, quicksort, heap sort
Implementing linear and binary search algorithms
Principles of dynamic programming
Heaps, tries, and segment trees
Implementing and using advanced data structures

Course content

7 sections • 30 lectures • 4h 59m total length

Definition and importance of data structures6:36
Explore the definition and importance of data structures in Python programming, focusing on organizing, storing, and retrieving data, and how time and space complexity affect efficiency and maintainability and readability.
Overview of algorithms and their applications7:36
Basic Python programming refresher8:25

Operations on lists and tuples7:07
Use cases and applications9:44
Understanding stack data structure6:46
Explore the stack data structure in Python by implementing a list-based stack and practicing push, pop, and peek operations, demonstrating last-in, first-out behavior and backtracking applications.
Implementing stacks using lists7:48
Applications of stacks (e.g., backtracking, browser history)15:13
Introduction to queues and types (FIFO, priority queues)7:30
Implementing queues using lists and deque7:17
For Loops and Range Function6:33

Implementing linked lists in Python14:58
Use cases and applications of linked lists7:28
Binary trees, binary search trees, AVL trees18:52
Tree traversal techniques (in-order, pre-order, post-order)10:03
Explore Python implementations of in-order, pre-order, and post-order tree traversals by visiting the left subtree, the root, and the right subtree; learn recursive traversal concepts and practical examples.
Basics of graph theory and types of graphs9:06
Graph traversal algorithms (BFS, DFS)10:36
Understanding hash functions and hash tables7:58
Explore hash functions and hash tables in Python, showing how key-value pairs map to values for fast lookups with dictionaries.
Applications of hash tables (e.g., caching, indexing)9:06

Divide and conquer, greedy algorithms, dynamic programming14:50
Learn divide and conquer through a merge sort example, apply greedy algorithms to coin change, and optimize Fibonacci with dynamic programming and memoization to avoid redundant calculations.
Bubble sort, selection sort, insertion sort11:04
Explore bubble sort, selection sort, and insertion sort in Python, with step-by-step demonstrations of swapping, selecting the smallest element, and inserting values to build a sorted array.
Merge sort, quicksort, heap sort17:47
Explore merge sort, quicksort, and heapsort with Python code and examples using divide-and-conquer, pivoting, and heap operations. Grasp their time complexities, including average O(n log n) and worst-case O(n^2).
Comparing the efficiency of different sorting algorithms11:18

Heaps, tries, and segment trees15:26
Explore heaps, tries, and segment trees in Python, including min-heap with heapq for fast inserts and pops, a prefix trie for word search, autocomplete, and spell check, and range queries.
Techniques to optimize algorithms for better performance7:30
Explore memoization as a dynamic programming technique to optimize Python algorithms, reduce redundant calculations, and improve performance, demonstrated by comparing non-memoized and memoized versions.
Memory and space optimization strategies10:03

Requirements

No prior programming experience is required.

Description

Unlock your full potential with Python Programming: The Complete Course for Success! Whether you're a beginner or looking to sharpen your skills, this comprehensive course will guide you through the fundamentals of Python, one of the most popular and versatile programming languages. Learn to write clean, efficient code from scratch and build practical projects that reinforce your knowledge.

What You'll Learn:

Python fundamentals: variables, data types, loops, and functions
Object-Oriented Programming (OOP) concepts
How to work with libraries like NumPy, Pandas, and Matplotlib
Web scraping, data analysis, and automation techniques
Best practices for debugging, testing, and writing efficient code

Why This Course?

This course is designed with real-world applications in mind. You’ll not only master Python syntax but also learn how to apply it in real-life scenarios like web development, data analysis, and automation. With hands-on exercises and interactive coding challenges, you’ll gain the confidence to tackle complex projects and prepare for your career in tech.

Step-by-step lessons, practical examples, and engaging challenges ensure you stay motivated throughout the learning process. By the end of this course, you’ll have a deep understanding of Python and the ability to solve problems with creativity and efficiency.

Start your journey to success in Python programming today!

Who this course is for:

Anyone interested in Python Programming Complete Course for Success
Anyone interested in automating tasks or diving into data analysis

Python Programming: The Complete Course for Success

What you'll learn

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Course content

Module 1: Introduction to Data Structures and Algorithms3 lectures • 23min

Module 2: Basic Data Structures in Python8 lectures • 1hr 8min

Module 3: Advanced Data Structures8 lectures • 1hr 28min

Module 4: Algorithmic Techniques and Sorting Algorithms4 lectures • 55min

Module 5: Searching Algorithms3 lectures • 27min

Module 6: Recursion and Dynamic Programming1 lecture • 6min

Module 7: Advanced Topics and Optimization Techniques3 lectures • 33min

Requirements

Description

Who this course is for: