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Introducing Algorithms in Python

Name: Introducing Algorithms in Python Course
Price: 36 USD

Explore essential algorithms in Python! Learn searching (Linear & Binary), sorting (Bubble, Insertion, Merge, Quick), and complexity analysis (Big O notation). Understand efficiency, recognize trade-offs, and build a strong foundation for developing optimized algorithms in data science, software development, and coding interviews.

4.8

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671 students already enrolled

Skill level:

Basic

Duration:

2 hours

Lessons (2 hours)

CPE credits:

3

CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of professionals must earn to maintain their licenses and stay current with regulations and best practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's official website: www.nasbaregistry.org

Accredited

certificate

What you learn

Understand algorithms and pseudocode, including purpose and structure.
Master linear and binary search, knowing when each is most efficient.
Analyze algorithm complexity using Big‑O notation for performance comparison.
Implement Bubble, Insertion, Merge, and Quick Sort.
Identify the correct algorithm for a task based on efficiency and context.

Topics & tools

PythonAlgorithmsProblem SolvingAlgorithms ComplexitySearch AlgorithmsSorting AlgorithmsBig O-NotationProgramming

Your instructor

Muhammad Ateeq

Lecturer | Data Science | Ph.D.

Track record

Bringing real-world expertise from leading global companies

Academic background

Doctorate (PhD), Computer Science

Media and recognition

PhD in Computer Science, COMSATS Institute of IT
Published in Elsevier's Computer Communications and IEEE Internet Computing

Ask Muhammad a question

Course overview

Accredited certificates

Course OVERVIEW

Description

CPE Credits: 3 Field of Study: Information Technology
Delivery Method: QAS Self Study

Programming has two essential parts: devising logic (algorithms) to solve problems using relevant data (data structures). In data science, efficient algorithms and appropriate data structures are crucial for effectively processing and analyzing large datasets. This Introducing Algorithms in Python course bridges these fundamental concepts of algorithms.

In this Algorithms in Python course, you'll start by immersing yourself in the world of algorithms. You’ll learn how to think critically and develop efficient solutions through essential searching techniques like linear search and binary search, as well as fundamental Python sorting algorithms—including bubble sort, insertion sort, merge sort, and quick sort. Implementing these algorithms in Python will enhance your coding proficiency and problem-solving skills, which are vital in data science.

By the end of our Introducing Algorithms in Python course, you'll have a solid foundation to tackle data science projects more effectively—equipped with the skills to write efficient code to handle varying data proficiently.

Are you an aspiring data scientist, programmer, or someone looking to strengthen your fundamentals? This Algorithms in Python course provides the knowledge to advance your skills and confidence in recognizing and developing efficient solutions and applications.

Start mastering essential algorithms in this Introducing Algorithms in Python Course today!

Prerequisites

Python (any recent version, such as Python 3.8 or later) and a code editor or IDE (e.g., Spyder, VS Code, or Jupyter Notebook)
Completion of an introductory Python course is recommended.

Advanced preparation

Introduction to Python

Curriculum

29 lessons 25 exercises 1 exam

1. Intro to Algorithms

7 min
- Understand what algorithms are, why they are fundamental in computing, and how they solve problems.
- Learn to write pseudocode to express logic clearly and systematically.
- Develop a structured problem-solving mindset applicable across programming and real-world challenges.
7 min
- Understand what algorithms are, why they are fundamental in computing, and how they solve problems.
- Learn to write pseudocode to express logic clearly and systematically.
- Develop a structured problem-solving mindset applicable across programming and real-world challenges.
Welcome to the Course Free

What Are Algorithms? Free

Exercise Free

Algorithms vs. Code Free

Coding exercise

Summary Free
2. Search Algorithms

17 min
- Master Linear and Binary Search, understanding their strengths and limitations.
- Gain an intuitive grasp of efficiency—how different search strategies perform as data grows.
- Learn to choose the best search approach for different types of datasets and applications.
17 min
- Master Linear and Binary Search, understanding their strengths and limitations.
- Gain an intuitive grasp of efficiency—how different search strategies perform as data grows.
- Learn to choose the best search approach for different types of datasets and applications.
Introduction

Linear Search

Exercise

Linear Search Implementation

Coding exercise

Analyzing Linear Search

Exercise

Coding exercise

Sorted Data and Binary Search

Exercise

Binary Search Implementation

Coding exercise

Analyzing Binary Search

Exercise

Summary: Search Algorithms
3. Analyzing Algorithm Complexity

15 min
- Understand how input size affects performance and why some algorithms scale better than others.
- Explore the growth of functions and how they influence execution time.
- Learn Big-O notation intuitively, using real-world analogies and comparisons.
15 min
- Understand how input size affects performance and why some algorithms scale better than others.
- Explore the growth of functions and how they influence execution time.
- Learn Big-O notation intuitively, using real-world analogies and comparisons.
Introduction to analyzing algorithm complexity

Exercise

Formalizing Big O Notation

Best, Average, and Worst Case

Exercise

Fundamental Complexity Classes

Exercise

Summary - Analyzing algorithm complexity
4. Sorting Algorithms - I

27 min
- Discover why sorting is fundamental for organizing, retrieving, and optimizing data.
- Learn how Bubble Sort and Insertion Sort work step by step.
- Compare their efficiency and understand where they are practical despite being slow.
27 min
- Discover why sorting is fundamental for organizing, retrieving, and optimizing data.
- Learn how Bubble Sort and Insertion Sort work step by step.
- Compare their efficiency and understand where they are practical despite being slow.
Introduction - Sorting Algorithms - I

Naive Sorting: Bubble Sort

Exercise

Coding exercise

Visualizing Complexity Growth: From Constant to Quadratic

Naive Sorting: Insertion Sort

Exercise

Coding exercise

Summary - Sorting Algorithms - I
5. Sorting Algorithms - II

38 min
- Explore recursion as an alternative to iteration, forming the foundation for efficient sorting.
- Master Merge Sort and Quick Sort, understanding their divide-and-conquer strategies.
- Learn when to use Merge Sort for stability and Quick Sort for speed and in-place efficiency.
38 min
- Explore recursion as an alternative to iteration, forming the foundation for efficient sorting.
- Master Merge Sort and Quick Sort, understanding their divide-and-conquer strategies.
- Learn when to use Merge Sort for stability and Quick Sort for speed and in-place efficiency.
Introduction - Sorting algorithms - II

Exercise

Merge Sort

Complexity of Merge Sort

Exercise

Coding exercise

Quick Sort

Complexity of Quick Sort

Exercise

Coding exercise

Summary - Sorting algorithms - II

You Did It!
6. Course exam

35 min

35 min

Course exam

Free lessons

1.1 Welcome to the Course

3 min

1.2 What Are Algorithms?

2 min

1.4 Algorithms vs. Code

1 min

1.6 Summary

1 min

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