Emad S.
Before 365:
Business Analyst | Snapp!
After 365:
Senior Data Analyst | RBS Global Asset Management
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Online Course
new
Introducing Algorithms in Python
with
Muhammad Ateeq
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.
339 students already have enrolled
Skill level:
Basic
Duration:
2 hours
-
Lessons (97 hours)
- Lessons (97 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
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
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
Course OVERVIEW
Description
CPE Credits: 3
Field of Study: Information Technology
Delivery Method: QAS Self Study
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
Curriculum
29 lessons
25 exercises
1 exam
- 2. Search Algorithms17 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.
- 3. Analyzing Algorithm Complexity15 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.
- 4. Sorting Algorithms - I27 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.
- 5. Sorting Algorithms - II38 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.
- 6. Course exam35 Min
Free preview
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
9 in 10
of our graduates landed a new AI & data job
after enrollment
94%
of AI and data science graduates
successfully change
or advance their careers.
$29,000
average salary increase
after moving to an AI and data science career
ACCREDITED certificates
Craft a resume and LinkedIn profile you’re proud of—featuring certificates recognized by leading global
institutions.
Earn CPE-accredited credentials that showcase your dedication, growth, and essential skills—the qualities
employers value most.
Certificates are included with the Self-Study learning plan.
How it WORKS
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