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CS 101: Introduction to Computational Thinking
Course Code CS 101
Course Name Introduction to Computational Thinking
Department Computer Science
Semester Offered Odd (Term 1 - Dubai)
Tuition Hours 30 hours (Theory) + 10 hours (Lab)
Course Level Foundational
Pre-requisite -
Co-requisite CS 102: Rapid Web Development for AI Products
Course Objective Programming is not about syntax. It is about thinking clearly enough to instruct a machine.

This course introduces students to computational thinking as a way of solving real problems, not as an academic exercise. Python is used as a medium, not the goal. The real goal is to learn how to break messy, real-world problems into precise, executable steps.

Students will learn to design algorithms, work with data, and integrate external AI systems through APIs. By the end of the course, they will be able to build small but complete systems that take inputs, process information, and produce useful outputs.

The focus is simple: write code that works, ship it fast, and improve it through iteration.
Course Philosophy This course emphasizes
  • Concepts over syntax
  • Building over reading
  • Shipping over perfection
Programming is treated as a tool for thinking and building, not as a theoretical subject. Students will write code from week one, make mistakes, debug, and learn by doing. The goal is not to memorize Python, but to internalize how systems behave when instructions are executed step by step.
Course Learning Outcomes Upon successful completion of this course, students will be able to:
  • Break down real-world problems into step-by-step computational procedures.
  • Write clean and functional Python programs that process inputs and produce meaningful outputs.
  • Use control flow constructs (branching, looping, condition-based execution) to design logical systems.
  • Work with basic data structures such as lists, dictionaries, and strings to manipulate data.
  • Debug code effectively, identify errors, and improve program reliability.
  • Integrate external APIs, especially AI APIs, to build simple intelligent workflows.
  • Build and ship small working systems that can be used by real users in their Term 1 projects.
Course Author Sagar Udasi
MSc Statistics and Data Science with Computational Finance from The University of Edinburgh.
Contact: sagar.l.udasi@gmail.com
Course Organiser TBD
Details will be updated before course commencement.
No. Lecture Title Concepts Covered Lecture Objective
01 Why Computers Are Stupid (And That’s Their Power) What is computation, instructions, determinism, abstraction Build the right mental model: computers do exactly what you tell them. This is the foundation of writing correct programs.
02 How Do You Explain A Task To A Machine? Algorithms, step-by-step thinking, pseudo-code Teach students to think in procedures. This directly maps to building AI workflows later.
03 Your First Program That Actually Does Something Python basics, inputs, outputs, variables Move from theory to action quickly. Students write and run their first useful program.
04 Decisions: Teaching Machines To Choose Branching logic, conditions, decision trees Essential for building agents that respond differently based on user inputs or data.
05 Repetition Without Boredom Looping, iteration, automation Show how machines scale effort. Critical for data processing and automation tasks.
06 Data Is Just Organized Information Lists, strings, basic data structures Students learn how to store and manipulate real-world data.
07 How To Think Like A Debugger Errors, debugging strategies, reading tracebacks Debugging is a core skill. This lecture builds resilience and problem-solving ability.
08 Functions: Naming Your Thinking Modularization, functions, reuse Teach students to break systems into reusable components. Crucial for scaling projects.
09 Files: Making Your Code Remember Things File I/O, reading and writing data Enables persistence. Important for logs, datasets, and simple products.
10 From Raw Data To Insight Basic data processing, transformations Students start doing meaningful work with data, preparing for ML workflows.
11 The Internet Is A Giant API APIs, HTTP requests, JSON Introduces how software talks to other software. Foundation for AI integrations.
12 Talking To AI Models Using AI APIs, prompts, responses Students integrate real AI systems into their code. This directly feeds into their agent projects.
13 Automating A Real Task End-To-End Combining logic, loops, APIs Students build a complete workflow. This is a mini version of their microbusiness agent.
14 When Code Meets Users Input validation, edge cases Prepares students to handle real users, not ideal scenarios.
15 Speed vs Correctness: What Matters? Trade-offs, iteration, MVP thinking Instills product thinking. Shipping fast matters more than perfect code.
16 Making Code Readable (So You Don’t Hate Yourself Later) Code structure, naming, clarity Teaches maintainability. Important for long-term projects.
17 From Script To System Structuring larger programs Helps students transition from small scripts to real applications.
18 Failure Modes In Real Systems Handling errors, robustness Prepares students for real-world unpredictability in deployed systems.
19 Building Your First AI Microbusiness Engine Putting everything together Directly connects course learning to Term 1 capstone project.
20 Demo Day: What Did You Actually Build? Presentations, feedback Forces students to ship and reflect.

Lab Sessions (7 Sessions)

No. Lab Title Concepts Covered Objective
L1 Setup That Actually Works Environment setup, running Python Remove friction. Students should be able to code without setup issues.
L2 Build A CLI Tool Input/output programs Create a usable command-line tool.
L3 Automate A Boring Task Loops, file handling Apply automation to real problems.
L4 Data Cleanup Sprint Data manipulation Work with messy real-world data.
L5 First API Integration API calls, JSON parsing Connect code with external systems.
L6 AI Workflow Builder AI API integration Build a simple AI-powered workflow.
L7 Ship Your Micro Tool End-to-end project Deploy a usable tool that contributes to their AI agent.
Component Weightage
Weekly Coding Assignments (5 total) 30%
Lab Project (AI Workflow Tool) 20%
Final Project: Microbusiness Component 30%
Viva + Code Review 20%
Type Resource Provider
Lecture CS50’s Introduction to Programming with Python Harvard (David Malan)
Lecture Python for Everybody Dr. Charles Severance
Reading Automate the Boring Stuff with Python Al Sweigart
Documentation Python Official Documentation python.org
Practice LeetCode (Easy Problems) LeetCode
Practice HackerRank Python Track HackerRank