Syllabus for Algorithmic Thinking with Python

Module 1: Problem-solving and Python fundamentals

Contact hours: 7

1. Problem-solving strategies

   • Trial and error
   • Heuristics
   • Means–ends analysis
   • Backtracking (working backward)

2. Problem-solving process

   • Computer as a model of computation
   • Understand the problem and formulate a model
   • Develop an algorithm and write a program
   • Test the program and evaluate the solution

3. Python essentials

   • Variables and numeric/string data types
   • Math module and standard-library basics
   • Input and output with input and print
   • Operators and precedence

Module 2: Algorithms, flowcharts and control flow

Contact hours: 9

1. Algorithms and pseudocode

   • Meaning and purpose of pseudocode
   • Sequence, selection (if–else, case) and repetition (for, while, repeat–until)
   • Practice problems: expressions, interest, comparisons, grades, loops, factorial and largest value

2. Flowcharts

   • Start/end, calculation, input/output and decision symbols
   • Module calls, loops, flow lines and page connectors
   • Use flowcharts to visualize algorithm control flow

3. Selection, iteration and collections

   • if, elif, for, range and while
   • Lists, tuples, sets, strings and dictionaries
   • Arrays using NumPy

Module 3: Decomposition, functions and recursion

Contact hours: 10

1. Decomposition and modularization

   • Break complex problems into smaller parts
   • Motivation for modular design
   • Demonstrations using merge sort and finding the top three values

2. Functions

   • Define and use functions
   • Functions with multiple return values

3. Recursion

   • Recursive calls and the call stack
   • Avoid circularity
   • Examples: Fibonacci numbers, GCD, factorial, addition and sum of digits

Module 4: Computational approaches to problem-solving

Contact hours: 10

1. Brute force

   • Examples: padlocks and password guessing

2. Divide and conquer

   • Merge sort
   • Advantages and disadvantages

3. Dynamic programming

   • Fibonacci series
   • Recursion compared with dynamic programming

4. Greedy algorithms

   • Task scheduling by completion time
   • Motivation and characteristics
   • Greedy methods compared with dynamic programming

5. Randomized algorithms

   • Coupon-collector example
   • Random hat-check example
   • Motivation for randomized approaches

Course objectives

1. Develop algorithmic thinking and apply it to practical problems.
2. Explore problem-solving strategies and algorithmic approaches.

Course outcomes

1. Use computing as a model for real-world problems.
2. Describe a problem and form a clear model.
3. Develop algorithms and implement them as programs.
4. Apply problem-solving strategies and essential Python skills.

Assessment

Continuous Internal Evaluation (CIE)

• Attendance — 5 marks

• Programming-task continuous assessment — 5 marks

• Two written internal examinations — 10 marks each

• Laboratory examination — 10 marks

End-Semester Examination (ESE): 60 marks

• Part A: Eight questions, two from each module, carrying 3 marks each (24 marks). Answer all.

• Part B: Two questions from each module; answer one full question from each module. Each carries 9 marks (36 marks).

• Examination duration: 2 hours 30 minutes.