Algorithmic Thinking with Python (UCEST105) Syllabus

Problem-Solving Strategies: Problem-solving strategies defined, importance of understanding multiple problem-solving strategies, Trial and Error, Heuristics, Means-Ends Analysis, and Backtracking (working backward). The Problem-Solving Process: Computer as a model of computation, understanding the problem, formulating a model, developing an algorithm, writing the program, testing the program, and evaluating the solution. Essentials of Python Programming: creating and using variables in Python, numeric and string data types, using the math module, using the Python Standard Library for basic I/O (print, input), Python operators and their precedence.

Algorithm and Pseudocode Representation: meaning and definition of pseudocode, reasons for using pseudocode, main constructs (sequencing, selection — if-else, case structure, repetition — for/while/repeat-until loops), sample problems. Flowcharts: symbols used in creating a flowchart — start/end, arithmetic calculations, input/output, decision, module call, for loop, flow-lines, on-page/off-page connectors (visualization only, tools like RAPTOR suggested).

Selection and Iteration using Python: if-else, elif, for loop, range, while loop; sequence data types — list, tuple, set, strings, dictionary; creating and using arrays in Python (NumPy). Decomposition and Modularization: problem decomposition as a strategy for solving complex problems, modularization, defining and using functions in Python, functions with multiple return values. Recursion: recursion defined, reasons for using recursion, the call stack, avoiding circularity, sample problems (nth Fibonacci number, GCD, factorial, sum of digits).

Computational Approaches to Problem-Solving (introductory diagrammatic/algorithmic explanations only): Brute-force Approach (padlock, password guessing); Divide-and-Conquer Approach (Merge Sort, advantages/disadvantages); Dynamic Programming Approach (Fibonacci series, recursion vs dynamic programming); Greedy Algorithm Approach (task scheduling example, motivations, characteristics, greedy vs dynamic programming); Randomized Approach (coupon collector problem, hat-check problem, motivations).