UCEST105-S1-M1

COURSE INFORMATION

  • Course Title: Algorithmic Thinking with Python

  • Course Code: UCEST105

  • CIE Marks: 40

  • ESE Marks: 60

  • Credits: 4

  • Teaching Hours/Week: 3:0:2:0 (Lecture: 3, Tutorial: 0, Practical: 2, Research: 0)

  • Exam Duration: 2 Hrs. 30 Min

  • Prerequisites: None

  • Course Type: Theory

COURSE OBJECTIVES

  1. Provide students with a thorough understanding of algorithmic thinking and its practical applications in solving real-world problems.

  2. Explore various algorithmic paradigms including:

    • Brute Force

    • Divide-and-Conquer

    • Dynamic Programming


    • Heuristicsto address and solve complex problems.

SYLLABUS OVERVIEW

MODULE 1: PROBLEM-SOLVING STRATEGIES

  • Definition of problem-solving strategies and their importance

  • Key strategies to include:

    • Trial and Error

    • Heuristics

    • Means-Ends Analysis

    • Backtracking (Working Backward)

MODULE 2: THE PROBLEM-SOLVING PROCESS

  • Computer as a model of computation

  • Steps in the problem-solving process:

    • Understanding the problem

    • Formulating a model

    • Developing an algorithm

    • Writing and testing the program

    • Evaluating the solution

MODULE 3: ESSENTIALS OF PYTHON PROGRAMMING

  • Creating and using variables in Python

  • Understanding numeric and string data types

  • Using the math module and Python's standard library for basic I/O

  • Overview of Python operators and their precedence

PROBLEM-SOLVING STRATEGIES

General Overview

  • Problem-solving strategies are essential tools that provide structured methods to analyze, understand, and resolve various challenges.

  • Strategies include:

    • Trial and Error

    • Algorithms

    • Heuristics

    • Means-Ends Analysis

    • Backtracking

TRIAL AND ERROR

  • Involves attempting different solutions and learning from mistakes until achieving success.

  • Fundamental method relying on experimentation and iteration rather than systematic approaches.

  • Example: Regaining access to an online account by trying different passwords until successful.

    • Initial Attempts: Start with familiar passwords.

    • Learning from Mistakes: Recall personal variations.

    • Refinement: Try new formats based on previous patterns.

    • Success: Achieving the correct password through persistence.

ALGORITHMIC PROBLEM SOLVING

  • An algorithm is a step-by-step logical procedure that guarantees a solution to a problem.

  • Systematic, defined sequence of operations ensures accuracy and consistency.

HEURISTIC PROBLEM SOLVING

  • Heuristics offer a practical approach based on experience and intuition.

  • Provides good enough solutions quickly, often without guaranteeing perfection.

  • Example: Navigating through a congested city using a traffic app and personal experience to find alternative routes.

MEANS-ENDS ANALYSIS

  • Strategy that breaks down a problem into manageable parts to reach a final goal.

  • Involves identifying the current state, desired state, and steps needed to bridge the difference.

  • Example: Planning a road trip from Trivandrum to Kashmir in segments.

PROBLEM DECOMPOSITION

  • Breaking complex problems into sub-problems makes them more manageable.

ADDITIONAL STRATEGIES

BRAINSTORMING

  • Generating a wide range of ideas without immediate judgement.

  • Encourages creative thinking to explore possibilities.

  • Example: Ideas for improving customer satisfaction in a team setting.

LATERAL THINKING

  • Looking at problems from unconventional angles.

  • Example: Identifying new markets or uses for declining products.

ROOT CAUSE ANALYSIS

  • Identifying the fundamental causes rather than just symptoms.

SWOT ANALYSIS

  • Evaluating strengths, weaknesses, opportunities, and threats related to decisions.

  • Guides strategic decisions based on internal and external factors.

  • Example: Conducting SWOT analysis before launching a new product.

SIMULATION

  • Creating models of real-world scenarios to predict outcomes.

  • Example: Traffic simulations help urban planners mitigate congestion.

USING EXPERIENCE

  • Drawing on past experiences to address current challenges.

BACKTRACKING

  • Algorithm works by exploring all possible solutions recursively. Steps include:

    1. Choose an initial solution.

    2. Explore possible extensions.

    3. Return solutions or backtrack if needed.

EXAMPLES OF BACKTRACKING ALGORITHM

  • Shortest path finding in a maze using recursive exploration of four directions while considering obstacles.

PROBLEM SOLVING PROCESS

  • Understanding the Problem: Clear comprehension using techniques like interviews and questionnaires to collect information.

  • Formulate a Model: Developing a solution using brainstorming and modeling techniques.

  • Develop an Algorithm: Translating solutions into formal algorithms and assessing their efficacy based on various factors (e.g., time, memory).

  • Code the Algorithm: Implementing the best algorithm into an executable program while following an incremental approach for development.