L4 Problem Solving Techniques--Module 2

Lecture 4: Problem Solving Techniques

Page 1:

  • Lecture Title: Problem Solving Techniques

Page 2: Introduction to Problem Solving

  • Problem Solving Basics

    • Logic is essential for effective problem solving.

  • Classification of Computational Problems

    • Various types of computational problems.

  • Programming Paradigms and Methodologies

    • Introduction and significance in computing.

  • Introduction to Algorithms

    • Algorithms for simple problems and analysis techniques.

  • Flow Charts

    • Basic flow chart creation and usage of the RAPTOR tool for implementation with simple problems.

  • Learning Outcomes

    • Relevant for CSE 101/102, Computer Science and Engineering.

Page 3: The Problem Solving Loop

  1. Identify the Problem

  2. Explore Information

    • Gather data and create ideas.

  3. Select the Best Idea

  4. Build and Test the Idea

  5. Evaluate the Results

  • Lesson 4 connects problem solving with programming paradigms and methodologies.

Page 4: Learning Objectives

  • Understanding key concepts such as:

    • Problem solving basics

    • The importance of logic in problem solving

    • Various types and classifications of computational problems

    • Introduction to relevant programming paradigms and methodologies.

Page 5: Overview of Concepts

  • Additional Information

    • Syntax, Control Flow, and Video clips.

    • Case studies and DIY exercises included.

Page 6: Skill Set for Software Engineers

  • Key competencies and skills necessary for software engineers.

Page 7: Understanding What is a Problem

  • Definition and characteristics of problems in programming and computation.

Page 8: Detailed Exploration of Problems

  • More insights into problem characteristics and context.

Page 9: Handling Bigger Problems

  • Approaches to managing larger problems effectively.

Page 10: Problem Solving Life Cycle

  • Structured approach to navigating the problem-solving process.

Page 11: The Role of Logic

  • Syntax and Additional Information

    • Key phrases and keywords related to logic in problem solving.

Page 12: Logical Thinking Process

  • Steps and considerations in logical thinking for problem solving.

Page 13: Importance of Logic

  • Logic's crucial role in effective problem solving.

Page 14: Continued Importance of Logic

  • Further discussion on the relevance of logic in various contexts.

Page 15: Types of Logic

  • Overview of the different systems and types of logic.

Page 16: Types of Problems

  • Categorization of varying types of problems faced in computational contexts.

Page 17: Computational Problems

  • Detailed look at computational problems and their characteristics.

Page 18: Applications of Computational Problems

  • Broad applications for computational problems in real-world scenarios.

Page 19: Classification of Computational Problems

  • Different methods for classifying computational problems.

Page 20: Distributed/Concurrent Computation

  • Key points on distributed and concurrent computing.

Page 21: Sequential/Event-Based Computation

  • Explanation of sequential and event-driven approaches to computation.

Page 22: Software Development Life Cycle (SDLC)

  • Overview of the Software Development Life Cycle and its significance.

Page 23: Programming Paradigms and Methodologies

  • Discussion of methodologies used during the SDLC:

    • Unstructured Programming

    • Structured Programming (C)

    • Object-Oriented Programming (OOP; C++)

Page 24: Unstructured Programming (USP)

  • Characteristics of USP:

    • Entire code in one continuous program.

    • Direct data operation.

    • Example: Basic Language.

Page 25: Limitations of USP

  • Challenges as program size increases:

    • Hard to understand and modify.

    • Redundant code resourcing and risk of errors.

Page 26: Structured Programming (SP)

  • Definition and advantages of SP:

    • Breaks problems into manageable subtasks.

    • Example Programming Languages: ALGOL, Pascal, Ada.

Page 27: Limitations of SP

  • Focuses on algorithms over the data, making it suitable mainly for small to medium-sized projects.

Page 28: Object-Oriented Programming (OOP)

  • Characteristics of OOP:

    • Designed for complex problems.

    • Represents real-life situations through objects.

    • Components: State and Behavior (Example languages: C++, Java).

Page 29: Summary of Key Concepts

  • Summary covering:

    • Problem solving

    • Logic and its significance

    • Classifications of computational problems

    • Programming paradigms: USP, SP, and OOP.