slide1: abstract 1. Automation in Civil Engineering: Increasing Role and Impact 2. Python Program for Structural Analysis: Achieving 99.98% Accuracy 3. Addressing Student Awareness Gap: Bridging the Knowledge Divide Slide 2: Literature review 1. Python Applications in Civil Engineering Review 2. Python for Interactive Reinforced Concrete Structure Design 3. Enhancing Analytical Skills in Civil Engineering Education with Python Slide3: Problem statement 1. Licensing and Cost Limitations: Expensive and restrictive licensing agreements for existing computer applications. 2. Functionality Constraints: Limitations of MS-Excel and other tools for complex calculations and data display. 3. User Skill and Efficiency: Challenges with software usability, skill requirements, and time-consuming report generation. Slide 4: software and hardware 1. Software Tools: 2. a. Python (IDLE, PyCharm) b. Staad Pro c. Google Colab d. Jupyter Notebook e. Anaconda Navigator 3. Hardware Compatibility: a. Android devices (Android 8+) b. Apple devices (iOS 14+, macOS 10+) c. Windows devices (7, 8, 10, 11) 4. Development Platforms: a. Python programming b. Structural analysis (Staad Pro) c. Cloud-based development (Google Colab) d. Data analysis (Jupyter, Anaconda) e. Cross-platform compatibility (Android, iOS, macOS, Windows) Slide 4: METHODOLOGY 1. Manual Structural Analysis Methods 2. Software for Structural Analysis and Design 3. Automated Structural Analysis using Python Slide 5,6: Algorithm and data RESULTS 1. Algorithm Description 2. Data Collection and Processing 3. Results and Findings Slide 7, 8: ADVANTAGES AND APPLICATIONS 1. Automated Efficiency through Programming Investment 2. Customizable Open-Source Solution 3. Enhanced Performance Slide 9: APPLICATIONS 1. Diverse Applications in Civil Engineering Fields 2. Technological Advancements in Civil Engineering 3. Innovations Transforming Civil Engineering Slide 9, 10, 11, 12: Discussions and conclusion, Future scope REFRENCES 1. **Highly Accurate Structural Design: ** By integrating manual methods and Python programming, we achieved an impressive 99.98% accuracy in designing rectangular beams. This paves the way for a free and user-friendly licensing software, rivaling costly alternatives. 2. **Programming Empowerment for Civil Engineers: ** Introducing software languages in Civil Engineering enhances analytical skills and contributes significantly to various areas like Structural Engineering, Reinforced Concrete/Steel Design, and more. This approach fosters a "Civil Programming Community," providing cost-effective problem-solving globally. 3. **Data Science and AI Revolution: ** This project exemplifies data science and AI's role in efficient project management and application development. Envisioned is a "Civil Programming Community," offering innovative solutions and support to engineers worldwide, akin to established programming communities.