Date: 16’th February 2025 (Sunday)
Time: 9:30 AM to 1:00 PM
Venue:
VGLUG Foundation
SRIMAA PRESCHOOL (Gov Recognized)
Landmark: Opposite to BSNL Exchange
Villupuram 605602
Minutes of meeting
Data Analytics with Python Team 1 & 2 (Engineering)
Topics:
- Session 1: Numpy and matplotlib introduction -Deepak
- Session 2: Docker networks and volumes Mathusoothanan
Session 1:
NumPy (Numerical Python)
NumPy is a powerful Python library for numerical computing. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these data structures efficiently.
Key Features:
ndarray (N-dimensional array): Efficient array processing.
Mathematical Functions: Linear algebra, statistics, and Fourier transforms.
Broadcasting: Enables element-wise operations on arrays of different shapes.
Performance: Faster than Python lists due to optimized C-based implementation
Matplotlib:
Matplotlib is a popular data visualization library in Python, primarily used for creating static, animated, and interactive plots. It allows users to generate a wide variety of graphs and charts with simple commands.
Key Features:
Plots and Graphs: Line plots, bar charts, histograms, scatter plots, etc.
Customization: Control over colors, labels, legends, and styles.
Integration: Works well with NumPy, Pandas, and other libraries.
Subplots: Allows multiple visualizations in a single figure.
Session 2: Docker networks and volumes:
Docker Networks
Docker networks allow containers to communicate with each other and external systems securely and efficiently.
Types of Docker Networks:
Bridge Network (Default):
Containers on the same bridge network can communicate using container names.
Used for standalone applications.
Example: docker network create my_bridge
Host Network:
Removes network isolation and directly uses the host’s networking.
Suitable for performance-sensitive applications.
Example: docker run –network host my_container
Overlay Network:
Used in Docker Swarm for multi-host communication.
Enables container-to-container communication across different physical machines.
Example: docker network create –driver overlay my_overlay
None Network:
Completely isolates the container from networking.
Example: docker run –network none my_container
Macvlan Network:
Assigns a unique MAC address to each container, making it appear as a physical device on the network.
Example: docker network create -d macvlan …
Docker Volumes
Docker volumes are used for persistent data storage, ensuring data remains intact even after container restarts.
Types of Docker Storage:
Volumes:
Managed by Docker and stored outside the container’s file system.
docker volume create my_volume
docker run -v my_volume:/app/data my_container
Bind Mounts:
Maps a host directory to a container directory.
docker run -v /host/path:/container/path my_container
Tmpfs Mounts:
Stores data in memory, not on disk.
docker run –tmpfs /app/tmpfs my_container
Data Analytics with Python Team 1 , Team 2(Arts)
Topics:
- Session 1: Numpy and matplotlib – Sathish,Vijayalakshmi
- Session 2: Book Reading – Vijayalakshmi, Kowsalya
Session 1: NumPy and Matplotlib Introduction
NumPy (Numerical Python)
NumPy is a powerful Python library for numerical computing. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these data structures efficiently.
Key Features:
ndarray (N-dimensional array): Efficient array processing.
Mathematical Functions: Linear algebra, statistics, and Fourier transforms.
Broadcasting: Enables element-wise operations on arrays of different shapes.
Performance: Faster than Python lists due to optimized C-based implementation.
Matplotlib:
Matplotlib is a popular data visualization library in Python, primarily used for creating static, animated, and interactive plots. It allows users to generate a wide variety of graphs and charts with simple commands.
Key Features:
Plots and Graphs: Line plots, bar charts, histograms, scatter plots, etc.
Customization: Control over colors, labels, legends, and styles.
Integration: Works well with NumPy, Pandas, and other libraries.
Subplots: Allows multiple visualizations in a single figure.
Session 2: Book Reading – Vijayalakshmi
Dilip led a thought-provoking book reading session, discussing:
- The role of storytelling in learning and personal growth
- A deep dive into key takeaways from selected books
- An interactive discussion on philosophical and technical insights
Devops
Topics:
- Session 1: Ansible Continuation – Vignesh
- Session 2: Discussion about secure coding
Session 1: Ansible Continuation – Vignesh
Vignesh deepened participants’ understanding of Ansible with:
- Playbook structuring and best practices
- Automating server configurations
- Integration with cloud services
Session 2: Discussion about secure coding
Secure coding is the practice of writing software in a way that protects against vulnerabilities, attacks, and data breaches. It ensures the security of applications by following best practices and guidelines to minimize risks.
Key Principles of Secure Coding:
Input Validation:
Always validate and sanitize user inputs to prevent SQL injection, XSS, and command injection.
Example: Use parameterized queries in SQL.
Authentication & Authorization:
Implement strong authentication (e.g., multi-factor authentication).
Follow the principle of least privilege (PoLP) to restrict user permissions.
Secure Data Handling:
Encrypt sensitive data (both at rest and in transit).
Avoid storing plain-text passwords—use secure hashing algorithms like bcrypt or Argon2.
Error Handling & Logging:
Do not expose detailed error messages to users (to prevent information leakage).
Log security-related events but avoid logging sensitive data.
Secure Dependencies & Updates:
Regularly update software, libraries, and dependencies to patch known vulnerabilities.
Use tools like OWASP Dependency-Check to monitor dependencies.
Secure Code Practices:
Use prepared statements for database queries.
Avoid hardcoding credentials in the source code—use environment variables or secrets management tools.
Session Management:
Use secure cookies with HttpOnly and Secure flags.
Implement proper session expiration and invalidation.
Secure API Development:
Use authentication mechanisms like OAuth 2.0 or API keys.
Implement rate limiting and input validation to prevent abuse.
Code Review & Security Testing:
Conduct regular security code reviews and penetration testing.
Use static and dynamic analysis tools (e.g., SonarQube, Snyk) to identify vulnerabilities.
Compliance & Security Standards:
Follow industry standards like OWASP Top 10, NIST, and ISO 27001.
Adhere to data protection laws like GDPR, CCPA, and HIPAA if applicable.
By following secure coding practices, developers can reduce security risks and build robust, attack-resistant applications.
Arasur Glug
Problem solving using Sudoku
Scanning for Missing Numbers
Check each row, column, and 3×3 grid for missing numbers.
Fill in the obvious missing values.
Elimination Method
If a number appears in a row or column, it cannot appear again in the same row, column, or 3×3 subgrid.
Narrow down possibilities by ruling out numbers that are already placed.
Here, I attached the meetup clicks,
Devops
Engineering 1 & 2
Arts 1 & 2
Acknowledgements
A big thanks to all the speakers: Mathusoothanan, sathish, Deepak, Kowsalya, Vijayalakshmi , and Vignesh for sharing their knowledge. The sessions provided valuable insights and practical learning experiences.
Thanking you
VGLUG Volunteer
VGLUG Foundation 