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01 - Introduction - Part 01

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[ Rebooting a remote server via SSH 🐧 ] 1️⃣Simple reboot via SSH Connect to the remote server via SSH and run the command: 
ssh user@server 'sudo reboot'
Alternative with shutdown: 
ssh user@server 'sudo shutdown -r now'
These commands will terminate all processes on the server, close sessions, and reboot. 2️⃣Force kernel reboot If the server is frozen and standard control methods are not available, you can send commands to the kernel via sysrq: 
ssh user@server 'echo b > /proc/sysrq-trigger'
• b — initiates an immediate reboot without terminating processes. • ⚠ Use this method only in emergency situations, as data loss is possible. 3️⃣ Bulk reboot of several servers If you need to reboot several servers at once, you can use a script: 
for server in server1 server2 server3; do 
ssh user@$server 'sudo reboot' 
done
4️⃣ Checking server availability after reboot To make sure that the server is available again, you can use ping: 
ping server -c 5
Or connect back via SSH: 
ssh user@server
Configure notifications or logging on the server to receive notifications about reboot completion.

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Linux System Administrator Roadmap ├── 🔹 Foundations │ ├── 🔸 Basic Linux Concepts │ │ ├── 📂 File System Hierarchy │ │ ├── 🔑 Permissions and Ownership │ │ ├── 🔄 Processes and Daemons │ │ └── 🖥️ Basic Commands (ls, cd, cp, mv, rm, etc.) │ ├── 🔸 Shell and Scripting │ │ ├── 📝 Bash Scripting │ │ ├── 🔧 Basic Automation │ │ └── 🛠️ Common Shell Commands (grep, awk, sed) │ ├── 🔸 Networking Fundamentals │ │ ├── 🌐 TCP/IP Stack │ │ ├── 🌍 DNS Configuration │ │ ├── 🔌 Network Interfaces │ │ └── 🛠️ Basic Network Troubleshooting (ping, traceroute, netstat) │ └── 🔸 System Installation and Configuration │ ├── 💿 Installation Methods (ISO, PXE) │ ├── 🗂️ Disk Partitioning and File Systems │ └── 🔒 Initial Configuration and Hardening ├── 🔹 System Administration │ ├── 🔸 User and Group Management │ │ ├── 👤 User Accounts │ │ ├── 👥 Group Policies │ │ └── 🔑 sudo Configuration │ ├── 🔸 Package Management │ │ ├── 📦 RPM and YUM (RHEL/CentOS) │ │ ├── 📦 APT (Debian/Ubuntu) │ │ └── 🔧 Compiling from Source │ ├── 🔸 Process Management │ │ ├── 🔄 Systemd and Init │ │ ├── 👀 Monitoring and Controlling Processes │ │ └── 🕒 Crontab and Scheduled Tasks │ ├── 🔸 Filesystem Management │ │ ├── 🗂️ Mounting and Unmounting File Systems │ │ ├── 📁 NFS and Samba │ │ └── 📊 Disk Quotas │ └── 🔸 Security │ ├── 🔥 Firewalls (iptables, firewalld) │ ├── 🛡️ SELinux and AppArmor │ ├── 🔒 SSH Configuration and Hardening │ └── 📝 Auditing and Logging ├── 🔹 Networking and Services │ ├── 🔸 Network Configuration │ │ ├── 🌐 Static and Dynamic IP Addressing │ │ ├── 🔌 Network Bonding and Bridging │ │ └── 📶 VLANs │ ├── 🔸 Web Services │ │ ├── 🌐 Apache and Nginx │ │ ├── 🔐 HTTPS and SSL/TLS │ │ ├── ↔️ Reverse Proxies │ │ └── 🛡️ Web Application Firewalls (WAF) │ ├── 🔸 Database Management │ │ ├── 💾 MySQL/MariaDB │ │ ├── 💾 PostgreSQL │ │ ├── 📂 NoSQL Databases (e.g., MongoDB) │ │ └── 🔄 Backup and Restore │ ├── 🔸 Email Services │ │ ├── ✉️ Postfix and Sendmail │ │ └── 🚫 Spam Filtering │ └── 🔸 File and Print Services │ ├── 📁 Samba │ ├── 📁 NFS │ └── 🖨️ CUPS ├── 🔹 Advanced Administration │ ├── 🔸 Virtualization │ │ ├── 💻 KVM and QEMU │ │ ├── 📦 VirtualBox │ │ └── 🔧 Libvirt │ ├── 🔸 Containerization │ │ ├── 🐳 Docker │ │ ├── ☸️ Kubernetes │ │ └── 🔧 Podman │ ├── 🔸 Cloud Computing │ │ ├── ☁️ AWS │ │ ├── ☁️ Azure │ │ ├── ☁️ OpenStack │ │ └── ☁️ GCP (Google Cloud Platform) │ ├── 🔸 Configuration Management │ │ ├── 🤖 Ansible │ │ ├── 🎭 Puppet │ │ ├── 🍴 Chef │ │ └── 🧂 SaltStack │ └── 🔸 High Availability │ ├── 🔗 Clustering (Pacemaker, Corosync) │ ├── ⚖️ Load Balancing (HAProxy, Nginx) │ └── 🔄 Backup and Disaster Recovery ├── 🔹 Monitoring and Performance │ ├── 🔸 System Monitoring │ │ ├── 🔧 Tools (Nagios, Zabbix, Prometheus, Grafana) │ │ └── 📝 Log Management (ELK Stack, Graylog) │ ├── 🔸 Performance Tuning │ │ ├── 🧠 CPU and Memory Optimization │ │ ├── 💾 Disk I/O Performance │ │ └── 🌐 Network Performance │ └── 🔸 Troubleshooting │ ├── 📝 Log Analysis │ ├── 🔍 Common Issues and Solutions │ ├── 🚧 Performance Bottlenecks │ └── 🔧 Kernel Tuning ├── 🔹 Scripting and Automation │ ├── 🔸 Advanced Scripting │ │ ├── 📜 Shell Scripting (Advanced) │ │ ├── 🐍 Python Scripting │ │ └── 🤖 Task Automation │ └── 🔸 Infrastructure as Code (IaC) │ ├── 🌍 Terraform │ ├── ☁️ CloudFormation │ └── 🔄 CI/CD Pipelines (Jenkins, GitLab CI) └── 🔹 Security and Compliance ├── 🔸 Security Best Practices │ ├── 🔒 System Hardening │ └──🔄 Regular Updates and Patching └── 🔍 Security Auditing Tools (e.g., Lynis, OpenVAS)

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Data Analyst Learning Plan in 2024 |-- Week 1: Introduction to Data Analysis | |-- Data Analysis Fundamentals | | |-- What is Data Analysis? | | |-- Types of Data Analysis | | |-- Data Analysis Workflow | |-- Tools and Environment Setup | | |-- Overview of Tools (Excel, SQL) | | |-- Installing Necessary Software | | |-- Setting Up Your Workspace | |-- First Data Analysis Project | | |-- Data Collection | | |-- Data Cleaning | | |-- Basic Data Exploration | |-- Week 2: Data Collection and Cleaning | |-- Data Collection Methods | | |-- Primary vs. Secondary Data | | |-- Web Scraping | | |-- APIs | |-- Data Cleaning Techniques | | |-- Handling Missing Values | | |-- Data Transformation | | |-- Data Normalization | |-- Data Quality | | |-- Ensuring Data Accuracy | | |-- Data Integrity | | |-- Data Validation | |-- Week 3: Data Exploration and Visualization | |-- Exploratory Data Analysis (EDA) | | |-- Descriptive Statistics | | |-- Data Distribution | | |-- Correlation Analysis | |-- Data Visualization Basics | | |-- Choosing the Right Chart Type | | |-- Creating Basic Charts | | |-- Customizing Visuals | |-- Advanced Data Visualization | | |-- Interactive Dashboards | | |-- Storytelling with Data | | |-- Data Presentation Techniques | |-- Week 4: Statistical Analysis | |-- Introduction to Statistics | | |-- Descriptive vs. Inferential Statistics | | |-- Probability Theory | |-- Hypothesis Testing | | |-- Null and Alternative Hypotheses | | |-- t-tests, Chi-square tests | | |-- p-values and Significance Levels | |-- Regression Analysis | | |-- Simple Linear Regression | | |-- Multiple Linear Regression | | |-- Logistic Regression | |-- Week 5: SQL for Data Analysis | |-- SQL Basics | | |-- SQL Syntax | | |-- Select, Insert, Update, Delete | |-- Advanced SQL | | |-- Joins and Subqueries | | |-- Window Functions | | |-- Stored Procedures | |-- SQL for Data Analysis | | |-- Data Aggregation | | |-- Data Transformation | | |-- SQL for Reporting | |-- Week 6-8: Python for Data Analysis | |-- Python Basics | | |-- Python Syntax | | |-- Data Types and Structures | | |-- Functions and Loops | |-- Data Analysis with Python | | |-- NumPy for Numerical Data | | |-- Pandas for Data Manipulation | | |-- Matplotlib and Seaborn for Visualization | |-- Advanced Data Analysis in Python | | |-- Time Series Analysis | | |-- Machine Learning Basics | | |-- Data Pipelines | |-- Week 9-11: Real-world Applications and Projects | |-- Capstone Project | | |-- Project Planning | | |-- Data Collection and Preparation | | |-- Building and Optimizing Models | | |-- Creating and Publishing Reports | |-- Case Studies | | |-- Business Use Cases | | |-- Industry-specific Solutions | |-- Integration with Other Tools | | |-- Data Analysis with Excel | | |-- Data Analysis with R | | |-- Data Analysis with Tableau/Power BI | |-- Week 12: Post-Project Learning | |-- Data Analysis for Business Intelligence | | |-- KPI Dashboards | | |-- Financial Reporting | | |-- Sales and Marketing Analytics | |-- Advanced Data Analysis Topics | | |-- Big Data Technologies | | |-- Cloud Data Warehousing | |-- Continuing Education | | |-- Advanced Data Analysis Techniques | | |-- Community and Forums | | |-- Keeping Up with Updates | |-- Resources and Community | |-- Online Courses (edX, Udemy) | |-- Books | |-- Data Analysis Blogs | |-- Data Analysis Communities

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1. Prompt Engineering Basics https://explore.skillbuilder.aws/learn/course/external/view/elearning/17763/foundations-of-prompt-engineering 2. ChatGPT Prompts Mastery https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/ 3.Introduction to Generative AI https://www.cloudskillsboost.google/course_templates/536 4. AI Introduction by Harvard https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python/2023-05 5.Microsoft GenAI Basics https://www.linkedin.com/learning/what-is-generative-ai/generative-ai-is-a-tool-in-service-of-humanity 6. Prompt Engineering Pro https://learnprompting.org 7. Google’s Ethical AI https://www.cloudskillsboost.google/course_templates/554 8. Harvard Machine Learning https://pll.harvard.edu/course/data-science-machine-learning 9. LangChain for LLM Apps https://www.deeplearning.ai/short-courses/langchain-for-llm-application-development/ 10. Bing Chat Applications https://www.linkedin.com/learning/streamlining-your-work-with-copilot-formerly-bing-chat-bing-chat-enterprise/put-your-fingers-to-work-chatting-as-a-productivity-tool 11. Generative AI by Microsoft: https://learn.microsoft.com/en-us/training/paths/introduction-generative-ai/ 12. Amazon's AI Strategy: https://explore.skillbuilder.aws/learn/public/learning_plan/view/1909/generative-ai-learning-plan-for-decision-makers 13. GenAI for Everyone : https://www.deeplearning.ai/courses/generative-ai-for-everyone/ 14. AWS GenAI Foundation : https://www.coursera.org/learn/generative-ai-with-llms ♻️ 𝗕𝗼𝗻𝘂𝘀: • OpenCV Bootcamp: https://opencv.org/university/free-opencv-course/ • Tensorflow Bootcamp: https://opencv.org/university/free-tensorflow-keras-course/

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photo content

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📜 ADCS Attack Techniques Cheatsheet This is a handy table outlining the various methods of attack against Active Directory Certificate Services (ADCS) 🔗 Source: https://docs.google.com/spreadsheets/d/1E5SDC5cwXWz36rPP_TXhhAvTvqz2RGnMYXieu4ZHx64/edit?gid=0#gid=0

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fd command — a fast and convenient alternative to find The fd command is a tool for quickly searching for files and directories by name or pattern. It is designed as a modern and convenient alternative to the classic find command, offering improved speed and ease of use. Installing fd On most Linux distributions, fd is available in the official repositories: 
sudo apt install fd-find # For Debian/Ubuntu
sudo dnf install fd-find # For Fedora
sudo pacman -S fd # For Arch Linux
Basic usage 1️⃣Find by file name: 
fd filename
Finds all files whose name contains “filename”. 2️⃣Find by pattern: 
fd 'pattern'
Regular expressions for patterns are supported. 3️⃣ Search in a specific directory: 
fd filename /path/to/dir
4️⃣ Exclude binary files (default): 
fd 'filename'
fd searches only text files, ignoring binary ones. 5️⃣ Search by file type: 
• Only files: fd -t f
• Only directories: fd -t d
6️⃣ Display full information: 
fd -l
Shows detailed paths to found objects. 7️⃣ Case-sensitive search: 
fd -s filename
8️⃣Example of searching in a project: Find all files with the .rs extension in the current directory and its subdirectories: 
fd '\.rs$'

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An introductory book about using the Rust Programming Language on "Bare Metal" embedded systems, such as Microcontrollers https://docs.rust-embedded.org/book/

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Yo anyone learning Rust or GUI Development ?? Drop a msg @OxSourabh

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Quick Linux Tip 🐧 If you have trouble reading the directories in your $PATH variable, you can use the tr command to replace
Quick Linux Tip 🐧 If you have trouble reading the directories in your $PATH variable, you can use the tr command to replace the colons with newlines so that each directory appears on one line. 
$ echo $PATH | tr ":" "\n"

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Breaking into Data Science doesn’t need to be complicated. If you’re just starting out, Here’s how to simplify your approach: Avoid: 🚫 Trying to learn every tool and library (Python, R, TensorFlow, Hadoop, etc.) all at once. 🚫 Spending months on theoretical concepts without hands-on practice. 🚫 Overloading your resume with keywords instead of impactful projects. 🚫 Believing you need a Ph.D. to break into the field. Instead: ✅ Start with Python or R—focus on mastering one language first. ✅ Learn how to work with structured data (Excel or SQL) - this is your bread and butter. ✅ Dive into a simple machine learning model (like linear regression) to understand the basics. ✅ Solve real-world problems with open datasets and share them in a portfolio. ✅ Build a project that tells a story - why the problem matters, what you found, and what actions it suggests.

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