Looked After Learning Page

Learn Data Science In 2024 🚀

Getting Started:

- 🚀 Data Science Intro: DataCamp
- 📦 Anaconda Setup: Anaconda Documentation

Programming:

- 🐍 Python Basics: Real Python
- 📊 R Basics: R-bloggers
- 💻 SQL Fundamentals: SQLZoo
- 🧑‍💻 Java for Data Science: Udemy - Java Programming and Software Engineering Fundamentals

Mathematics:

- 🧮 Math for Data Science: Khan Academy
- 📈 Linear Algebra: MIT OpenCourseWare - Linear Algebra

Statistics:

- 📉 Statistics Basics: Statistics.com
- 📊 Practical Statistics: statquest
- 📚 Introduction to Probability: edX - Probability and Statistics
- 📊 Hypothesis Testing: Towards Data Science

Data Analysis:

- 📂 Data Wrangling: Python Pandas
- 📊 Exploratory Data Analysis (EDA): Towards Data Science
- 📁 Data Cleaning Techniques: Towards Data Science
- 📈 Time Series Analysis: Kaggle - Time Series Analysis

Data Visualization:
- 📊 Data Viz with Python: Matplotlib
- 📈 Interactive Data Dashboards: Tableau
- 📊 Data Viz with R: R Graph Gallery

Machine Learning:

- 🤖 Introduction to ML: Coursera - Machine Learning by Andrew Ng
- 🧠 Deep Learning: fast.ai
- 📚 Machine Learning Guides: Machine Learning Mastery

Advanced Topics:

- 📰 Natural Language Processing (NLP): Kaggle - Natural Language Processing
- 💡 Big Data: edX - Big Data Fundamentals
- 📡 Computer Vision: Stanford University - CS231n

YouTube Channels:

- 🎥 Data Science Tutorials: Corey Schafer
- 📉 Statistics & ML: statquest
- 🧮 Mathematics: 3Blue1Brown
- 📚 Data Analyst Insights: AlexTheAnalyst
- 🤖 ML & DL Guides: sentdex

Certifications:

- 📜 Data Science Certification: Coursera - IBM Data Science
- 📈 SQL Certification: Microsoft - Microsoft Certified: Azure Data Fundamentals

Communities:

👥 Data Science Central: Data Science Central
👥 Kaggle Community: Kaggle

Career Development:

- 💼 LinkedIn Profile Optimization: LinkedIn Learning
- 📝 Resume Building: The Balance Careers - Resume Examples and Writing Tips.

Here are the best Data Science playlists.🔥

In the IT industry, the fundamental skill of log parsing remains as vital as ever.

It's the backbone of troubleshooting, security analysis, and system monitoring.

To aid in this crucial task, I've compiled a comprehensive Log Parsing Cheatsheet that is perfect for IT professionals of all stripes.

Here’s a breakdown of each command and how you can use it:

🔎 𝗛𝗘𝗔𝗗: 𝚑𝚎𝚊𝚍 𝚏𝚒𝚕𝚎.𝚕𝚘𝚐 gives you the top ten lines of a file, which is often where critical recent error logs can be found. For instance, 𝚑𝚎𝚊𝚍 -𝚗 𝟸𝟶 𝚏𝚒𝚕𝚎.𝚕𝚘𝚐 displays the first 20 lines.

🔍 𝗧𝗔𝗜𝗟: 𝚝𝚊𝚒𝚕 𝚏𝚒𝚕𝚎.𝚕𝚘𝚐 does the opposite, showing you the last ten lines of a file — where the most recent events are logged. Try 𝚝𝚊𝚒𝚕 -𝚏 𝚏𝚒𝚕𝚎.𝚕𝚘𝚐 to get a real-time stream of log updates.

🔄 𝗖𝗢𝗠𝗠: 𝚌𝚘𝚖𝚖 𝚏𝚒𝚕𝚎𝟷.𝚕𝚘𝚐 𝚏𝚒𝚕𝚎𝟸.𝚕𝚘𝚐 helps you compare two sorted files. It's perfect for finding discrepancies between log versions, like 𝚌𝚘𝚖𝚖 -𝟹 𝚜𝚎𝚛𝚟𝚎𝚛𝟷.𝚕𝚘𝚐 𝚜𝚎𝚛𝚟𝚎𝚛𝟸.𝚕𝚘𝚐 to see lines unique to each.

🔣 𝗟𝗘𝗦𝗦: 𝚕𝚎𝚜𝚜 𝚏𝚒𝚕𝚎.𝚕𝚘𝚐 allows for on-the-fly viewing of large log files. Navigate with 𝙶, 𝚐𝚐, and /𝚜𝚎𝚊𝚛𝚌𝚑_𝚝𝚎𝚛𝚖.

📊 𝗖𝗦𝗩𝗞𝗜𝗧: 𝚌𝚜𝚟𝚌𝚞𝚝 -𝚌 𝟹 𝚍𝚊𝚝𝚊.𝚌𝚜𝚟 can extract columns from CSVs. For example, 𝚌𝚜𝚟𝚌𝚞𝚝 -𝚗 𝚍𝚊𝚝𝚊.𝚌𝚜𝚟 lists column names.

📑 𝗝𝗤: 𝚓𝚚 .𝚏𝚘𝚘 𝚍𝚊𝚝𝚊.𝚓𝚜𝚘𝚗 is for JSON parsing — invaluable for modern web app logs. Use 𝚓𝚚 '.[] | .𝚗𝚊𝚖𝚎' 𝚞𝚜𝚎𝚛𝚜.𝚓𝚜𝚘𝚗 to extract user names from a list.

🔍 𝗚𝗥𝗘𝗣: 𝚐𝚛𝚎𝚙 '𝚎𝚛𝚛𝚘𝚛' 𝚏𝚒𝚕𝚎.𝚕𝚘𝚐 finds all occurrences of 'error' in a file. Advanced usage like 𝚐𝚛𝚎𝚙 -𝙴 "𝟺[𝟶-𝟿]{𝟸}" 𝚏𝚒𝚕𝚎.𝚕𝚘𝚐 finds all 400-level errors in HTTP logs.

📡 𝗡𝗚𝗥𝗘𝗣: 𝚗𝚐𝚛𝚎𝚙 -𝚍 𝚎𝚝𝚑𝟶 '𝟺𝟶𝟺' 𝚙𝚘𝚛𝚝 𝟾𝟶 listens on the network for specific data, useful for real-time traffic analysis.

🔧 𝗧𝗥: 𝚝𝚛 '[:𝚕𝚘𝚠𝚎𝚛:]' '[:𝚞𝚙𝚙𝚎𝚛:]' < 𝚏𝚒𝚕𝚎.𝚝𝚡𝚝 transforms lowercase to uppercase. Remove duplicates with 𝚝𝚛 -𝚜 '\𝚗'.

🔪 𝗖𝗨𝗧: 𝚌𝚞𝚝 -𝚍 ',' -𝚏 𝟸 𝚏𝚒𝚕𝚎.𝚕𝚘𝚐 can parse fields from delimited logs, making it simple to see specific data columns.

🔨 𝗦𝗘𝗗: 𝚜𝚎𝚍 '𝚜/𝚘𝚕𝚍/𝚗𝚎𝚠/𝚐' 𝚏𝚒𝚕𝚎.𝚕𝚘𝚐 finds and replaces text — 𝚜𝚎𝚍 '/^$/𝚍' 𝚏𝚒𝚕𝚎.𝚕𝚘𝚐 removes empty lines.

🔢 𝗦𝗢𝗥𝗧: 𝚜𝚘𝚛𝚝 𝚏𝚒𝚕𝚎.𝚝𝚡𝚝 sorts text files line by line. For numeric sort, use 𝚜𝚘𝚛𝚝 -𝚗 𝚏𝚒𝚕𝚎.𝚝𝚡𝚝.

🌟 𝗨𝗡𝗜𝗤: 𝚞𝚗𝚒𝚚 -𝚌 𝚏𝚒𝚕𝚎.𝚝𝚡𝚝 counts and removes duplicates. Case-insensitive search can be done using 𝚞𝚗𝚒𝚚 -𝚒 𝚏𝚒𝚕𝚎.𝚝𝚡𝚝.

📃 𝗗𝗜𝗙𝗙: 𝚍𝚒𝚏𝚏 𝚏𝚒𝚕𝚎𝟷.𝚕𝚘𝚐 𝚏𝚒𝚕𝚎𝟸.𝚕𝚘𝚐 compares files line by line, crucial for version differences.

🖋️ 𝗔𝗪𝗞: 𝚊𝚠𝚔 '{𝚙𝚛𝚒𝚗𝚝 $𝟸}' 𝚏𝚒𝚕𝚎.𝚕𝚘𝚐 prints the second word in each line. It’s perfect for text processing scripts, like summarizing logs.

Keep it handy, and you'll find that these commands become second nature as you navigate through your daily tasks.

Learn Python with this Roadmap 🐍

• Basics to Advanced:
Learn the basics of Python, including syntax, variables, data types, conditional statements, type casting, error handling, functions, and advanced topics like list comprehensions, generators, programming paradigms, regular expressions, decorators, iterators, and lambda functions.

• Data Structures and Algorithms:
Study data structures and algorithms, including arrays, linked lists, heaps, stacks, queues, hash tables, binary search trees, recursion, and various sorting algorithms.

• Object-Oriented Programming:
Explore object-oriented programming (OOP) concepts like classes, inheritance, and methods, including the mysterious dunder methods.

• Data Science Exploration:
Venture into data science, mastering popular Python libraries like NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn, TensorFlow, and PyTorch.

• Package Management:
Learn how to manage Python packages using PyPI, pip, and conda.

• Web Frameworks:
Explore popular Python web frameworks like Django, Flask, FastAPI, and Tornado.

• Automation Mastery:
Gain expertise in automation tasks like file manipulation (using OS, shutil, and pathlib), web scraping (using BeautifulSoup and Scrapy), GUI automation (using PyAutoGUI), and network automation.

• Testing Proficiency:
Develop testing skills covering unit testing (using unittest and pytest), integration testing, end-to-end testing (using Selenium and PyAutoGUI), and test-driven development (TDD)

👨‍💻 As an added bonus, we will share the GitHub repo with code samples from the webinar with all registered attendees after the event.

bit.l

Unleashing the Potential of AWS: Delving into the Global Network Infrastructure 🌐

Are you searching for ways to harness the formidable capabilities of AWS to transform your organization's network? Look no further than AWS Transit Gateway, a game-changing solution designed to simplify and elevate your network architecture, especially in scenarios involving intricate setups of multiple AWS accounts and Amazon Virtual Private Clouds (VPCs). Here's a closer look at how AWS Transit Gateway can revolutionize your business:

🚀 Effortless VPC-to-VPC Connectivity Scaling: AWS Transit Gateway seamlessly connects VPCs within the same AWS Region and across AWS Regions, ensuring uninterrupted communication among your workloads.

🌍 Bridging AWS Regions and Spoke Networks: Whether you're utilizing AWS Site-to-Site VPN, AWS Direct Connect, or Transit Gateway Connect, this solution provides the flexibility to link AWS resources across different regions and hybrid networks.

📡 Multicast Support: Certain industries, like financial services and media and entertainment, demand multicast support, which AWS Transit Gateway readily accommodates.

🔗 Secure Access via AWS PrivateLink: Forge secure connections to applications in other VPCs using AWS PrivateLink, ensuring all network traffic remains within the AWS backbone, eliminating the need for an Internet Gateway (IGW).

🏢 Hybrid Connectivity to Data Centers: AWS Transit Gateway caters to two common hybrid connectivity approaches, depending on your organization's objectives. You can migrate assets to the cloud while maintaining short to medium-term hybrid connectivity for data center migration. Alternatively, for long-term hybrid setups with needs like low-latency processing and data residency, consider AWS Outposts for a consistent experience.

🌐 AWS Direct Connect and AWS Site-to-Site VPN: AWS offers two robust methods for hybrid network connections. AWS Direct Connect establishes a private, high-throughput connection between AWS and your data center, enhancing network performance and addressing compliance requirements. AWS Site-to-Site VPN is a swift, encrypted connectivity option with redundancy and failover support when integrated with Transit Gateway.

In an era where network transformation is imperative, AWS Transit Gateway empowers you to construct a robust, scalable, and secure network architecture. Dive into these solutions and elevate your connectivity to new heights. 💼✨

𝐎𝐧𝐞 𝐩𝐨𝐬𝐭 𝐭𝐨 𝐫𝐞𝐟𝐞𝐫 𝐟𝐨𝐫 𝐃𝐞𝐯𝐨𝐩𝐬 𝐑𝐨𝐚𝐝𝐦𝐚𝐩

DevOps, short for Development and Operations, is a set of practices and cultural philosophies that aim to improve collaboration and communication between software development and IT operations teams. The primary goal of DevOps is to streamline the software delivery process, allowing organisations to deliver high-quality software more quickly and efficiently.

DevOps emphasises automation, continuous integration, continuous delivery, and a feedback loop to enable faster and more reliable development and deployment of software.

Here's a roadmap for DevOps:

1. Programming:
Languages: Python, Bash, Ruby, Go, and/or JavaScript.
Version Control: Git (GitHub, GitLab, Bitbucket).

2. Server Administration:
Operating Systems: Linux (Ubuntu, CentOS, Debian).
Configuration Management: Ansible, Puppet, or Chef.
Containerization: Docker.

3. Network Security:
Firewalls: iptables (Linux), pf (BSD), or cloud based firewalls.
VPN: OpenVPN, IPsec.
Security Best Practices: Regular security audits, vulnerability scanning, and pe*******on testing.

4. Servers (Web, Database, Caching):
Web Servers: Apache, Nginx.
Databases: MySQL, PostgreSQL, MongoDB.
Caching: Redis, Memcached.

5. Infrastructure as a Service (IaaS):
Cloud Providers: AWS, Azure, Google Cloud Platform.
Infrastructure Orchestration: Terraform.

6. Continuous Integration/Continuous Deployment (CI/CD):
CI Tools: Jenkins, GitLab CI, Travis CI.
CD Tools: Ansible, Kubernetes, Docker.

7. Monitoring and Logging:
Monitoring Tools: Prometheus, Grafana, Nagios.
Logging: ELK Stack (Elasticsearch, Logstash, Kibana), Splunk.

8. Clouds:
Cloud Services: AWS (EC2, S3, RDS, Lambda), Azure, GCP.
Serverless Computing: AWS Lambda, Azure Functions.

Remember, practical experience and hands on projects are crucial for reinforcing these skills. As you progress, consider working on real-world projects, participating in open-source communities, and continuously staying updated with industry trends. Additionally, certifications in relevant areas can also boost your profile and provide structured learning paths.

Here's a high-level roadmap for becoming a data analyst in 2023 within 3 months, with a dedicated timeline.

1. Develop strong foundational skills in mathematics, statistics, and computer programming.

2. Gain hands-on experience working with data. This can be done through online courses, internships, or working on personal projects.

3. Learn the key tools and technologies used by data analysts such as SQL, spreadsheets, data visualization tools, and data storage solutions like databases or data lakes.

4. Learn a programming language commonly used for data analysis like Python or R.

5. Acquire experience with machine learning algorithms and understand when and how to apply them.

6. Gain expertise in a specific domain or industry to better understand the data and the problems being solved.

7. Build a portfolio of projects that demonstrate your skills and expertise to potential employers.

8. Network with others in the field, attend industry events and conferences, and stay current on industry trends and best practices.

This roadmap, provided by Dhaval Patel Sir on his YouTube channel, codebasics, for becoming a data analyst, includes some free and paid resources and dedicated time slots.

Remember, becoming a data analyst takes time and effort, but the reward of being able to turn data into insights and solutions is well worth it.

Till then keep learning and keep exploring! 🙌

AWS Data Platform Reference Architecture!

In today's data-driven world, organizations need a robust data platform to handle the growing volume, variety, and velocity(3 V’s) of data. A well-designed data platform provides a scalable, secure, and efficient infrastructure for data management, processing, and analysis. It transforms raw data into actionable insights that can inform strategic decision-making, drive innovation, and achieve business objectives.

Let's delve into some key components of this architecture:

✅Centralized Data Repository: Amazon S3 acts as a centralized storage hub for both structured and unstructured data, ensuring durability, availability, and scalability.

✅Streamlined Data Transformation: AWS Glue simplifies the process of extracting, transforming, and loading (ETL) data into usable formats, preparing it for downstream analysis.

✅Powerful Data Analytics: Amazon Redshift, a fully managed data warehouse, supports complex SQL queries on large datasets, enabling organizations to gain deep insights from their data.

✅Efficient Big Data Processing: Amazon EMR, a cloud-native big data platform, handles massive data volumes using frameworks like Hadoop, Spark, and Hive.

✅Real-time Data Streaming: Amazon Kinesis enables real-time ingestion, buffering, and analysis of data streams from various sources, powering real-time applications and insights.

✅Event-driven Automation: AWS Lambda offers serverless computing, executing code in response to events, automating tasks and triggering other services.

✅Simplified Search and Analytics: Amazon Elasticsearch Service provides a managed search and analytics service, making it easy to analyze logs, perform text-based search, and enable real-time analytics.

✅Seamless Data Visualization and Sharing: Amazon Quicksight empowers users to explore and share data insights through interactive visualizations and reports.

✅Automated Data Workflow Orchestration: AWS Data Pipeline automates and orchestrates data-driven workflows across various AWS services, ensuring consistency and simplifying data management.

✅Machine Learning Made Easy: Amazon SageMaker simplifies the process of building, training, and deploying machine learning models for data analysis and predictions.

✅Centralized Metadata Management: The AWS Glue Data Catalog serves as a central repository for metadata, storing information about data sources, transformations, and schemas, facilitating data discovery and management.

✅Data Governance for Quality and Trust: Data governance ensures data quality, security, compliance, and privacy through policies, procedures, and controls, maintaining data integrity and compliance.

Empowering a Data-driven Future
A data platform architecture transforms data into valuable assets, enabling informed decisions and business growth. Organizations can leverage data to shape their future and gain a competitive edge.

Ethical Hacking and Cybersecurity Courses and Resources!

1️⃣ Developing Ethical Hacking Tools with Python:

This course likely focuses on teaching students how to create hacking tools using the Python programming language, with an emphasis on ethical use for security testing and research.

🔗 https://lnkd.in/gFJDKtPk

2️⃣ Ethical Hacking Course - Network Pe*******on Testing:

This course is likely centered around network pe*******on testing, where students learn how to assess and secure computer networks to identify vulnerabilities and protect against cyber threats.

🔗 https://lnkd.in/gMKA2cC4

3️⃣ Ethical Hacking 101: Web App Pe*******on Testing:

This course probably covers the fundamentals of ethical hacking techniques specific to web applications, focusing on testing and securing web-based software for vulnerabilities.

🔗 https://lnkd.in/gYPCf4xF

4️⃣ Web Application Ethical Hacking:

This course likely delves deeper into the field of web application security, teaching students advanced techniques for identifying and addressing vulnerabilities in web apps.

🔗 https://lnkd.in/gJCNZCwt

5️⃣ Ethical Hacking Essentials (EHE):

This course probably provides a comprehensive introduction to the essentials of ethical hacking, covering a wide range of topics and techniques used in ethical hacking.

🔗 https://lnkd.in/gStpeJfY

6️⃣ CNIT 128: Hacking Mobile Devices:

This course likely focuses on the security aspects of mobile devices, teaching students how to assess and secure smartphones and tablets against hacking threats.

🔗 https://lnkd.in/g8YJMeUh

7️⃣ Ethical Hacking Course Certification:

This might be a course that offers certification upon completion, indicating that students have gained the necessary knowledge and skills in ethical hacking.

🔗 https://lnkd.in/gmNRsrb4

8️⃣ Linux Essentials for Ethical Hackers:

This course likely covers the essential Linux skills and knowledge needed for ethical hacking, as Linux is a commonly used platform for security testing and pe*******on testing.

🔗 https://lnkd.in/g5ZSWn28

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Study these 45 problems well and you have prepared for 99% of your System Design Interview:

𝐄𝐚𝐬𝐲
1. Design URL Shortener like TinyURL
2. Design Text Storage Service like Pastebin
3. Design Content Delivery Network (CDN)
4. Design Parking Garage
5. Design Vending Machine
6. Design Distributed Key-Value Store
7. Design Distributed Cache
8. Design Distributed Job Scheduler
9. Design Authentication System
10. Design Unified Payments Interface (UPI)

𝐌𝐞𝐝𝐢𝐮𝐦
11. Design Instagram
12. Design Tinder
13. Design WhatsApp
14. Design Facebook
15. Design Twitter
16. Design Reddit
17. Design Netflix
18. Design Youtube
19. Design Google Search
20. Design E-commerce Store like Amazon
21. Design Spotify
22. Design TikTok
23. Design Shopify
24. Design Airbnb
25. Design Autocomplete for Search Engines
26. Design Rate Limiter
27. Design Distributed Message Queue like Kafka
28. Design Flight Booking System
29. Design Online Code Editor
30. Design Stock Exchange System
31. Design an Analytics Platform (Metrics & Logging)
32. Design Notification Service
33. Design Payment System

𝐇𝐚𝐫𝐝
34. Design Location Based Service like Yelp
35. Design Uber
36. Design Food Delivery App like Doordash
37. Design Google Docs
38. Design Google Maps
39. Design Zoom
40. Design File Sharing System like Dropbox
41. Design Ticket Booking System like BookMyShow
42. Design Distributed Web Crawler
43. Design Code Deployment System
44. Design Distributed Cloud Storage like S3
45. Design Distributed Locking Service

You can find the links to study these problems in this GitHub repo: https://lnkd.in/gSv3GeKZ