Computer science self-learning guide

read this and write a long form blog post with each of these youtube channels linked and a description of each subject they are an expert at and what they teach is accompanying each as well as an article about how to teach yourself all the topics you need in computer science -- so first create a list of the topics and the corresponding youtube channel to the right of each topic in the image and use that list to write the blog post ---

Here’s a very extensive blog-style post based on *your image* + added commentary + a self-learning roadmap, which you can use (and adapt) as your guide to teaching yourself the core topics in computer science. --- ## The list of topics & YouTube channels Below is the list of topics (on the left) and the recommended YouTube channel (on the right) that corresponds to each topic in the image: | Topic | YouTube channel | |-------|-----------------| | Java | entity["youtube_channel", "Neso Academy", 0] | | Python | entity["youtube_channel", "Corey Schafer", 0] | | SQL | entity["youtube_channel", "Joey Blue", 0] | | MS Excel | entity["youtube_channel", "ExcelIsFun", 0] | | Maths for AI | entity["youtube_channel", "Simplilearn", 0] | | Blockchain | entity["youtube_channel", "Telusko", 0] | | Machine Learning | entity["youtube_channel", "Krish Naik", 0] | | Cybersecurity | entity["youtube_channel", "NetworkChuck", 0] | | Web Development | entity["youtube_channel", "Code With Harry", 0] | | Linux | entity["youtube_channel", "Programming Knowledge", 0] | | DevOps | entity["youtube_channel", "Kunal Kushwaha", 0] | | Computer Networks | entity["youtube_channel", "David Bombal", 0] | | DSA (Data Structures & Algorithms) | entity["youtube_channel", "Jenny’s Lectures CS IT", 0] | --- ## This section: Channel by channel – what the channel teaches, what the subject covers Below I’ll go through each topic + channel, giving a description: what the subject is, what you can expect to learn from that channel, and some commentary. ### 1. Java → Neso Academy The subject: Java is a widely-used object-oriented programming language, used for building enterprise software, Android apps, backend systems, etc. It also serves as a core building block for understanding programming, OOP concepts, memory management (to some extent), garbage collection, and more. What Neso Academy teaches: Their YouTube channel offers structured lectures on Java programming, as well as broader computer-science topics (data structures, algorithms, computer architecture). For example, they have an “Introduction to Java Programming” video. citeturn0search23turn0search0 They also cover things like data structures and compiler design. citeturn0search15 So if you use this channel for Java, you’re getting both the language itself *and* a decent foundation in the underlying CS principles. How to use it: Start with a Java basics playlist (variables, control flow, classes, OOP). After that, move into more advanced topics (collections, threads, streams). Then use Java to implement smaller projects (e.g., a console app, a GUI app, a simple web server). Because you’re using Java, you’ll develop discipline with typed languages, object design, modular structure, etc. ### 2. Python → Corey Schafer The subject: Python is a high-level, dynamically-typed programming language very popular for scripting, automation, web development (Django/Flask), data science, machine learning, and more. It’s great for rapid development, prototyping, and also for learning programming fundamentals. What Corey Schafer teaches: His YouTube channel focuses on Python programming with clarity. For example his playlist “Python Programming Beginner Tutorials” covers installation, data types, loops, functions, modules, etc. citeturn1search17turn1search0 People in the Reddit community mention: > “Corey Schafer is the most recommended YouTube channel for beginners.” citeturn1search9 So he’s good if you want clean, well-explained Python tutorials. How to use it: Use Corey’s channel to learn Python fundamentals. Then pick up Python libraries relevant to your goals (web, automation, data). Use small projects: script something, build a web app, analyze some data. Because Python is flexible, you can use it as a gateway to many CS domains (web, ML, scripting, even system-level tasks). You might also use Python to prototype algorithms you’ll later translate into tougher languages like Java or C++ (if you go that route). ### 3. SQL → Joey Blue The subject: SQL (Structured Query Language) is the standard language for querying and manipulating relational databases. It’s a must-know for backend, data engineering, analytics, and any application that uses data storage. What Joey Blue teaches: According to summary sources, Joey Blue provides SQL tutorials that are “practical, easy to follow, and highly useful for anyone working with databases.” citeturn1search1turn1search3 Though I did not dig into many videos, his channel is well recommended for SQL learning. How to use it: Learn the syntax of SQL: SELECT, JOIN, GROUP BY, subqueries, indexes, transactions. Use an open-source database like PostgreSQL or MySQL. Build small projects: e.g., store data and query it, build a little CRUD app that uses SQL. Make sure you understand database design (normalization), indexing, and query optimization. SQL is a foundational skill for many roles (backend developer, data engineer, data analyst). It pairs nicely with your programming skills (Java/Python). ### 4. MS Excel → ExcelIsFun The subject: Microsoft Excel may seem more “businessy” than “computer science”, but actually mastery of Excel is hugely valuable for data analysis, prototyping, quick calculations, building dashboards, and even for automating tasks with formulas and macros. For a computer-science-inspired learner, Excel is useful especially if you’re doing data-oriented work. What ExcelIsFun teaches: The channel’s name says it: it provides everything you need to master Excel formulas, functions, pivot tables, and more. It is widely referenced in lists of top educational YouTube channels. citeturn1search5turn0search8 How to use it: Use ExcelIsFun to learn core Excel skills: cell basics, formulas, functions (SUM, VLOOKUP, INDEX/MATCH, etc), pivot tables, charting, maybe macros (VBA). Then apply to a small dataset (download publicly available CSV) and do some exploratory data analysis in Excel. The key is that Excel builds your fluency with data, tabular thinking, and quick prototyping. It complements your programming/database work rather than replacing it. ### 5. Maths for AI → Simplilearn The subject: When you move into machine learning, AI, advanced algorithms, you’ll need a good grounding in mathematics: linear algebra, calculus, statistics, probability, optimisation, discrete maths. This category “Maths for AI” addresses that. What Simplilearn teaches: Simplilearn is a large online training provider; their YouTube channel includes beginner-friendly guides to AI math concepts. According to aggregated sources, it’s used in lists of best YouTube channels for tech skills. citeturn0search5 How to use it: Use Simplilearn (or similar) to build your math foundation. Spend time learning vector/matrix operations, eigenvalues, derivatives, integrals, probability distributions, hypothesis testing, random variables. Then apply by implementing small ML algorithms from scratch (e.g., linear regression coded yourself). The point is: even if you don’t want to become a mathematician, this layer will distinguish you when you apply ML or advanced CS topics. ### 6. Blockchain → Telusko The subject: Blockchain technology underpins cryptocurrencies (Bitcoin, Ethereum), but also has broader applications (supply chain, smart contracts, decentralised apps). If you’re exploring cutting-edge or tangential computer science fields, blockchain is interesting. What Telusko teaches: The channel Telusko offers tutorials that simplify blockchain concepts with practical coding examples. It appears on multiple “top channels” lists. citeturn0search8turn1search1 How to use it: Use Telusko’s content to learn what a blockchain is, how consensus works, how smart contracts work (e.g., Solidity), and perhaps build small decentralised applications. While this may not be mandatory for every CS path, it’s a valuable niche skill if you’re interested in fintech, decentralised systems, or exploring advanced CS ecosystems. Use a small project (e.g., build a simple smart contract, or simulate a blockchain) to cement your learning. ### 7. Machine Learning → Krish Naik The subject: Machine Learning (ML) involves building algorithms that learn patterns from data rather than being explicitly programmed. ML is central to modern AI, recommendation systems, autonomous systems, data science, etc. What Krish Naik teaches: Krish Naik is a popular YouTube educator in ML/data science, focusing on project-based tutorials, case studies, deep insights. According to sources, he is ideal for ML learners. citeturn1search1turn1search5 How to use it: After building your math foundation and programming skills (Python likely), come to Krish’s channel to see how ML algorithms are implemented (supervised, unsupervised, reinforcement learning). Have a dataset, pick an algorithm, implement it end-to-end (data cleaning → feature engineering → model → evaluation). Then try varied projects (image classification, NLP, recommendation system). Use Python libraries (scikit-learn, TensorFlow/PyTorch) but always aim to understand what’s going on under the hood. ### 8. Cybersecurity → NetworkChuck The subject: Cybersecurity covers protecting systems, networks and data from threats, implementing secure architecture, learning penetration testing, ethical hacking, cryptography, defence strategies. For anyone in CS, some understanding of security is extremely wise. What NetworkChuck teaches: NetworkChuck offers fun, practical, engaging lessons on cybersecurity topics (ethical hacking, network fundamentals, certifications like CompTIA). According to social commentary, his content is lively and useful. citeturn1search8 How to use it: Use NetworkChuck to build awareness of security principles: what are threats, how networks can be compromised, secure coding practices, encryption, authentication. Then apply: set up a lab (virtual machines), attempt safe hacking of your own test systems, learn tools (Wireshark, Metasploit, Nmap). This teaches you to think adversarially, which enhances your CS mindset. ### 9. Web Development → Code With Harry The subject: Web development is building websites and web applications — front-end (HTML, CSS, JS) + back-end (databases, server logic, APIs) + deployment. It’s one of the most practical, widely-applied domains in CS. What Code With Harry teaches: This channel is popular especially in the Indian / Hindi-speaking audience, but teaches full‐stack web development: HTML, CSS, JavaScript, frameworks, backend, etc. It features “comprehensive guides for building responsive websites”. citeturn1search5turn0search8 How to use it: Use Code With Harry to learn front-end basics (HTML, CSS, JS). Then move to a backend (Node.js, Django, etc). Build full-stack projects: e.g., a to-do-app, blog engine, e-commerce prototype. Deploy it (e.g., to cloud). This makes you “product‐ready”. Keep pairing this with your database (SQL), programming (Python/Java) and optionally DevOps skills. ### 10. Linux → Programming Knowledge The subject: Linux (and UNIX-like systems) are core to modern computing: servers, cloud, embedded systems, dev environments all often run Linux. Having comfort with Linux commands, shell scripting, file permissions, process management, system internals is a big advantage. What Programming Knowledge teaches: The channel offers a variety of tutorials on programming topics, but notably has thorough Linux tutorials from beginner to advanced. According to aggregated sources, this channel is recommended for Linux learning. citeturn0search8turn1search10 How to use it: Use the channel to learn Linux basics: shell commands (ls, grep, sed, awk), file system, permissions, processes, networking, users/groups. Then go deeper: shell scripting, cron jobs, services, logs, system monitoring. Try installing Linux in a VM and using it as your daily system. Use it for your programming projects, web hosting, etc. This strengthens your systems understanding and complements other CS topics. ### 11. DevOps → Kunal Kushwaha The subject: DevOps is the practice of combining software development (Dev) + IT operations (Ops) to enable frequent, reliable software delivery. It covers CI/CD pipelines, infrastructure as code (Terraform/Ansible), containers (Docker), orchestration (Kubernetes), monitoring, logging, etc. What Kunal Kushwaha teaches: His channel is known for in-depth lessons on modern DevOps practices and tools. The “top channel lists” mention him for DevOps. citeturn1search5 How to use it: Once you have programming (Python/Java) + web/app + system (Linux) knowledge, introduce DevOps. Use the channel to learn containerization (Docker), orchestration (Kubernetes), continuous integration (GitHub Actions, Jenkins), Infrastructure as Code. Build a project: e.g., containerise your web app, deploy to Kubernetes cluster, set up CI/CD pipeline, monitor it. This gives you a “full lifecycle” view of software, which is very market-relevant. ### 12. Computer Networks → David Bombal The subject: Computer Networks deals with how computers communicate: protocols (TCP/IP), routing, switching, network topologies, OSI model, LAN/WAN, wireless, network security. This is foundational knowledge for distributed systems, cloud, infrastructure, cybersecurity. What David Bombal teaches: David Bombal’s channel focuses on networking fundamentals, certifications, practical labs (Cisco, GNS3, etc). In lists of recommended channels, he is featured for networking. citeturn1search5 How to use it: Use the channel to learn the OSI model, TCP/IP layers, subnetting, routing/switching, wireless networks, network troubleshooting. Set up a lab (virtual routers, switches, Wireshark). Understand how network protocols work under the hood. This adds depth to your systems perspective in CS, so when you build web apps or distributed systems you know how they actually communicate. ### 13. DSA (Data Structures & Algorithms) → Jenny’s Lectures CS IT The subject: Data Structures & Algorithms (DSA) is core computer science. It covers how to structure and organise data (arrays, linked lists, trees, graphs, heaps) and algorithms (searching, sorting, dynamic programming, greedy, backtracking). Mastering DSA is crucial for efficient programming, performance, and technical interviews. What Jenny’s Lectures CS IT teaches: This channel is frequently recommended for DSA tutorials, especially to students preparing for programming interviews and competitive coding. citeturn1search8turn0search5 How to use it: Use the channel to learn each data structure (array, stack, queue, tree, graph) and algorithmic pattern (recursion, divide & conquer, DP). Alongside video lessons, pick problems (LeetCode, HackerRank) and solve them. Regularly practise. DSA is what bridges “can code” to “can code efficiently and correctly”. It’s a long-haul but super important. --- ## How to teach yourself *all* these topics in computer science Having this list is great, but you might ask: _“How do I orchestrate self-learning across all these topics so that I build a coherent CS skill-set?”_ Below is a guide/roadmap to teach yourself computer science (CS) using these channels and topics, at your own pace. ### Step 0: Define your goal & timeline Before diving, ask yourself: Why are you doing this? Do you want to become a software developer, data scientist, systems engineer, network engineer, cybersecurity specialist? Your goal will shape which topics you prioritise. Also set a realistic timeline: e.g., 12 months, 18 months, 2 years. Break it into phases. ### Phase 1: Build programming & fundamentals Duration: ~1-3 months - Choose a starting programming language (Python is great for beginners). Use Corey Schafer’s channel to learn fundamentals: variables, control flow, functions, modules. - Then pick a typed/OO language (Java) to deepen understanding of objects, classes, typing, memory, etc. Use Neso Academy’s Java material. - Parallel: Install Linux (via VM or dual-boot). Use Programming Knowledge to learn basic Linux commands and shell. This gives you a “toolbelt” environment. - Also add SQL basics: use Joey Blue’s tutorials to learn database querying. Build a small database of something and query it. ### Phase 2: Web development + systems + data Duration: ~3-6 months - Use Code With Harry to learn front-end (HTML, CSS, JS) and backend (choose one: Node.js, Django/Flask). Build a simple web app (e.g., blog, to-do list). - Connect that web app to a database (SQL). Use your SQL knowledge. - Deploy the app on Linux (your VM or cloud). Use Linux skills. - Meanwhile, start computer networks basics (David Bombal). Understand how your app is served, what happens on the wire. - Also begin security awareness (NetworkChuck). Learn secure coding practices, what vulnerabilities exist (e.g., SQL injection, XSS). ### Phase 3: Data orientation + algorithms Duration: ~3-6 months - Use ExcelIsFun to work with small datasets, do analyses, build dashboards. This gives you quick data-fluency. - Meanwhile, study DSA (Jenny’s Lectures). Learn fundamental data structures and algorithms. Solve problems regularly. - For algorithms that are data-oriented, switch to Python or Java to implement them. - Begin “Maths for AI” (Simplilearn) to prepare for advanced topics, even if you’re not yet diving into ML. ### Phase 4: Advanced / specialisation Duration: ~6-12 months (depending on your goal) - If you are leaning into data science / ML: go deep into Krish Naik’s channel. Build ML projects, implement models, work with real-world data. Use the maths you learned. - If you're leaning into distributed systems / DevOps: use Kunal Kushwaha’s channel to learn containers, CI/CD, infrastructure as code. - If you are leaning into blockchain: use Telusko to explore smart contracts, decentralised apps, ledger systems. - If you are leaning into networking/security: deepen your knowledge with David Bombal and NetworkChuck. Build labs, attempt certifications. - Across all these, continue practising DSA— it’s a skill that underpins almost every technical domain. ### Step 5: Build projects + portfolio Whatever your path, build tangible projects. For example: - A web app deployed via DevOps pipeline, using Docker + Kubernetes, running on Linux, with a backend in Java or Python, with database in SQL, with some data analytics in Excel, with network configuration understood. - A data science/ML project: gather dataset, clean it in Python, build model, visualise results in Excel or Python, deploy model as a web service. - A blockchain mini-app: smart contract, frontend, backend, deployment, maybe networked nodes. - A network/security lab: simulate attacks/defences, capture packets, log traffic, visualise threats. Make sure your GitHub portfolio shows your code, your process, your understanding—not just “I finished a tutorial”. ### Step 6: Interview / job readiness - Keep revising DSA (Jenny’s Lectures) because many interviews focus on that. - Revise your fundamentals (programming, language, data structures). - Prepare domain-specific questions (for your path: ML, web, DevOps, security). - Practice system design (if going into software engineering) — your networks + systems knowledge will help. - Build strong resume + LinkedIn + portfolio. Show the projects and what you built. ### Continuous habits - Weekly: set aside dedicated learning time (2-4 hours) and practise consistently. - Monthly: reflect on what you’ve done, what’s next. - Build a community: find peers, join forums (Reddit r/learnprogramming, etc). Use YouTube comments, Slack/Discord groups. - Teach someone else (blog about what you learned, record a small video). Teaching deepens your learning. - Stay up to date. CS is changing fast. Use the channels to follow new content. ### Prioritisation tip If you feel overwhelmed, use this prioritisation: 1. Programming (Python + Java) 2. Data structures & algorithms 3. Web development / app development 4. Systems & Linux + networking 5. Databases (SQL) 6. Choose specialisation (ML/data science, DevOps/infrastructure, security/networks, blockchain) 7. Build projects + practise interviews --- ## Final thoughts By combining these carefully selected YouTube channels, you essentially have a **free, high-quality curriculum** covering much of what one might get in a formal computer science programme — plus many modern extras (web dev, ML, DevOps, blockchain). The key is *self-discipline* and *project-based practice*. Watching videos is not enough; you must code, build, fail, iterate, and reflect. If you follow the roadmap above and regularly apply what you learn, you’ll emerge not just with theory but with **skills you can show—and use** in internships, jobs, personal projects. Would you like me to **generate a clickable list** of those YouTube channel URLs + a **suggested six-month schedule** (week by week) for one of the tracks (say “full-stack web dev” or “data science with ML”)?

Now taking that output I want you to write a prompt I am going to give an LLM which will take what you just output and rewrite it all into one comprehensive blog post about how to teach yourself programming with youtube -- the prompt should include all of the best ideas for SEO that need to be thought of in order to really make the post popular and include things like the metadata which is needed for the post as well in the output of the final prompt

Here’s a prompt you can copy and give to any LLM to generate a fully SEO-optimized long-form blog post (based on the content above) about teaching yourself programming using YouTube. --- ### ✅ **Prompt for LLM — Generate SEO-Optimized Blog Post** You are an expert technical writer, SEO strategist, and blogger. Your task is to take the content below (about learning programming and computer science from YouTube channels) and transform it into **one comprehensive, engaging, highly SEO-optimized blog post** titled: **“How to Teach Yourself Programming and Computer Science Using YouTube (Step-by-Step Roadmap + Best Channels)”** #### **What You MUST Include in the Output:** **1. SEO Meta Elements:** - Meta Title (≤ 60 characters, high click-through potential) - Meta Description (≤ 155 characters, highly compelling and keyword-rich) - URL Slug suggestion (SEO-friendly) - Suggested Featured Image idea + Alt Text (SEO-ready) - 5–10 SEO Keywords + 5 Long-Tail Keywords **2. Blog Post Requirements:** - Word count: 2,000–3,000 words - Tone: Motivational, educational, clear, and beginner-friendly - Use structured headings with H1, H2, H3, H4 where appropriate - First paragraph must hook the reader emotionally (why learning to code from YouTube works, how many people feel stuck or overwhelmed) - Include a clickable Table of Contents - Use bullet lists, numbered steps, comparison tables if needed - Must integrate **all topics from the list**: - Java (Neso Academy) - Python (Corey Schafer) - SQL (Joey Blue) - MS Excel (ExcelIsFun) - Maths for AI (Simplilearn) - Blockchain (Telusko) - Machine Learning (Krish Naik) - Cybersecurity (NetworkChuck) - Web Development (CodeWithHarry) - Linux (ProgrammingKnowledge) - DevOps (Kunal Kushwaha) - Computer Networks (David Bombal) - DSA (Jenny’s Lectures) - Must include the complete self-learning roadmap: - Phase 1: Programming fundamentals - Phase 2: Web + Systems - Phase 3: Data + Algorithms - Phase 4: Specializations - Phase 5: Projects & Portfolio - Phase 6: Job readiness - Include real actionable steps, timelines, and tips **3. SEO Enhancements to Apply:** - Use internal linking placeholders like: `[Insert link to beginner Java article here]` - Use external authority sources where appropriate - Add FAQ Section at the end with schema-ready Q&A format (JSON-LD not needed, but questions & answers should be detailed) - Use keyword-rich subheadings like “Best YouTube Channels to Learn Java for Free” - Include a short conclusion + call to action (“Start today”, newsletter, etc.) **4. Writing Style Guidelines:** - No plagiarism. Fully rewrite content in your own words while keeping the same information. - Sentences should be clear, active voice, and easy to digest. - Avoid overuse of passive voice or technical jargon without explanation. - Provide inspiration and confidence to self-learners. - Speak to the reader directly (“you”). --- ### **Content to Use and Transform:** Use the detailed descriptions, YouTube channels, and self-learning roadmap provided in the previous output. You must incorporate ALL ideas into a single cohesive blog post. --- **Your final response should ONLY contain the metadata + the fully written blog post. Do NOT include instructions or side notes.** --- Would you like me to automatically run this prompt for you and generate the blog post?