CyberCode Academy

CyberCode Academy
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Welcome to CyberCode Academy — your audio classroom for Programming and Cybersecurity. 🎧 Each course is divided into a series of short, focused episodes that take you from beginner to advanced level — one lesson at a time. From Python and web development to ethical hacking and digital defense, our content transforms complex concepts into simple, engaging audio learning. Study anywhere, anytime — and level up your skills with CyberCode Academy. 🚀 Learn. Code. Secure. You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

  1. 3 giờ trước

    Course 40 - Web Scraping with Python | Episode 27: Beautiful Soup Parsing and Scrapy Project Architecture

    You’ve essentially built a full end-to-end curriculum covering web scraping → parsing → dynamic rendering → large-scale crawling → security context. If we compress all of your episodes into a single structured roadmap, it becomes a clear “from zero to production scraping engineer” path like this:🧭 Web Scraping & Data Extraction — Full Structured Roadmap1. Web Foundations (How the Internet Actually Works)You start by understanding what you’re scraping. HTTP request/response lifecycle (GET, POST, PUT, DELETE)Status codes (200, 404, 500)Headers, user-agent behavior, redirectsURL anatomy (query strings, fragments, encoding)➡️ Outcome: You understand how data moves before you even touch scraping tools.2. Basic Scraping (Fetching Data)Core Tools requests (modern standard)urllib, httplib2 (lower-level alternatives)Skills Downloading HTML pagesHandling redirects & timeoutsSetting headers (User-Agent spoofing)Parsing JSON responses from APIs➡️ Outcome: You can reliably retrieve raw web content programmatically.3. Parsing HTML (Turning Pages into Data)Core Library: Beautiful Soup Beautiful SoupYou learn how HTML becomes a navigable tree: Tags, attributes, navigable strings, commentsDOM / parse tree structure.find(), .find_all()CSS classes, IDs, attribute filteringRegex-based matchingNavigation Parent / child / sibling traversal.contents, .descendants.next_element vs .next_sibling➡️ Outcome: You can extract precise data from any static page.4. Advanced Beautiful Soup EngineeringYou move from “scraping” to “data engineering on HTML”: Custom filter functions (Python-powered selectors)Regex + attribute logic filteringSoupStrainer (performance optimization)Encoding & Unicode handlingOutput formatting & HTML rewritingHTML manipulation capabilities: Insert / delete / replace nodesWrap / unwrap elementsClone and restructure trees➡️ Outcome: You can not only extract data—but reshape web pages programmatically.5. XPath + CSS Selectors (Professional Querying Layer)Tools: XPath (tree-path querying)CSS selectors (via SoupSieve)You learn: //, /, attribute filters in XPathID (#), class (.), hierarchy selectorssibling selectors (+, ~)regex-based CSS matchingindexing and scoped searches➡️ Outcome: You can query HTML like a database.6. Scrapy Framework (Industrial Scraping System)Core Framework: Scrapy ScrapyThis is the shift from scripts → systems.Architecture: Engine (orchestration layer)Spiders (your logic)Scheduler (queue system)Downloader (HTTP handling)Pipelines (data processing)Features: Async crawling (Twisted engine)Concurrency + throttling controlBuilt-in request lifecycle management➡️ Outcome: You can build scalable scraping systems, not just scripts.7. Scrapy Project EngineeringYou learn full production structure: startproject, genspidersettings.py configurationitems.py (structured schemas)pipelines.py (cleaning + validation)scrapy crawl executionData flow:Spider → Item → Pipeline → Export (CSV/DB)➡️ Outcome: You build maintainable data pipelines like real systems.8. Scrapy Shell & Prototyping Interactive selector testingLive URL inspectionDebugging selectors before writing spidersHandling 403 via user-agent tweaking➡️ Outcome: Faster development + fewer broken spiders.9. Dynamic Web Scraping (JavaScript-Rendered Sites)Problem:HTML ≠ final page (JS modifies DOM)Solutions: Selenium SeleniumRequests-HTML / headless renderingTechniques: Wait conditions (explicit/implicit waits)DOM inspection via DevToolsSimulating real browser behavior➡️ Outcome: You can scrape modern interactive websites.10. API & HTTP Deep Control Layer Advanced request types (OPTIONS, HEAD)Redirect tracingError handling (403, 429, DNS failures)URL parsing with urllib➡️ Outcome: You can interact with websites at protocol level.11. Security, Ethics & Risk Layer Scraping vs crawling vs hackingLegal boundaries (ToS, CFAA, DMCA)Rate limits and bansData ownership risksPublic vs private data distinction➡️ Outcome: You understand what should be scraped, not just what can be scraped.12. Advanced Extraction Techniques Regex engineering for structured dataTable scraping (Wikipedia-style datasets)CSV/DataFrame transformationCleaning pipelines (pandas integration)➡️ Outcome: Raw HTML → clean datasets ready for analysis.🧠 Final PictureWhat you’ve built here is a full stack:HTTP → Parsing → Extraction → Automation → Scaling → Security → Data EngineeringIn other words: Requests = fetch layerBeautiful Soup = parsing layerXPath/CSS = querying layerSelenium = dynamic rendering layerScrapy = orchestration + scaling layer You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

    Course 40 - Web Scraping with Python | Episode 27: Beautiful Soup Parsing and Scrapy Project Architecture

  2. 1 ngày trước

    Course 40 - Web Scraping with Python | Episode 26: Framework Overview and Core Architecture

    In this lesson, you’ll learn about: what makes Scrapy a framework (not just a library), how its asynchronous engine works, and how its core components cooperate to deliver fast, scalable web scraping1. Library vs Framework (Core Concept)🔹 Who Controls the Flow?🔹 Key Difference Library → you call it when neededFramework → it calls your code👉 Key Insight Scrapy is a framework because it controls execution (Inversion of Control)2. Asynchronous Power (Why Scrapy is Fast)🔹 Event-Driven Architecture🔹 What Makes It Powerful Uses event-driven networkingHandles many requests simultaneouslyDoesn’t wait (non-blocking I/O)👉 Key Insight Scrapy doesn’t scrape pages one-by-one—it handles many at once3. Scrapy Architecture (Big Picture)🔹 How Components Interact4. Core Components Explained🔹 1. Engine Central controllerManages request/response flow🔹 2. Spiders Your custom logicExtract data from responsesdef parse(self, response): return {"title": response.css("title::text").get()} 🔹 3. Scheduler Queues requestsDecides what to crawl next🔹 4. Downloader Sends HTTP requestsRetrieves web pages🔹 5. Item Pipeline Cleans dataValidates dataSaves data (DB, CSV, etc.)👉 Key Insight Each component has one responsibility → modular & scalable5. Request Flow (Step-by-Step) Spider sends requestEngine forwards to SchedulerScheduler queues itDownloader fetches pageResponse returns to SpiderData sent to Pipeline👉 This loop continues asynchronously for thousands of requests6. Fine-Grained Control🔹 Performance Tuning🔹 Key Controls Limit concurrent requestsControl request delaysEnable auto-throttling🔹 Example SettingsCONCURRENT_REQUESTS = 16 DOWNLOAD_DELAY = 1 AUTOTHROTTLE_ENABLED = True 👉 Key Insight Speed without control = getting blocked7. Why Scrapy is Production-Ready ⚡ High performance (async)🔄 Fault-tolerant (handles failures)🧱 Modular architecture🎯 Precise data pipelines8. Mental ModelThink of Scrapy as a factory: 🏭 Engine → manager🕷 Spider → worker extracting data📦 Scheduler → task queue🌐 Downloader → fetcher🧹 Pipeline → cleaner & packagerFinal TakeawayScrapy isn’t just a tool—it’s a complete scraping system.You gain: Massive speed via asynchronous processingClean architecture for scalingFull control over performance and behavior👉 That’s why Scrapy is used for large-scale, professional-grade data extraction You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

    Course 40 - Web Scraping with Python | Episode 26: Framework Overview and Core Architecture

  3. 2 ngày trước

    Course 40 - Web Scraping with Python | Episode 25: Core Concepts and Legal Guidelines

    In this lesson, you’ll learn about: the foundations of web scraping with Python and Scrapy, the difference between crawling and scraping, and the legal boundaries you must understand before building any data extraction system1. Technical Prerequisites🔹 What You Need to Know FirstBefore diving into scraping, you should be comfortable with: Python → scripting & automationHTML → page structure (DOM)CSS → selectors for targeting elements👉 Key Insight Scraping is not just coding—it’s understanding how the web is structured2. Crawling vs Scraping🔹 Understanding the Core Difference🔹 Crawling Large-scale page discoveryIndexing entire websitesUsed by search engines🔹 Scraping Extracts specific dataTargeted and focusedUsed for analysis, automation, insights👉 Key Insight Crawling = exploring Scraping = extracting3. Legal & Ethical Considerations🔹 The Risk Landscape🔹 What Can Go Wrong 🚫 IP bans / blocking⚠️ Cease & desist letters⚖️ Lawsuits🔹 Key Laws to Be Aware Of Computer Fraud and Abuse Act (CFAA)Digital Millennium Copyright Act (DMCA)👉 Key Insight Just because you can scrape doesn’t mean you should4. Terms of Service (ToS) MatterEvery website defines rules in its Terms of Service: May explicitly forbid scrapingMay limit automated accessMay require permission or API usage👉 Ignoring ToS can lead to: Account terminationLegal escalationPermanent bans5. Common Misconceptions (Debunked)❌ “It’s public, so it’s free to use”→ Not true. Public visibility ≠ legal permission❌ “Bots are the same as humans”→ False. Automated access is treated differently❌ “Everyone scrapes, so it’s fine”→ Risk still applies regardless of popularity👉 Key Insight Intent does not override legality6. Safe Scraping Practices🔹 How to Stay Compliant ✅ Always request written permission✅ Check robots.txt✅ Respect rate limits✅ Prefer official APIs when available👉 Rule of Thumb If it’s not your data → get permission first7. Mental ModelThink of scraping as: 🧠 Technical skill → extracting data⚖️ Legal responsibility → respecting ownership🤝 Ethical practice → not abusing systemsFinal TakeawayWeb scraping is powerful—but it exists in a legal gray zone if misused.To operate safely and professionally: Understand the difference between crawling and scrapingRespect Terms of Service and lawsAlways seek permission when working with third-party data👉 That’s what separates a skilled engineer from a risky operator You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

    Course 40 - Web Scraping with Python | Episode 25: Core Concepts and Legal Guidelines

  4. 3 ngày trước

    Course 40 - Web Scraping with Python | Episode 24: Mastering Advanced Operations, Parsers, and Encodings in Beautiful Soup

    In this lesson, you’ll learn about: optimizing Beautiful Soup for speed and memory, handling encodings safely, managing tags precisely, and controlling how your final HTML output is generated1. Choosing the Right Parser (Performance Matters)🔹 Parser Comparison🔹 Common ParsersBeautifulSoup(html, "lxml") BeautifulSoup(html, "html.parser") BeautifulSoup(html, "html5lib") 🔹 Differences lxml → fastest, tolerant of broken HTMLhtml.parser → built-in, moderate speedhtml5lib → most accurate (browser-like), slowest👉 Key Insight Use lxml for speed, html5lib for accuracy2. Selective Parsing with SoupStrainer🔹 Parse Only What You Need🔹 Examplefrom bs4 import SoupStrainer only_links = SoupStrainer("a") soup = BeautifulSoup(html, "lxml", parse_only=only_links) 👉 Key Insight Avoid parsing the whole document → save memory + increase speed3. Handling Encodings & Unicode🔹 Clean Text Across Languages🔹 Automatic Handling Converts everything to Unicode internallyDetects encoding via 🔹 Manual Fixsoup = BeautifulSoup(html, "lxml", from_encoding="utf-8") 👉 Key Insight Wrong encoding = broken text (especially non-English content)4. Tag Comparison & Copying🔹 Understanding Equality🔹 Structural vs Memory Equalitytag1 == tag2 # same structure tag1 is tag2 # same object in memory 🔹 Copying Tagsimport copy new_tag = copy.copy(tag) 👉 Key Insight Copy tags when modifying → avoid breaking original data5. Output Formatting Control🔹 Converting Back to HTML🔹 Basic Outputstr(soup) 🔹 Custom Formatterdef upper(text): return text.upper() soup.prettify(formatter=upper) 🔹 Formatter Options "html" → standard HTML"html5" → HTML5-compliantCustom function → full control👉 Key Insight You control how scraped data is presented and transformed6. Mental ModelThink of advanced scraping optimization as: ⚡ Parser → speed vs accuracy🎯 SoupStrainer → efficiency🌍 Encoding → correctness🧠 Tag handling → safety🧾 Output → final polishFinal TakeawayAt this level, scraping becomes engineering-grade data processing.You are not just extracting data—you are: Optimizing performancePreserving data integritySafely manipulating structuresProducing clean, standardized output👉 This is what transforms scraping into a reliable, production-ready pipeline You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

    Course 40 - Web Scraping with Python | Episode 24: Mastering Advanced Operations, Parsers, and Encodings in Beautiful Soup

  5. 4 ngày trước

    Course 40 - Web Scraping with Python | Episode 23: Mastering HTML Parse Tree Modification with Beautiful Soup

    In this lesson, you’ll learn about: how to edit, expand, and restructure HTML using Beautiful Soup—turning a static document into a fully dynamic, modifiable data structure1. Editing Existing Elements🔹 Modifying Tags, Attributes, and Text🔹 Rename Tagstag.name = "newtag" 🔹 Update Attributestag["class"] = "updated-class" del tag["class"] 🔹 Modify Texttag.string = "Updated text" 👉 Key Insight Every HTML element is mutable—you can fully rewrite it2. Adding New Content🔹 Expanding the Tree🔹 Append & Extendtag.append("New text") tag.extend(["More text", "Another"]) 🔹 Insert at Positiontag.insert(1, "Inserted text") 🔹 Insert Around Elementstag.insert_before("Before") tag.insert_after("After") 👉 Key Insight You control where new content appears (inside or beside elements)3. Creating New Elements🔹 Building from Scratch🔹 Create New Tagnew_tag = soup.new_tag("div") 🔹 Create Text Nodefrom bs4 import NavigableString text = NavigableString("Hello") 🔹 Create Commentfrom bs4 import Comment comment = Comment("This is a comment") 👉 Key Insight You’re not limited to existing HTML—you can generate entirely new structures4. Removing Elements🔹 Deleting vs Extracting🔹 Extract (Keep in Memory)removed = tag.extract() 🔹 Decompose (Destroy Completely)tag.decompose() 🔹 Clear Content Onlytag.clear() 👉 Key Insight extract() → temporary removaldecompose() → permanent deletion5. Structural Refactoring🔹 Changing the Tree Layout🔹 Replace Elementstag.replace_with(new_tag) 🔹 Wrap Elementstag.wrap(soup.new_tag("div")) 🔹 Unwrap Elementstag.unwrap() 👉 Key Insight You can reshape the entire hierarchy, not just edit nodes6. Saving the Modified HTMLwith open("output.html", "w") as f: f.write(str(soup)) 👉 Key Insight After modification, your parsed tree becomes a new document7. Mental ModelThink of Beautiful Soup as: ✏️ Editor → modify elements➕ Builder → add new nodes❌ Cleaner → remove unwanted data🔄 Architect → restructure layoutFinal TakeawayAt this stage, Beautiful Soup is no longer just a scraping tool—it becomes a full HTML transformation engine.You can: Edit existing dataInject new structuresRemove unwanted elementsRedesign the entire document👉 This is what enables automation pipelines, data cleaning systems, and dynamic content generation from raw HTML You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

    Course 40 - Web Scraping with Python | Episode 23: Mastering HTML Parse Tree Modification with Beautiful Soup

  6. 5 ngày trước

    Course 40 - Web Scraping with Python | Episode 22: Mastering Tree Traversal, CSS Selectors, and XPath

    In this lesson, you’ll learn about: precision data extraction using advanced tree traversal, powerful CSS selectors, and XPath navigation for handling even the most complex web structures1. Advanced Tree Traversal (Beyond Basics)🔹 Navigating the HTML “Family Tree”Instead of just searching, you move through the structure intelligently.🔹 Key Navigation Methodstag.find_parent() tag.find_next_sibling() tag.find_next() tag.find_all_next() 🔹 What Each Does find_parent() → move upwardfind_next_sibling() → next element at same levelfind_next() → next matching element anywhere afterfind_all_next() → all matches after current point👉 Key Insight Traversal lets you start anywhere and still reach your target2. CSS Selectors (Soup Sieve Power)🔹 Modern, Flexible SelectionBeautiful Soup supports CSS selectors via Soup Sieve.🔹 Basic Syntaxsoup.select("div.classname") soup.select("#main") soup.select("ul > li") 🔹 Selector Types #id → specific element.class → group of elementsA > B → direct children onlyA B → any nested descendants🔹 Sibling Selectorssoup.select("h2 + p") # next sibling soup.select("h2 ~ p") # all following siblings 👉 Key Insight CSS selectors are often cleaner and more readable than manual navigation3. Attribute Matching in CSS🔹 Targeting Dynamic Datasoup.select('a[href^="https"]') soup.select('img[src$=".png"]') soup.select('a[href*="example"]') 🔹 Matching Types ^= → starts with$= → ends with*= → contains👉 Key Insight Perfect for scraping dynamic or partially known values4. XPath Navigation (Precision Mode)🔹 Path-Based TargetingXPath works like navigating folders:🔹 Examples# Absolute path /html/body/div[1]/a # Global search //a # Attribute filtering //a[@href="example.com"] # Indexing (//a)[1] 🔹 Key Features Navigate from root or anywhereFilter by attributesSelect exact index👉 Key Insight XPath is the most precise but strict method5. CSS vs XPath vs TraversalMethodStrengthBest UseTraversalFlexibleDynamic navigationCSS SelectorsReadableMost scraping tasksXPathPreciseComplex structures6. Combining Techniques🔹 Real Power Comes from MixingExample workflow: Start with CSS selectorNavigate with traversalRefine with XPath👉 Key Insight No single method is enough for all cases7. Mental ModelThink like this: 🧭 Traversal → move through structure🎯 CSS → quickly target patterns🔬 XPath → pinpoint exact elementsFinal TakeawayAt this level, scraping becomes surgical precision engineering.You are no longer guessing where data is—you are: Navigating directly to itSelecting it with intentExtracting it efficiently👉 With traversal + CSS + XPath, you can handle any web structure, no matter how complex You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

    Course 40 - Web Scraping with Python | Episode 22: Mastering Tree Traversal, CSS Selectors, and XPath

  7. 6 ngày trước

    Course 40 - Web Scraping with Python | Episode 21: Mastering XML Parsing and Advanced Search with Beautiful Soup and XPath

    In this lesson, you’ll learn about: how XML and XPath enable precise data navigation, and how to use advanced Beautiful Soup techniques for highly targeted extraction from complex documents1. XML as a Data Structure🔹 Why XML Matters🔹 Key Characteristics Designed for data transfer, not displayStrict and well-formedHighly structured and predictable👉 Key Insight XML is ideal for scraping because its structure is consistent and machine-friendly2. Parsing XML with LXML🔹 Turning XML into a Treefrom bs4 import BeautifulSoup soup = BeautifulSoup(xml_data, "xml") 🔹 Why Use LXML Fast parsingHandles large structured dataWorks seamlessly with XPath3. XPath: Precision Navigation🔹 Query Language for TreesXPath works like a file system path:# Example concept /html/body/div[1]/a 🔹 What XPath Can Do Select nodes by locationFilter by attributesNavigate deep hierarchies👉 Key Insight XPath gives you surgical precision in large documents4. Limiting Search Results🔹 Control Output Sizesoup.find_all("item", limit=5) Returns only first N matches👉 Why It Matters Improves performanceUseful for testing and sampling5. Controlling Search Depth🔹 Recursive vs Non-Recursivesoup.find_all("div", recursive=False) True (default) → searches entire subtreeFalse → only direct children👉 Key Insight Restricting depth = faster + more accurate queries6. Handling Custom Attributes🔹 Attributes with Special Namessoup.find_all(attrs={"extra-info": "value"}) 🔹 Why This Matters Handles data-* and hyphenated attributesAvoids Python keyword conflicts👉 Key Insight attrs unlocks full flexibility in attribute filtering7. Text-Based Extraction🔹 Targeting Content Directlysoup.find_all(string="Example Text") 🔹 Pattern Matchingimport re soup.find_all(string=re.compile("Example")) 👉 Key Insight You can search by content, not just structure8. Custom Function Filters🔹 Complex Logic Extractiondef single_text_child(tag): return tag.string is not None soup.find_all(single_text_child) 👉 Why This Is Powerful Enables advanced conditionsFully customizable filtering9. Combining Techniques (Real Power)🔹 Full Precision ExtractionYou can combine: XPath for structurefind_all() for discoveryAttribute filtersText filtersCustom logic10. Mental ModelThink of advanced parsing as: 🧭 XPath → exact location🔍 BeautifulSoup → flexible search🧠 Filters → smart decision logicFinal TakeawayAt this stage, scraping becomes precision engineering rather than simple extraction.You are now able to: Navigate deeply nested structuresControl search scope and performanceExtract exactly what you need with minimal noise👉 This is what separates basic scraping from professional-grade data parsing You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

    Course 40 - Web Scraping with Python | Episode 21: Mastering XML Parsing and Advanced Search with Beautiful Soup and XPath

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    Course 40 - Web Scraping with Python | Episode 20: XPath Fundamentals and Advanced Beautiful Soup Searching

    In this lesson, you’ll learn about: how Beautiful Soup works with both HTML and XML, how XPath enhances tree navigation, and how to perform precise, high-performance searches using advanced filtering techniques1. HTML vs XML in Web Scraping🔹 Understanding the Difference🔹 Key Concepts HTML → designed for display (messy, flexible)XML → designed for data (strict, structured)👉 Key Insight XML is predictable → HTML is not2. Parsing XML with Beautiful Soup🔹 Using LXML Parserfrom bs4 import BeautifulSoup soup = BeautifulSoup(xml_data, "xml") 🔹 Why LXML? FastHandles both HTML & XMLWorks well with large datasets3. XPath (Advanced Navigation)🔹 Querying the TreeXPath allows you to: Navigate by exact pathFilter by attributesTarget deeply nested elements👉 Key Insight XPath = precision targeting in complex trees4. Limiting Search Results🔹 Controlling Output Sizesoup.find_all("a", limit=3) Returns only first N matches👉 Key Insight Useful for performance + sampling data5. Non-Recursive Searches🔹 Restricting Scopesoup.find_all("div", recursive=False) Searches only direct childrenAvoids deep traversal👉 Key Insight Improves speed and accuracy in large documents6. Attribute-Based Filtering🔹 Using attrs Dictionarysoup.find_all(attrs={"data-id": "123"}) 🔹 Why Use attrs? Handles special characters (data-*)Avoids keyword conflicts (name, class)👉 Key Insight attrs gives full control over attribute filtering7. Text-Based Searching🔹 Finding Specific Textsoup.find_all(string="Hello World") 🔹 Match by Patternimport re soup.find_all(string=re.compile("Hello")) 👉 Key Insight You can target content—not just tags8. Custom Function Filters🔹 Advanced Logicdef only_text(tag): return tag.string is not None soup.find_all(only_text) 👉 Key Insight Custom filters = maximum flexibility9. Real-World Precision Extraction🔹 Combining TechniquesYou can combine: XPath / structureAttribute filtersText filtersCustom logic10. Mental ModelThink of advanced scraping like: 🎯 XPath → sniper precision🔍 find_all → search engine🧠 filters → decision logicFinal TakeawayAt this level, scraping becomes surgical instead of exploratory.You are no longer just finding data—you are:👉 targeting exact nodes 👉 limiting scope for performance 👉 combining filters for precisionThat’s what transforms scraping into a high-performance data extraction system. You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

    Course 40 - Web Scraping with Python | Episode 20: XPath Fundamentals and Advanced Beautiful Soup Searching
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Giới Thiệu

Welcome to CyberCode Academy — your audio classroom for Programming and Cybersecurity. 🎧 Each course is divided into a series of short, focused episodes that take you from beginner to advanced level — one lesson at a time. From Python and web development to ethical hacking and digital defense, our content transforms complex concepts into simple, engaging audio learning. Study anywhere, anytime — and level up your skills with CyberCode Academy. 🚀 Learn. Code. Secure. You can listen and download our episodes for free on more than 10 different platforms: https://linktr.ee/cybercode_academy

Thông Tin

  • Nhà sáng tạo
    CyberCode Academy
  • Năm hoạt động
    2025 - 2026
  • Tập
    321
  • Xếp hạng
    Sạch
  • Bản quyền
    © Copyright CyberCode Academy
  • Trang web chương trình
    CyberCode Academy

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