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Programming Resources | Python | Javascript | Artificial Intelligence Updates | Computer Science Courses | AI Books

Programming Resources | Python | Javascript | Artificial Intelligence Updates | Computer Science Courses | AI Books

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Everything about programming for beginners * Python programming * Java programming * App development * Machine Learning * Data Science Managed by: @love_data

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๐Ÿ“ˆ Telegram kanali Programming Resources | Python | Javascript | Artificial Intelligence Updates | Computer Science Courses | AI Books analitikasi

Programming Resources | Python | Javascript | Artificial Intelligence Updates | Computer Science Courses | AI Books (@programming_guide) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 56 105 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 2 372-o'rinni va Hindiston mintaqasida 6 557-o'rinni egallagan.

๐Ÿ“Š Auditoriya koโ€˜rsatkichlari va dinamika

ะฝะตะฒั–ะดะพะผะพ sanasidan buyon loyiha tez oโ€˜sib, 56 105 obunachiga ega boโ€˜ldi.

06 Iyun, 2026 dagi oxirgi maโ€™lumotlarga koโ€˜ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 110 ga, soโ€˜nggi 24 soatda esa 1 ga oโ€˜zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya oโ€˜rtacha 2.56% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 0.84% ini tashkil etuvchi reaksiyalarni toโ€˜playdi.
  • Post qamrovi: Har bir post oโ€˜rtacha 1 435 marta koโ€˜riladi; birinchi sutkada odatda 473 ta koโ€˜rish yigโ€˜iladi.
  • Reaksiyalar va oโ€˜zaro taโ€™sir: Auditoriya faol: har bir postga oโ€˜rtacha 3 ta reaksiya keladi.
  • Tematik yoโ€˜nalishlar: Kontent algorithm, structure, stack, javascript, programming kabi asosiy mavzularga jamlangan.

๐Ÿ“ Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida taโ€™riflaydi:
โ€œEverything about programming for beginners * Python programming * Java programming * App development * Machine Learning * Data Science Managed by: @love_dataโ€

Yuqori yangilanish chastotasi (oxirgi maโ€™lumot 08 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boโ€˜lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim taโ€™sir nuqtasiga aylantirishini koโ€˜rsatadi.

56 105
Obunachilar
+124 soatlar
+357 kunlar
+11030 kunlar
Postlar arxiv
๐—”๐—œ/๐— ๐—Ÿ ๐—ฟ๐—ผ๐—น๐—ฒ๐˜€ ๐—ฎ๐—ฟ๐—ฒ ๐—ณ๐—ฎ๐˜€๐˜๐—ฒ๐˜€๐˜-๐—ด๐—ฟ๐—ผ๐˜„๐—ถ๐—ป๐—ด ๐—ฐ๐—ฎ๐—ฟ๐—ฒ๐—ฒ๐—ฟ ๐—ณ๐—ถ๐—ฒ๐—น๐—ฑ ๐—ถ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฒ๐Ÿ˜ The demand is real, salarie
๐—”๐—œ/๐— ๐—Ÿ ๐—ฟ๐—ผ๐—น๐—ฒ๐˜€ ๐—ฎ๐—ฟ๐—ฒ ๐—ณ๐—ฎ๐˜€๐˜๐—ฒ๐˜€๐˜-๐—ด๐—ฟ๐—ผ๐˜„๐—ถ๐—ป๐—ด ๐—ฐ๐—ฎ๐—ฟ๐—ฒ๐—ฒ๐—ฟ ๐—ณ๐—ถ๐—ฒ๐—น๐—ฑ ๐—ถ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฒ๐Ÿ˜ The demand is real, salaries are high, and the talent gap is wide open Enrol for AI/ML Certification Program by CCE, IIT Mandi! Eligibility: Open to everyone Duration: 6 Months Program Mode: Online Taught By: IIT Mandi Professors Deadline :- 23rd May ๐—ฅ๐—ฒ๐—ด๐—ถ๐˜€๐˜๐—ฒ๐—ฟ ๐—ก๐—ผ๐˜„๐Ÿ‘‡ :- https://pdlink.in/4nmI024 . ๐ŸŽ“Get Placement Assistance With 5000+ Companies

โœ… Essential Tools for Data Analytics ๐Ÿ“Š๐Ÿ› ๏ธ ๐Ÿ”ฃ 1๏ธโƒฃ Excel / Google Sheets โ€ข Quick data entry & analysis โ€ข Pivot tables, charts, functions โ€ข Good for early-stage exploration ๐Ÿ’ป 2๏ธโƒฃ SQL (Structured Query Language) โ€ข Work with databases (MySQL, PostgreSQL, etc.) โ€ข Query, filter, join, and aggregate data โ€ข Must-know for data from large systems ๐Ÿ 3๏ธโƒฃ Python (with Libraries) โ€ข Pandas โ€“ Data manipulation โ€ข NumPy โ€“ Numerical analysis โ€ข Matplotlib / Seaborn โ€“ Data visualization โ€ข OpenPyXL / xlrd โ€“ Work with Excel files ๐Ÿ“Š 4๏ธโƒฃ Power BI / Tableau โ€ข Create dashboards and visual reports โ€ข Drag-and-drop interface for non-coders โ€ข Ideal for business insights & presentations ๐Ÿ“ 5๏ธโƒฃ Google Data Studio โ€ข Free dashboard tool โ€ข Connects easily to Google Sheets, BigQuery โ€ข Great for real-time reporting ๐Ÿงช 6๏ธโƒฃ Jupyter Notebook โ€ข Interactive Python coding โ€ข Combine code, text, and visuals in one place โ€ข Perfect for storytelling with data ๐Ÿ› ๏ธ 7๏ธโƒฃ R Programming (Optional) โ€ข Popular in statistical analysis โ€ข Strong in academic and research settings โ˜๏ธ 8๏ธโƒฃ Cloud & Big Data Tools โ€ข Google BigQuery, Snowflake โ€“ Large-scale analysis โ€ข Excel + SQL + Python still work as a base ๐Ÿ’ก Tip: Start with Excel + SQL + Python (Pandas) โ†’ Add BI tools for reporting. ๐Ÿ’ฌ Tap โค๏ธ for more!

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C Syntax Cheatsheet๐Ÿ‘จ๐Ÿปโ€๐Ÿ’ป๐Ÿ“ #techinfo
C Syntax Cheatsheet๐Ÿ‘จ๐Ÿปโ€๐Ÿ’ป๐Ÿ“ #techinfo

๐—™๐—ฅ๐—˜๐—˜ ๐—ข๐—ป๐—น๐—ถ๐—ป๐—ฒ ๐— ๐—ฎ๐˜€๐˜๐—ฒ๐—ฟ๐—ฐ๐—น๐—ฎ๐˜€๐˜€ ๐—ข๐—ป ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ ( ๐—•๐˜‚๐˜€๐—ถ๐—ป๐—ฒ๐˜€๐˜€ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€)๐Ÿ˜ Learn
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If you are starting a new project, you must first choose a tech stack. Project starter pack: - Backend: .NET - Mobile: .NET MAUI(or AvaloniaUI or Uno) - Frontend: Blazor or Angular - Database: SQL Server or PostgreSQL - Testing: xUnit + NSubstitute Depending on the project type, mix and match various technologies. This list has evolved over time because the key is to have a tech stack that allows you to build a robust and maintainable product. Not just to follow trends. Web Development Best Resources โˆŸ๐Ÿ“‚ topmate.io/coding/930165 ENJOY LEARNING ๐Ÿ‘๐Ÿ‘

๐—ฃ๐—ฎ๐˜† ๐—”๐—ณ๐˜๐—ฒ๐—ฟ ๐—ฃ๐—น๐—ฎ๐—ฐ๐—ฒ๐—บ๐—ฒ๐—ป๐˜ ๐—ฃ๐—ฟ๐—ผ๐—ด๐—ฟ๐—ฎ๐—บ ๐—ง๐—ผ ๐—•๐—ฒ๐—ฐ๐—ผ๐—บ๐—ฒ ๐—ฎ ๐—๐—ผ๐—ฏ-๐—ฅ๐—ฒ๐—ฎ๐—ฑ๐˜† ๐—ฆ๐—ผ๐—ณ๐˜๐˜„๐—ฎ๐—ฟ๐—ฒ ๐——๐—ฒ๐˜ƒ๐—ฒ๐—น๐—ผ๐—ฝ๐—ฒ๏ฟฝ
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๐Ÿš€ Habits That Make You a Better Programmer ๐Ÿ‘จโ€๐Ÿ’ป๐Ÿ”ฅ โฐ 1. Code Every Day โœ” Consistency builds skill โœ” Even 30โ€“60 mins daily matters โœ” Small progress compounds fast ๐Ÿ“š 2. Read Documentation โœ” Donโ€™t rely only on tutorials โœ” Learn from official docs โœ” Understand concepts deeply ๐Ÿ›  3. Build Projects Regularly โœ” Apply what you learn โœ” Create portfolio-worthy apps โœ” Real projects = real learning ๐Ÿง  4. Practice Problem Solving โœ” Improve logical thinking โœ” Solve DSA questions โœ” Learn multiple approaches ๐Ÿž 5. Learn Debugging Skills โœ” Fix errors patiently โœ” Use breakpoints & logs โœ” Understand why bugs happen ๐ŸŒ 6. Use Version Control โœ” Learn Git basics โœ” Push projects on GitHub โœ” Track changes professionally ๐Ÿ‘จโ€๐Ÿ’ป 7. Read Other Developersโ€™ Code โœ” Learn clean coding styles โœ” Understand architecture โœ” Explore open-source projects โšก 8. Learn One Thing at a Time โœ” Avoid tutorial overload โœ” Focus on one stack โœ” Master fundamentals first ๐Ÿค– 9. Use AI as a Mentor, Not a Shortcut โœ” Ask for explanations โœ” Learn debugging with AI โœ” Donโ€™t blindly copy-paste code ๐Ÿ”ฅ 10. Never Stop Learning โœ” Technology keeps evolving โœ” Stay curious โœ” Upgrade your skills continuously ๐Ÿ’ก Great programmers are built through daily habits, not overnight success. ๐Ÿ’ฌ Tap โค๏ธ if this helped you!

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โœ… Programming Languages & Their Difficulty Level ๐Ÿ‘จโ€๐Ÿ’ป๐Ÿ“Š ๐ŸŸข BEGINNER FRIENDLY ๐Ÿ Python โœ” Simple Syntax โœ” Easy to Read โœ” Best for AI & Automation โœ” Huge Learning Resources ๐ŸŸจ JavaScript โœ” Easy to Start โœ” Build Websites Quickly โœ” Great for Frontend Development โœ” Huge Community Support ๐ŸŸฆ PHP โœ” Beginner Friendly โœ” Easy Backend Development โœ” Great for Dynamic Websites โœ” Fast to Learn Basics ๐ŸŸก MODERATE LEVEL โ˜• Java โœ” Strong OOP Concepts โœ” Enterprise Applications โœ” Android Development โœ” More Structured Syntax ๐ŸŸฉ C# โœ” Great for Unity & .NET โœ” Powerful Frameworks โœ” Beginner + Intermediate Friendly โœ” Enterprise Ready ๐Ÿ“ฑ Kotlin โœ” Modern Android Development โœ” Cleaner than Java โœ” Safe & Scalable โœ” Easy for Java Developers ๐Ÿ”ด ADVANCED LEVEL ๐Ÿ’ป C++ โœ” Memory Management โœ” Fast Performance โœ” System Programming โœ” Complex Syntax ๐Ÿฆ€ Rust โœ” Memory Safety โœ” System Level Development โœ” High Performance โœ” Steeper Learning Curve โšก BONUS TIP ๐Ÿ”น Start with one beginner-friendly language ๐Ÿ”น Build small projects daily ๐Ÿ”น Learn problem-solving, not only syntax ๐Ÿ”น Consistency beats speed ๐Ÿ’ก Every expert programmer was once a beginner. ๐Ÿ’ฌ Tap โค๏ธ if this helped you!

๐—ฃ๐—ฟ๐—ผ๐—ฑ๐˜‚๐—ฐ๐˜ ๐— ๐—ฎ๐—ป๐—ฎ๐—ด๐—ฒ๐—บ๐—ฒ๐—ป๐˜ ๐˜„๐—ถ๐˜๐—ต ๐—”๐—œ ๐—ฃ๐—ฟ๐—ผ๐—ด๐—ฟ๐—ฎ๐—บ by iHUB IIT Roorkee ๐Ÿ˜ Freshers get paid 12 LPA average sala
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SQL From Basic to Advanced level Basic SQL is ONLY 7 commands: - SELECT - FROM - WHERE (also use SQL comparison operators such as =, <=, >=, <> etc.) - ORDER BY - Aggregate functions such as SUM, AVERAGE, COUNT etc. - GROUP BY - CREATE, INSERT, DELETE, etc. You can do all this in just one morning. Once you know these, take the next step and learn commands like: - LEFT JOIN - INNER JOIN - LIKE - IN - CASE WHEN - HAVING (undertstand how it's different from GROUP BY) - UNION ALL This should take another day. Once both basic and intermediate are done, start learning more advanced SQL concepts such as: - Subqueries (when to use subqueries vs CTE?) - CTEs (WITH AS) - Stored Procedures - Triggers - Window functions (LEAD, LAG, PARTITION BY, RANK, DENSE RANK) These can be done in a couple of days. Learning these concepts is NOT hard at all - what takes time is practice and knowing what command to use when. How do you master that? - First, create a basic SQL project - Then, work on an intermediate SQL project (search online) - Lastly, create something advanced on SQL with many CTEs, subqueries, stored procedures and triggers etc. This is ALL you need to become a badass in SQL, and trust me when I say this, it is not rocket science. It's just logic. Remember that practice is the key here. It will be more clear and perfect with the continous practice Best telegram channel to learn SQL: https://t.me/sqlanalyst Data Analyst Jobs๐Ÿ‘‡ https://t.me/jobs_SQL Join @free4unow_backup for more free resources. Like this post if it helps ๐Ÿ˜„โค๏ธ ENJOY LEARNING ๐Ÿ‘๐Ÿ‘

๐Ÿš€ ๐—•๐—ฒ๐—ฐ๐—ผ๐—บ๐—ฒ ๐—๐—ผ๐—ฏ-๐—ฅ๐—ฒ๐—ฎ๐—ฑ๐˜† ๐—ถ๐—ป ๐——๐—ฎ๐˜๐—ฎ ๐—ฆ๐—ฐ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ & ๐—”๐—œ ๐˜„๐—ถ๐˜๐—ต ๐—œ๐—ป๐—ฑ๐˜‚๐˜€๐˜๐—ฟ๐˜† ๐—˜๐˜…๐—ฝ๐—ฒ๐—ฟ๐˜๐˜€! ๐Ÿ“Š Learn the
๐Ÿš€ ๐—•๐—ฒ๐—ฐ๐—ผ๐—บ๐—ฒ ๐—๐—ผ๐—ฏ-๐—ฅ๐—ฒ๐—ฎ๐—ฑ๐˜† ๐—ถ๐—ป ๐——๐—ฎ๐˜๐—ฎ ๐—ฆ๐—ฐ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ & ๐—”๐—œ ๐˜„๐—ถ๐˜๐—ต ๐—œ๐—ป๐—ฑ๐˜‚๐˜€๐˜๐—ฟ๐˜† ๐—˜๐˜…๐—ฝ๐—ฒ๐—ฟ๐˜๐˜€! ๐Ÿ“Š Learn the most in-demand skills of 2026 ๐Ÿ’ซData Science ,AI,ML &Python & SQL โœ… ๐Ÿ’ผ Get Placement Assistance ๐ŸŽ“ Beginner Friendly Program ๐Ÿ’ป Learn Online from Anywhere ๐Ÿ“ˆ Build Skills Companies Actually Hire For ๐Ÿ”ฅ AI is changing every industry โ€” this is the best time to upskill and secure high-paying tech jobs. ๐‘๐ž๐ ๐ข๐ฌ๐ญ๐ž๐ซ ๐๐จ๐ฐ ๐Ÿ‘‡:-  https://pdlink.in/4fdWxJB โšก Limited Seats Available โ€“ Apply Fast!

โœ… Programming Languages, Libraries & Tools Every Tech Field Uses ๐Ÿ‘จโ€๐Ÿ’ป๐Ÿš€ ๐Ÿง  DATA SCIENCE & MACHINE LEARNING 1. Python โ†’ Pandas, NumPy, TensorFlow, PyTorch 2. R โ†’ ggplot2, dplyr, caret 3. SQL โ†’ PostgreSQL, MySQL 4. Julia โ†’ Flux, Pluto ๐Ÿค– ARTIFICIAL INTELLIGENCE 1. Python โ†’ Keras, OpenCV, LangChain 2. C++ โ†’ OpenCV, CUDA 3. Java โ†’ Deeplearning4j ๐ŸŒ WEB DEVELOPMENT 1. JavaScript โ†’ React, Node.js, Express.js 2. TypeScript โ†’ Next.js, Angular 3. PHP โ†’ Laravel 4. Python โ†’ Django, Flask ๐Ÿ“ฑ APP DEVELOPMENT 1. Kotlin โ†’ Android SDK, Jetpack Compose 2. Swift โ†’ SwiftUI, UIKit 3. Dart โ†’ Flutter 4. JavaScript โ†’ React Native ๐ŸŽฎ GAME DEVELOPMENT 1. C++ โ†’ Unreal Engine 2. C# โ†’ Unity 3. Lua โ†’ Roblox Studio 4. Python โ†’ Pygame ๐Ÿ” CYBER SECURITY 1. Python โ†’ Scapy, Requests 2. Bash โ†’ Linux Tools 3. PowerShell โ†’ Windows Automation 4. Go โ†’ Networking Tools โ˜๏ธ CLOUD & DEVOPS 1. Go โ†’ Docker, Kubernetes 2. Python โ†’ Ansible, Boto3 3. Shell Script โ†’ Linux Automation 4. YAML โ†’ CI/CD Pipelines ๐Ÿ’ฌ Tap โค๏ธ if this helped you!

๐—”๐—œ ๐—ฎ๐—ป๐—ฑ ๐— ๐—Ÿ ๐—ฃ๐—ฟ๐—ผ๐—ด๐—ฟ๐—ฎ๐—บ ๐—ฏ๐˜† ๐—–๐—–๐—˜, ๐—œ๐—œ๐—ง ๐— ๐—ฎ๐—ป๐—ฑ๐—ถ๐Ÿ˜ Freshers get 15 LPA Average Salary with AI & ML Skills! ๐Ÿ’ป 1
๐—”๐—œ ๐—ฎ๐—ป๐—ฑ ๐— ๐—Ÿ ๐—ฃ๐—ฟ๐—ผ๐—ด๐—ฟ๐—ฎ๐—บ ๐—ฏ๐˜† ๐—–๐—–๐—˜, ๐—œ๐—œ๐—ง ๐— ๐—ฎ๐—ป๐—ฑ๐—ถ๐Ÿ˜ Freshers get 15 LPA Average Salary with AI & ML Skills! ๐Ÿ’ป 100% Online โณ 6 Months Duration ๐Ÿ‘จโ€๐Ÿซ Learn from IIT Professors ๐Ÿ“Œ Open for Students ,Freshers & Working Professionals ๐Ÿ’ผ Placement Assistance with 5000+ Companies ๐Ÿ“ˆ High Demand Skills for Future Tech Jobs Top companies are hiring for candidates with ๐—”๐—œ, ๐— ๐—ฎ๐—ฐ๐—ต๐—ถ๐—ป๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด skills in 2026 ๐Ÿ”ฅDeadline :- 17th May   ๐—”๐—ฝ๐—ฝ๐—น๐˜† ๐—ก๐—ผ๐˜„๐Ÿ‘‡ :-  https://pdlink.in/4nmI024 . Get Placement Assistance With 5000+ Companies

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๐Ÿ—„๏ธ ๐—ง๐—ผ๐—ฝ ๐Ÿฑ ๐—™๐—ฅ๐—˜๐—˜ ๐—ฆ๐—ค๐—Ÿ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐Ÿš€ SQL is one of the most important skills for Data A
๐Ÿ—„๏ธ ๐—ง๐—ผ๐—ฝ ๐Ÿฑ ๐—™๐—ฅ๐—˜๐—˜ ๐—ฆ๐—ค๐—Ÿ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐Ÿš€ SQL is one of the most important skills for Data Analyst & Tech jobs in 2026 ๐Ÿ”ฅ These FREE certification courses can help you learn SQL from scratch & boost your resume ๐Ÿ’ผ โœจ Learn: โœ” SQL Queries & Databases ๐Ÿ—„๏ธ โœ” Data Analysis Basics ๐Ÿ“Š โœ” Real-world Projects โœ” Beginner to Advanced Concepts ๐—˜๐—ป๐—ฟ๐—ผ๐—น๐—น ๐—™๐—ผ๐—ฟ ๐—™๐—ฅ๐—˜๐—˜๐Ÿ‘‡:-    https://pdlink.in/4dCHiKI   ๐Ÿ’ฏ Beginner Friendly + FREE Certificates ๐ŸŽ“ ๐Ÿ’ผ Perfect for Students, Freshers & Career Switchers

๐Ÿ”ฐ How to become a data scientist in 2025? ๐Ÿ‘จ๐Ÿปโ€๐Ÿ’ป If you want to become a data science professional, follow this path! I've prepared a complete roadmap with the best free resources where you can learn the essential skills in this field. ๐Ÿ”ข Step 1: Strengthen your math and statistics! โœ๏ธ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master: โœ… Linear algebra: matrices, vectors, eigenvalues. ๐Ÿ”— Course: MIT 18.06 Linear Algebra โœ… Calculus: derivative, integral, optimization. ๐Ÿ”— Course: MIT Single Variable Calculus โœ… Statistics and probability: Bayes' theorem, hypothesis testing. ๐Ÿ”— Course: Statistics 110 โž–โž–โž–โž–โž– ๐Ÿ”ข Step 2: Learn to code. โœ๏ธ Learn Python and become proficient in coding. The most important topics you need to master are: โœ… Python: Pandas, NumPy, Matplotlib libraries ๐Ÿ”— Course: FreeCodeCamp Python Course โœ… SQL language: Join commands, Window functions, query optimization. ๐Ÿ”— Course: Stanford SQL Course โœ… Data structures and algorithms: arrays, linked lists, trees. ๐Ÿ”— Course: MIT Introduction to Algorithms โž–โž–โž–โž–โž– ๐Ÿ”ข Step 3: Clean and visualize data โœ๏ธ Learn how to process and clean data and then create an engaging story from it! โœ… Data cleaning: Working with missing values โ€‹โ€‹and detecting outliers. ๐Ÿ”— Course: Data Cleaning โœ… Data visualization: Matplotlib, Seaborn, Tableau ๐Ÿ”— Course: Data Visualization Tutorial โž–โž–โž–โž–โž– ๐Ÿ”ข Step 4: Learn Machine Learning โœ๏ธ It's time to enter the exciting world of machine learning! You should know these topics: โœ… Supervised learning: regression, classification. โœ… Unsupervised learning: clustering, PCA, anomaly detection. โœ… Deep learning: neural networks, CNN, RNN ๐Ÿ”— Course: CS229: Machine Learning โž–โž–โž–โž–โž– ๐Ÿ”ข Step 5: Working with Big Data and Cloud Technologies โœ๏ธ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing. โœ… Big Data Tools: Hadoop, Spark, Dask โœ… Cloud platforms: AWS, GCP, Azure ๐Ÿ”— Course: Data Engineering โž–โž–โž–โž–โž– ๐Ÿ”ข Step 6: Do real projects! โœ๏ธ Enough theory, it's time to get coding! Do real projects and build a strong portfolio. โœ… Kaggle competitions: solving real-world challenges. โœ… End-to-End projects: data collection, modeling, implementation. โœ… GitHub: Publish your projects on GitHub. ๐Ÿ”— Platform: Kaggle๐Ÿ”— Platform: ods.ai โž–โž–โž–โž–โž– ๐Ÿ”ข Step 7: Learn MLOps and deploy models โœ๏ธ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model. โœ… MLOps training: model versioning, monitoring, model retraining. โœ… Deployment models: Flask, FastAPI, Docker ๐Ÿ”— Course: Stanford MLOps Course โž–โž–โž–โž–โž– ๐Ÿ”ข Step 8: Stay up to date and network โœ๏ธ Data science is changing every day, so it is necessary to update yourself every day and stay in regular contact with experienced people and experts in this field. โœ… Read scientific articles: arXiv, Google Scholar โœ… Connect with the data community: ๐Ÿ”— Site: Papers with code ๐Ÿ”— Site: AI Research at Google
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