c_forcoding
Kanalga Telegram’da o‘tish
C++, C, python & DSA and many more info about programming so just join our coders community ☺️
Ko'proq ko'rsatish📈 Telegram kanali c_forcoding analitikasi
c_forcoding (@cforcodingofficial) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 18 146 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 5 586-o'rinni va Hindiston mintaqasida 29 620-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 18 146 obunachiga ega bo‘ldi.
31 May, 2025 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -191 ga, so‘nggi 24 soatda esa 0 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 0% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining N/A% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 0 marta ko‘riladi; birinchi sutkada odatda 0 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 0 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent dsa, learning, programming, certification, sql kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“C++, C, python & DSA and many more info about programming so just join our coders community ☺️”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 01 Iyun, 2025 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
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Obunachilar
Ma'lumot yo'q24 soatlar
-377 kunlar
-19130 kunlar
Postlar arxiv
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100 DBMS & SQL Interview Questions
https://drive.google.com/file/d/1irA1RZ6TyHPhjRLZM10ReQ27QBbQ0YZg/view?usp=drive_link
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🔟 Python resources to boost your resume 👇👇
𝟭. 𝗜𝗻𝘁𝗿𝗼 𝘁𝗼 𝗣𝘆𝘁𝗵𝗼𝗻
This a great course to get started with learning Python, if you have no coding experience.
👉 https://kaggle.com/learn/intro-to-programming
𝟮. 𝗣𝘆𝘁𝗵𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗰𝗼𝘂𝗿𝘀𝗲
Learn the fundamentals like functions, loops, conditional statements, etc of the most important language for data science.
👉 https://kaggle.com/learn/python
𝟯. 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹𝘀 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻
Part 1 prepares you for PCEP – Certified Entry-Level Python Programmer Certification.
Part 2 prepares you for PCAP – Certified Associate in Python Programming Certification.
👉 https://netacad.com/courses/programming/pcap-programming-essentials-python
𝟰. Python Data Structure and Algorithms
👉https://python.org
𝟱. 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻
You'll learn Python fundamentals like variables, loops, conditionals, and functions.
Then you'll quickly ramp up to complex data structures, networking, relational databases, and data visualization.
👉 https://freecodecamp.org/learn/scientific-computing-with-python/
𝟲. 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻
You'll learn how to read data from sources like CSVs and SQL, and how to use libraries like Numpy, Pandas, Matplotlib, and Seaborn to process and visualize data.
👉 https://freecodecamp.org/learn/data-analysis-with-python/
𝟳. 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻
You will learn how to implement the graphical representation of data in order to interactively and efficiently convey insights to clients, customers, and stakeholders in general.
👉 https://cognitiveclass.ai/courses/data-visualization-python#about-course
𝟴. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻
You will build several neural networks and explore more advanced techniques like natural language processing and reinforcement learning.
👉 https://freecodecamp.org/learn/machine-learning-with-python/
9. Practice Python
👉 https://learnpython.org/
10. Free Python course by datacamp
👉 https://campus.datacamp.com/courses/intro-to-python-for-data-science/chapter-1-python-basics?ex=1
ENJOY LEARNING 👍👍
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Free Certification Courses to Learn Machine Learning in 2024:
1. Python
🔗 https://imp.i384100.net/5gmXXo
2. SQL
🔗 https://edx.org/learn/relational-databases/stanford-university-databases-relational-databases-and-sql
3. Statistics and R
🔗 https://edx.org/learn/r-programming/harvard-university-statistics-and-r
4. Data Science: R Basics
🔗https://edx.org/learn/r-programming/harvard-university-data-science-r-basics
5. Excel and PowerBI
🔗 https://learn.microsoft.com/en-gb/training/paths/modern-analytics/
6. Data Science: Visualization
🔗https://edx.org/learn/data-visualization/harvard-university-data-science-visualization
7. Data Science: Machine Learning
🔗https://edx.org/learn/machine-learning/harvard-university-data-science-machine-learning
8. R
🔗https://imp.i384100.net/rQqomy
9. Tableau
🔗https://imp.i384100.net/MmW9b3
10. PowerBI
🔗 https://lnkd.in/dpmnthEA
11. Data Science: Productivity Tools
🔗 https://lnkd.in/dGhPYg6N
12. Data Science: Probability
🔗https://mygreatlearning.com/academy/learn-for-free/courses/probability-for-data-science
13. Mathematics
🔗http://matlabacademy.mathworks.com
14. Statistics
🔗 https://lnkd.in/df6qksMB
15. Data Visualization
🔗https://imp.i384100.net/k0X6vx
16. Machine Learning
🔗 https://imp.i384100.net/nLbkN9
17. Deep Learning
🔗 https://imp.i384100.net/R5aPOR
18. Data Science: Linear Regression
🔗https://pll.harvard.edu/course/data-science-linear-regression/2023-10
19. Data Science: Wrangling
🔗https://edx.org/learn/data-science/harvard-university-data-science-wrangling
20. Linear Algebra
🔗 https://pll.harvard.edu/course/data-analysis-life-sciences-2-introduction-linear-models-and-matrix-algebra
21. Probability
🔗 https://pll.harvard.edu/course/data-science-probability
22. Introduction to Linear Models and Matrix Algebra
🔗https://edx.org/learn/linear-algebra/harvard-university-introduction-to-linear-models-and-matrix-algebra
23. Data Science: Capstone
🔗 https://edx.org/learn/data-science/harvard-university-data-science-capstone
24. Data Analysis
🔗 https://pll.harvard.edu/course/data-analysis-life-sciences-4-high-dimensional-data-analysis
25. IBM Data Science Professional Certificate
https://imp.i384100.net/9gxbbY
26. Neural Networks and Deep Learning
https://imp.i384100.net/DKrLn2
27. Supervised Machine Learning: Regression and Classification
https://imp.i384100.net/g1KJEA
Happy Learning!🌟
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