Programing Resources: Python - Java - R - SQL - Javascript - C++
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Free eBooks for programmers and developers Promotions: @coderfun
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Network:📈 Analytical overview of Telegram channel Programing Resources: Python - Java - R - SQL - Javascript - C++
Channel Programing Resources: Python - Java - R - SQL - Javascript - C++ (@the_programming_girls) in the English language segment is an active participant. Currently, the community unites 29 444 subscribers, ranking 3 888 in the Technologies & Applications category and 16 506 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 29 444 subscribers.
According to the latest data from 24 December, 2025, the channel demonstrates stable activity. Although there has been a change in the number of participants by 630 over the last 30 days and by 0 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 3.56%. Within the first 24 hours after publication, content typically collects 0.77% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 047 views. Within the first day, a publication typically gains 226 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 0.
- Thematic interests: Content is focused on key topics such as git, learning, sql, link:-, css.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Free eBooks for programmers and developers
Promotions: @coderfun”
Thanks to the high frequency of updates (latest data received on 18 March, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
29 444
Subscribers
No data24 hours
+1187 days
+63030 days
Posts Archive
Software Engineer Jobs
👇👇
https://t.me/internshiptojobs
+5
Skills to master as a web developer
Essential statistics topics for data science
1. Descriptive statistics: Measures of central tendency, measures of dispersion, and graphical representations of data.
2. Inferential statistics: Hypothesis testing, confidence intervals, and regression analysis.
3. Probability theory: Concepts of probability, random variables, and probability distributions.
4. Sampling techniques: Simple random sampling, stratified sampling, and cluster sampling.
5. Statistical modeling: Linear regression, logistic regression, and time series analysis.
6. Machine learning algorithms: Supervised learning, unsupervised learning, and reinforcement learning.
7. Bayesian statistics: Bayesian inference, Bayesian networks, and Markov chain Monte Carlo methods.
8. Data visualization: Techniques for visualizing data and communicating insights effectively.
9. Experimental design: Designing experiments, analyzing experimental data, and interpreting results.
10. Big data analytics: Handling large volumes of data using tools like Hadoop, Spark, and SQL.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/datasciencefun
Like if you need similar content 😄👍
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Using FastAPI to Build Python Web APIs
https://realpython.com/fastapi-python-web-apis/
80 most common interview questions and answers.pdf4.56 MB
7 Most Popular Programming Languages in 2025
1. Python
The Jack of All Trades
Why it's loved: Simple syntax, huge community, beginner-friendly.
Used for: Data Science, Machine Learning, Web Development, Automation.
Who uses it: Data analysts, backend developers, researchers, even kids learning to code.
2. JavaScript
The Language of the Web
Why it's everywhere: Runs in every browser, now also on servers (Node.js).
Used for: Frontend & backend web apps, interactive UI, full-stack apps.
Who uses it: Web developers, app developers, UI/UX enthusiasts.
3. Java
The Enterprise Backbone
Why it stands strong: Portable, secure, scalable — runs on everything from desktops to Android devices.
Used for: Android apps, enterprise software, backend systems.
Who uses it: Large corporations, Android developers, system architects.
4. C/C++
The Power Players
Why they matter: Super fast, close to the hardware, great for performance-critical apps.
Used for: Game engines, operating systems, embedded systems.
Who uses it: System programmers, game developers, performance-focused engineers.
5. C#
Microsoft’s Darling
Why it's growing: Built into the .NET ecosystem, great for Windows apps and games.
Used for: Desktop applications, Unity game development, enterprise tools.
Who uses it: Game developers, enterprise app developers, Windows lovers.
6. SQL
The Language of Data
Why it’s essential: Every application needs a database — SQL helps you talk to it.
Used for: Querying databases, reporting, analytics.
Who uses it: Data analysts, backend devs, business intelligence professionals.
7. Go (Golang)
The Modern Minimalist
Why it’s rising: Simple, fast, and built for scale — ideal for cloud-native apps.
Used for: Web servers, microservices, distributed systems.
Who uses it: Backend engineers, DevOps, cloud developers.
Free Coding Resources: https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
Here are a few project ideas that could help you stand out:
Quantitative Analysis of Financial Data: Create a project where you analyze historical financial data using statistical methods and time series analysis to identify patterns, correlations, and trends in the data.
Development of Trading Strategies: Design and backtest quantitative trading strategies using historical market data. Showcase your ability to develop, test, and optimize algorithmic trading models.
Risk Management Simulation: Build a simulation model to assess and manage financial risk. This could involve implementing Value at Risk (VaR) models or stress testing methodologies.
Machine Learning for Finance: Explore the application of machine learning algorithms to financial markets. Develop a project that uses machine learning for stock price prediction, sentiment analysis of news articles, or credit risk assessment.
Financial Modeling and Valuation: Create detailed financial models for companies or investment opportunities. This could include building discounted cash flow (DCF) models, comparable company analysis, and merger and acquisition (M&A) valuation.
Portfolio Optimization: Develop a project that focuses on portfolio optimization techniques, such as modern portfolio theory, mean-variance optimization, or factor modeling.
By working on these projects, you can demonstrate your skills in quantitative analysis, financial modeling, and programming, which are highly valued in the field of quantitative finance.
Additionally, consider sharing your projects on platforms like GitHub or creating a personal website to showcase your work to potential employers.
Repost from Python for Data Analysts
𝟳 𝗠𝘂𝘀𝘁-𝗛𝗮𝘃𝗲 𝗦𝗸𝗶𝗹𝗹𝘀 𝘁𝗼 𝗟𝗮𝗻𝗱 𝗮 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱😍
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+8
7 Loop Types in JavaScript 👆
Repost from AI Prompts | ChatGPT | Google Gemini | Claude
𝟒 𝐁𝐞𝐬𝐭 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 𝐢𝐧 𝟐𝟎𝟐𝟓 𝐭𝐨 𝐒𝐤𝐲𝐫𝐨𝐜𝐤𝐞𝐭 𝐘𝐨𝐮𝐫 𝐂𝐚𝐫𝐞𝐞𝐫😍
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30-day roadmap to get started with web development
Week 1: HTML and CSS
- Day 1-3: Learn HTML basics (structure, tags, elements).
- Day 4-7: Dive into CSS (styling, selectors, layouts).
Week 2: Advanced CSS and Responsive Design
- Day 8-11: Explore advanced CSS concepts (flexbox, grid).
- Day 12-14: Learn about responsive web design.
Week 3: JavaScript Fundamentals
- Day 15-18: Get started with JavaScript (variables, data types, operators).
- Day 19-21: Study JavaScript functions and control structures.
Week 4: JavaScript DOM Manipulation and Frameworks
- Day 22-25: Understand the Document Object Model (DOM) and how to manipulate it with JavaScript.
- Day 26-28: Explore JavaScript frameworks/libraries (e.g., React, Vue, Angular).
- Day 29-30: Build a simple project combining HTML, CSS, and JavaScript.
Complete Web development bootcamp: https://t.me/webdevcoursefree/3
Here are 5 beginner-friendly web development projects: https://t.me/Programming_experts/455
Remember, practice and building projects are crucial. Adjust the pace based on your learning speed, and feel free to delve deeper into areas that interest you. Web development is an ongoing learning process. Good luck!
ENJOY LEARNING 👍👍
Repost from AI Prompts | ChatGPT | Google Gemini | Claude
𝐋𝐞𝐚𝐫𝐧 𝟔 𝐇𝐢𝐠𝐡-𝐈𝐧𝐜𝐨𝐦𝐞 𝐒𝐤𝐢𝐥𝐥𝐬 𝐟𝐨𝐫 𝐅𝐑𝐄𝐄 𝐰𝐢𝐭𝐡 𝐓𝐡𝐞𝐬𝐞 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐂𝐡𝐚𝐧𝐧𝐞𝐥𝐬!😍
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Top free Data Science resources
1. CS109 Data Science
http://cs109.github.io/2015/pages/videos.html
2. Machine Learning with Python
https://www.freecodecamp.org/learn/machine-learning-with-python/
3. Learning From Data from California Institute of Technology
http://work.caltech.edu/telecourse
4. Mathematics for Machine Learning by University of California, Berkeley
https://gwthomas.github.io/docs/math4ml.pdf?fbclid=IwAR2UsBgZW9MRgS3nEo8Zh_ukUFnwtFeQS8Ek3OjGxZtDa7UxTYgIs_9pzSI
5. Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravindran Kannan
https://www.cs.cornell.edu/jeh/book.pdf?fbclid=IwAR19tDrnNh8OxAU1S-tPklL1mqj-51J1EJUHmcHIu2y6yEv5ugrWmySI2WY
6. Python Data Science Handbook
https://jakevdp.github.io/PythonDataScienceHandbook/?fbclid=IwAR34IRk2_zZ0ht7-8w5rz13N6RP54PqjarQw1PTpbMqKnewcwRy0oJ-Q4aM
7. CS 221 ― Artificial Intelligence
https://stanford.edu/~shervine/teaching/cs-221/
8. Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science
https://ocw.mit.edu/courses/mathematics/18-s096-topics-in-mathematics-of-data-science-fall-2015/lecture-notes/MIT18_S096F15_TenLec.pdf
9. Python for Data Analysis by Boston University
https://www.bu.edu/tech/files/2017/09/Python-for-Data-Analysis.pptx
10. Data Mining bu University of Buffalo
https://cedar.buffalo.edu/~srihari/CSE626/index.html?fbclid=IwAR3XZ50uSZAb3u5BP1Qz68x13_xNEH8EdEBQC9tmGEp1BoxLNpZuBCtfMSE
Credits: https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z
Repost from Python for Data Analysts
𝐄𝐚𝐫𝐧 𝐅𝐑𝐄𝐄 𝐎𝐫𝐚𝐜𝐥𝐞 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 𝟐𝟎𝟐𝟓 — 𝐂𝐥𝐨𝐮𝐝, 𝐀𝐈 & 𝐃𝐚𝐭𝐚!😍
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⌨️ Grammar Correction using Python
