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Coding & Data Science Resources (@codingfreebooks) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 32 421 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 4 025-o'rinni va Hindiston mintaqasida 12 523-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
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25 Avgust, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 244 ga, so‘nggi 24 soatda esa 4 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
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“Official Telegram Channel for Free Coding & Data Science Resources
Admin: @love_data”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 26 Avgust, 2026 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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Kanal postlari
💻 How to Learn Programming in 1 Year – Step by Step 📝✨
✅ Tip 1: Start with a Single Language
Choose one language (Python, JavaScript, or Java) and stick to it. Mastering one deeply beats superficial knowledge of many—Python's great for beginners due to its readability.
✅ Tip 2: Learn the Basics First
Focus on fundamentals:
⦁ Variables & Data Types
⦁ Loops & Conditionals
⦁ Functions / Methods
⦁ Lists, Arrays, Dictionaries / Objects
✅ Tip 3: Practice Small Projects
Build weekly:
⦁ Calculator
⦁ To-do list app
⦁ Simple web scraper
⦁ Guess-the-number game
⦁ Weather API fetcher
✅ Tip 4: Understand Problem-Solving & Logic
Programming is problem-solving:
⦁ Break problems into steps
⦁ Write pseudocode
⦁ Debug carefully—use print statements or debuggers
✅ Tip 5: Learn Version Control
Use Git to track changes, collaborate, and avoid losing work. Commands like git commit, push, and branch are essentials.
✅ Tip 6: Read Others' Code
Explore open-source on GitHub to see pro structure and patterns—start with simple repos like a basic web app.
✅ Tip 7: Practice Coding Challenges
Hit LeetCode, HackerRank, or Codewars daily for logic, algorithms, and speed—aim for 5-10 problems/week.
✅ Tip 8: Understand Key Concepts Deeply
⦁ Object-Oriented Programming (OOP)
⦁ Recursion
⦁ Data Structures – Arrays, Lists, Stacks, Queues, Trees
⦁ Algorithms – Sorting, Searching
✅ Tip 9: Build Real Projects
⦁ Portfolio website
⦁ Chatbot
⦁ Data analysis with Python
⦁ Simple game
⦁ Full-stack app like a blog
✅ Tip 10: Be Consistent & Review
Code daily (30-60 mins), review old code to refine style. Track progress with a journal or GitHub streak.
💬 Tap ❤️ for more!
| 2 |  🔰 10 python one liners | 1 233 |
| 3 |  If you’re a student, graduate, or someone looking for a career switch, read this.
Most people spend months watching random YouTube videos and still don’t become job-ready.
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| 4 | Data Analyst Interview Questions & Preparation Tips
Be prepared with a mix of technical, analytical, and business-oriented interview questions.
1. Technical Questions (Data Analysis & Reporting)
SQL Questions:
How do you write a query to fetch the top 5 highest revenue-generating customers?
Explain the difference between INNER JOIN, LEFT JOIN, and FULL OUTER JOIN.
How would you optimize a slow-running query?
What are CTEs and when would you use them?
Data Visualization (Power BI / Tableau / Excel)
How would you create a dashboard to track key performance metrics?
Explain the difference between measures and calculated columns in Power BI.
How do you handle missing data in Tableau?
What are DAX functions, and can you give an example?
ETL & Data Processing (Alteryx, Power BI, Excel)
What is ETL, and how does it relate to BI?
Have you used Alteryx for data transformation? Explain a complex workflow you built.
How do you automate reporting using Power Query in Excel?
2. Business and Analytical Questions
How do you define KPIs for a business process?
Give an example of how you used data to drive a business decision.
How would you identify cost-saving opportunities in a reporting process?
Explain a time when your report uncovered a hidden business insight.
3. Scenario-Based & Behavioral Questions
Stakeholder Management:
How do you handle a situation where different business units have conflicting reporting requirements?
How do you explain complex data insights to non-technical stakeholders?
Problem-Solving & Debugging:
What would you do if your report is showing incorrect numbers?
How do you ensure the accuracy of a new KPI you introduced?
Project Management & Process Improvement:
Have you led a project to automate or improve a reporting process?
What steps do you take to ensure the timely delivery of reports?
4. Industry-Specific Questions (Credit Reporting & Financial Services)
What are some key credit risk metrics used in financial services?
How would you analyze trends in customer credit behavior?
How do you ensure compliance and data security in reporting?
5. General HR Questions
Why do you want to work at this company?
Tell me about a challenging project and how you handled it.
What are your strengths and weaknesses?
Where do you see yourself in five years?
How to Prepare?
Brush up on SQL, Power BI, and ETL tools (especially Alteryx).
Learn about key financial and credit reporting metrics.(varies company to company)
Practice explaining data-driven insights in a business-friendly manner.
Be ready to showcase problem-solving skills with real-world examples.
React with ❤️ if you want me to also post sample answer for the above questions
Share with credits: https://t.me/sqlspecialist
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| 6 | Python has a built-in topological dependency sorter!🚀
If you're working with tasks that have dependencies — for example, in build systems, CI/CD pipelines, or workflow orchestration — the order of execution often has to be determined manually.
Usually through graphs, DFS,, or custom execution order logic.
But Python's standard library already has graphlib.TopologicalSorter.
ts = TopologicalSorter()
ts.add("deploy", "test")
ts.add("test", "build")
After preparation, the sorter returns the correct execution order.
tuple(ts.static_order())
Result:
("build", "test", "deploy")
Especially useful for workflow management systems, dependency resolution, orchestration systems, and any tasks with a dependency graph.
🔥 TopologicalSorter allows you to solve dependency problems using Python's built-in tools without having to implement graph algorithms manually. | 1 023 |
| 7 | Python has a built-in topological dependency sorter!🚀
If you're working with tasks that have dependencies — for example, in build systems, CI/CD pipelines, or workflow orchestration — the order of execution often has to be determined manually.
Usually through graphs, DFS,, or custom execution order logic.
But Python's standard library already has graphlib.TopologicalSorter.
ts = TopologicalSorter()
ts.add("deploy", "test")
ts.add("test", "build")
After preparation, the sorter returns the correct execution order.
tuple(ts.static_order())
Result:
("build", "test", "deploy")
Especially useful for workflow management systems, dependency resolution, orchestration systems, and any tasks with a dependency graph.
🔥 TopologicalSorter allows you to solve dependency problems using Python's built-in tools without having to implement graph algorithms manually.
#Python #DependencyResolution #WorkflowOrchestration #CICD #BuildSystems #TopologicalSort
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