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Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources

Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources

Kanalga Telegram’da o‘tish

Data Analysis Useful Resources #dataanalysis #dataanalysisbooks #sqlbooks #pythonbooks #tableau #powerbi #datavisualization For promotions: @coderfun

Ko'proq ko'rsatish

📈 Telegram kanali Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources analitikasi

Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources (@learndataanalysis) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 51 866 obunachidan iborat bo'lib, Taʼlim toifasida 3 355-o'rinni va Hindiston mintaqasida 7 219-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 51 866 obunachiga ega bo‘ldi.

16 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 537 ga, so‘nggi 24 soatda esa 19 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya o‘rtacha 7.21% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.26% ini tashkil etuvchi reaksiyalarni to‘playdi.
  • Post qamrovi: Har bir post o‘rtacha 3 740 marta ko‘riladi; birinchi sutkada odatda 654 ta ko‘rish yig‘iladi.
  • Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 7 ta reaksiya keladi.
  • Tematik yo‘nalishlar: Kontent analyst, |--, excel, visualization, analytic kabi asosiy mavzularga jamlangan.

📝 Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
Data Analysis Useful Resources #dataanalysis #dataanalysisbooks #sqlbooks #pythonbooks #tableau #powerbi #datavisualization For promotions: @coderfun

Yuqori yangilanish chastotasi (oxirgi ma’lumot 17 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.

51 866
Obunachilar
+1924 soatlar
+1567 kunlar
+53730 kunlar
Postlar arxiv
Do you want to know the skills required before you apply for data analytics jobs?
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Best practices for writing SQL queries: Join for more: https://t.me/learndataanalysis 1- Write SQL keywords in capital letters. 2- Use table aliases with columns when you are joining multiple tables. 3- Never use select *, always mention list of columns in select clause. 4- Add useful comments wherever you write complex logic. Avoid too many comments. 5- Use joins instead of subqueries when possible for better performance. 6- Create CTEs instead of multiple sub queries , it will make your query easy to read. 7- Join tables using JOIN keywords instead of writing join condition in where clause for better readability. 8- Never use order by in sub queries , It will unnecessary increase runtime. 9- If you know there are no duplicates in 2 tables, use UNION ALL instead of UNION for better performance. SQL Basics: https://t.me/sqlanalyst/105

Here are some Excel shortcuts that are commonly used by data analysts: 1. Ctrl + C: Copy 2. Ctrl + V: Paste 3. Ctrl + X: Cut 4. Ctrl + Z: Undo 5. Ctrl + Y: Redo 6. Ctrl + S: Save 7. Ctrl + F: Find 8. Ctrl + H: Replace 9. Ctrl + Arrow Keys: Navigate to the edge of data 10. Ctrl + Shift + Arrow Keys: Select data range 11. Ctrl + Home: Go to cell A1 12. Ctrl + End: Go to last cell with data 13. Ctrl + Shift + L: Toggle filters 14. Alt + ; : Select visible cells only 15. F2: Edit active cell 16. Ctrl + Shift + Enter: Enter an array formula 17. Ctrl + D: Fill down 18. Ctrl + R: Fill right 19. Ctrl + 1: Format cells dialog box 20. Ctrl + Shift + 1, 2, 3, etc.: Apply different number formats These shortcuts can significantly increase your efficiency when working with Excel as a data analyst. One of the very important tool that a data analyst must be aware of is Excel

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My journey as a data analyst so far: https://www.linkedin.com/posts/sql-analysts_today-i-decided-to-share-my-journey-in-data-activity-7169341885914853376-GWnd?utm_source=share&utm_medium=member_android Feel free to comment your experience as well. I usually don't share my personal journey online but sometimes even small things like this help others. I hope you guys can relate with me ❤️

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🚀👉Data Analytics skills and projects to add in a resume to get shortlisted 1. Technical Skills: Proficiency in data analysis tools (e.g., Python, R, SQL). Data visualization skills using tools like Tableau or Power BI. Experience with statistical analysis and modeling techniques. 2. Data Cleaning and Preprocessing: Showcase skills in cleaning and preprocessing raw data for analysis. Highlight expertise in handling missing data and outliers effectively. 3. Database Management: Mention experience with databases (e.g., MySQL, PostgreSQL) for data retrieval and manipulation. 4. Machine Learning: If applicable, include knowledge of machine learning algorithms and their application in data analytics projects. 5. Data Storytelling: Emphasize your ability to communicate insights effectively through data storytelling. 6. Big Data Technologies: If relevant, mention experience with big data technologies such as Hadoop or Spark. 7. Business Acumen: Showcase an understanding of the business context and how your analytics work contributes to organizational goals. 8. Problem-Solving: Highlight instances where you solved business problems through data-driven insights. 9. Collaboration and Communication: Demonstrate your ability to work in a team and communicate complex findings to non-technical stakeholders. 10. Projects: List specific data analytics projects you've worked on, detailing the problem, methodology, tools used, and the impact on decision-making. 11. Certifications: Include relevant certifications such as those from platforms like Coursera, edX, or industry-recognized certifications in data analytics. 12. Continuous Learning: Showcase any ongoing education, workshops, or courses to display your commitment to staying updated in the field. 💼Tailor your resume to the specific job description, emphasizing the skills and experiences that align with the requirements of the position you're applying for.

Best practices for writing SQL queries: Join for more: https://t.me/learndataanalysis 1- Write SQL keywords in capital letters. 2- Use table aliases with columns when you are joining multiple tables. 3- Never use select *, always mention list of columns in select clause. 4- Add useful comments wherever you write complex logic. Avoid too many comments. 5- Use joins instead of subqueries when possible for better performance. 6- Create CTEs instead of multiple sub queries , it will make your query easy to read. 7- Join tables using JOIN keywords instead of writing join condition in where clause for better readability. 8- Never use order by in sub queries , It will unnecessary increase runtime. 9- If you know there are no duplicates in 2 tables, use UNION ALL instead of UNION for better performance. SQL Basics: https://t.me/sqlanalyst/105

Avoid directly copying YouTube projects onto your resume because if everyone looks the same, recruiters might discard resumes. Instead, for eg, let's say you are working on a SQL case study, download a dataset from Kaggle (usually a CSV file), set up a Postgre/MySQL database, connect it with the data, and prompt ChatGPT with questions ranging from basic to advanced SQL. Solve the questions step by step. When using PowerBI, connect to the database and create a compelling dashboard. Don't just upload the dataset; employ DAX queries, statistical functions, and avoid relying solely on drag-and-drop features. Use Formatting section to do creative stuff and add your unique element in the project. ENJOY LEARNING 👍👍

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