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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

رفتن به کانال در Telegram

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

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📈 تحلیل کانال تلگرام Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources

کانال Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources (@learndataanalysis) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 51 866 مشترک است و جایگاه 3 355 را در دسته آموزش و رتبه 7 219 را در منطقه الهند دارد.

📊 شاخص‌های مخاطب و پویایی

از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 51 866 مشترک جذب کرده است.

بر اساس آخرین داده‌ها در تاریخ 16 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 537 و در ۲۴ ساعت گذشته برابر 19 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.

  • وضعیت تأیید: تأیید نشده
  • نرخ تعامل (ER): میانگین تعامل مخاطب 7.21% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.26% واکنش نسبت به کل مشترکان کسب می‌کند.
  • دسترسی پست‌ها: هر پست به طور میانگین 3 740 بازدید دریافت می‌کند. در اولین روز معمولاً 654 بازدید جمع‌آوری می‌شود.
  • واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 7 است.
  • علایق موضوعی: محتوا بر موضوعات کلیدی مانند analyst, |--, excel, visualization, analytic تمرکز دارد.

📝 توضیح و سیاست محتوایی

نویسنده این فضا را محل بیان دیدگاه‌های شخصی توصیف می‌کند:
Data Analysis Useful Resources #dataanalysis #dataanalysisbooks #sqlbooks #pythonbooks #tableau #powerbi #datavisualization For promotions: @coderfun

به لطف به‌روزرسانی‌های پرتکرار (آخرین داده در تاریخ 17 ژوئن, 2026)، کانال همواره به‌روز و دارای دسترسی بالاست. تحلیل‌ها نشان می‌دهد مخاطبان به‌طور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته آموزش تبدیل کرده‌اند.

51 866
مشترکین
+1924 ساعت
+1567 روز
+53730 روز
آرشیو پست ها
Some basic concepts regarding data and database Data is representation of the facts, measurements, figures, or concepts in a formalized manner having no specific meaning. Database is an organized collection of the data stored and can be accessed electronically in a computer system. DBMS are software systems that enable users to store, retrieve, define and manage data in a database easily. RDBMS is a type of DBMS that stores data in a row-based table structure which connects related data elements. SQL is a database query language used for storing and managing data in RDBMS.

This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visua
This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0https://t.me/DataScienceM

Top 5 skills to become a data analyst 1. Proficiency in programming languages like Python, R, or SQL. 2. Strong analytical and problem-solving skills. 3. Ability to work with data manipulation and visualization tools like Pandas, NumPy, Matplotlib, and Seaborn. 4. Knowledge of statistical analysis and machine learning techniques. 5. Effective communication and storytelling skills to convey insights from data to stakeholders. Share with credits: https://t.me/learndataanalysis Hope it helps:)

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Learning data analytics in 2024 can be an exciting and rewarding journey. Here are some steps you can take to start learning data analytics: 1. Understand the Basics: Begin by familiarizing yourself with the basic concepts of data analytics, such as data types, data visualization, statistical analysis, and machine learning. 2. Take Online Courses: There are many online platforms that offer courses in data analytics, such as Coursera, Udemy, and edX. Look for courses that cover topics like data manipulation, data visualization, and predictive modeling. 3. Practice with Real Data: To truly understand data analytics, you need to practice with real datasets. You can find datasets on websites like Kaggle or UCI Machine Learning Repository to work on real-world projects. 4. Learn Tools and Software: Familiarize yourself with popular data analytics tools and software like Python, R, SQL, Tableau, and Power BI. These tools are commonly used in the industry for data analysis. 5. Join Data Analytics Communities: Join online communities like Reddit, LinkedIn groups, or local meetups to connect with other data analysts and learn from their experiences. 6. Build a Portfolio: Create a portfolio of your data analytics projects to showcase your skills to potential employers. Include detailed descriptions of the problem you solved, the data analysis techniques you used, and the results you achieved. 7. Stay Updated: Data analytics is a rapidly evolving field, so it's important to stay updated on the latest trends and technologies. Follow industry blogs, attend webinars, and participate in online forums to stay informed.

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Let me know in comments if you want to continue the series or need answers as well. Also ping your answers if you know any answer. These are bit advanced questions, so I don't expect everyone to solve it but some of you can try 😄

If you’re trying to get a job in data analytics, simplify your roadmap through SPN(skills, portfolio, network) Method: 1. Learn the Skills :- What to Learn: Focus on mastering SQL, Excel, and a data visualization tool like Tableau or Power BI. How to Learn: Utilize online resources, tutorials, and practice exercises to hone your skills. 2. Build Your Portfolio :- Why it's Important: A portfolio showcases your abilities to potential employers. How to Build: Create a free website using platforms like Wix or Wordpress. What to Include: Write-ups of your projects, detailing the business problems you've tackled and the methods you've used. Provide links to your code and dashboards. 3. Expand Your Network :- Why Network: Building connections increases your chances of landing a job. Where to Network: Connect with professionals on LinkedIn, attend local data meetups, and engage in industry-related events. How to Network: Interact genuinely with others, avoiding spammy or impersonal outreach tactics. 4. Stay Positive and Persistent:- Why it Matters: Job hunting can be challenging, but maintaining a positive attitude and persevering is key. How to Stay Motivated: Believe in your abilities and keep pushing forward despite obstacles. Conclusion: Keep Going! Final Encouragement: You've got what it takes. Keep learning, networking, and persevering. You'll reach your goals! If it's useful give us 👍

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How to answer tell me about yourself questions in data analyst interview 👇👇 Hi I’m [Your Name] and I'm passionate about leveraging data to uncover hidden patterns and inform better decision-making. I have strong analytical skills and experience in data wrangling, using SQL for data manipulation, and creating data visualizations with Python libraries like Matplotlib. In my previous role, I analyzed customer behavior data to identify churn factors, resulting in a 15% reduction in customer turnover (Tap to copy) Like this post to get more content like this 😄❤️ Hope it helps :)

Learning Excel for data analytics can be a valuable skill. Here are some steps you can take to learn Excel topics for data analytics: 1. Take an online course: There are many online courses available that specifically focus on Excel for data analytics. Look for courses on platforms like Coursera, Udemy, or LinkedIn Learning. 2. Practice with datasets: The best way to learn Excel is by practicing with real-world datasets. You can find datasets online on websites like Kaggle or data.gov. Practice manipulating and analyzing the data using Excel functions and tools. 3. Learn important functions: Familiarize yourself with important Excel functions for data analysis such as VLOOKUP, INDEX-MATCH, SUMIFS, AVERAGEIFS, COUNTIFS, and PivotTables. 4. Master data visualization: Excel offers powerful tools for data visualization such as charts and graphs. Learn how to create visually appealing and informative charts to present your data effectively. 5. Explore advanced features: Excel has many advanced features that can be useful for data analytics, such as Power Query, Power Pivot, and macros. Take the time to explore these features and understand how they can enhance your data analysis capabilities. 6. Join online communities: Join online forums and communities dedicated to Excel and data analytics. This can be a great way to ask questions, share knowledge, and learn from others who are also interested in data analytics. 7. Practice regularly: Like any skill, learning Excel for data analytics requires regular practice. Set aside time each week to practice your Excel skills and work on different data analysis projects. By following these steps and staying consistent in your practice, you can become proficient in using Excel for data analytics.

Hello everyone here is everything that you need to know if you are planning to learn Business Analytics 👇👇 https://t.me/analystcommunity/3