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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

前往频道在 Telegram

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

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📈 Telegram 频道 Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources 的分析概览

频道 Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources (@learndataanalysis) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 51 869 名订阅者,在 教育 类别中位列第 3 355,并在 印度 地区排名第 7 219

📊 受众指标与增长动态

невідомо 创建以来,项目保持高速增长,吸引了 51 869 名订阅者。

根据 16 六月, 2026 的最新数据,频道保持稳定运转。过去 30 天订阅人数变化为 537,过去 24 小时变化为 19,整体触达仍然可观。

  • 认证状态: 未认证
  • 互动率 (ER): 平均受众互动率为 7.21%。内容发布后 24 小时内通常能获得 1.26% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 3 740 次浏览,首日通常累积 654 次浏览。
  • 互动与反馈: 受众积极参与,单帖平均反应数为 7
  • 主题关注点: 内容集中在 analyst, |--, excel, visualization, analytic 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台:
Data Analysis Useful Resources #dataanalysis #dataanalysisbooks #sqlbooks #pythonbooks #tableau #powerbi #datavisualization For promotions: @coderfun

凭借高频更新(最新数据采集于 17 六月, 2026),频道始终保持新鲜度与高覆盖。分析显示受众积极互动,使其成为 教育 类别中的关键影响点。

51 869
订阅者
+1924 小时
+1567
+53730
帖子存档
Data Analyst is NOT a Business Analyst. BOTH Works with DATA,but the way they do it is Vastly Different. Data Analyst Mainly :- ✔️ Work in DATABASE. ✔️ Use SQL. ✔️ Propose actionable INSIGHTS. ✔️ Report to Team Memeber's. Business Analyst Mainly :- ✔️ Work in SPREADSHEET. ✔️ Use EXCEL. ✔️ Make Strategic Recommendations. ✔️ Report to Upper Management. Awareness of the difference's between Data Analyst & Business Analyst.

4. Learn Tableau Software: Tableau Public Learn with Tableau Public website and YouTube channels such as Alex the Analyst, Andy Kriebel, and Tableau Tim Learn Data Analytics

If I need to teach someone data analytics from the basics, here is my strategy: 1. I will first remove the fear of tools from that person 2. i will start with the excel because it looks familiar and easy to use 3. I put more emphasis on projects like at least 5 to 6 with the excel. because in industry you learn by doing things 4. I will release the person from the tutorial hell and move into a more action oriented person 5. Then I move to the sql because every job wants it , even with the ai tools you need strong understanding for it if you are going to use it daily 6. After strong understanding, I will push the person to solve 100 to 150 Sql problems from basic to advance 7. It helps the person to develop the analytical thinking 8. Then I push the person to solve 3 case studies as it helps how we pull the data in the real life 9. Then I move the person to power bi to do again 5 projects by using either sql or excel files 10. Now the fear is removed. 11. Now I push the person to solve unguided challenges and present them by video recording as it increases the problem solving, communication and data story telling skills 12. Further it helps you to clear case study round given by most of the companies 13. Now i help the person how to present them in resume and also how these tools are used in real world. 14. You know the interesting fact, all of above is present free in youtube and I also mentor the people through existing youtube videos. 15. But people stuck in the tutorial hell, loose motivation , stay confused that they are either in the right direction or not. 16. As a personal mentor , I help them to get of the tutorial hell, set them in the right direction and they stay motivated when they start to see the difference before amd after mentorship I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope this helps you 😊

3. Learn SQL Software: MySQL Free courses: Khan Academy, W3Schools, SQL Bolt, SQL Zoo, Luke Barousse and Alex the Analyst on YouTube Telegram channel: SQL for data analysis Practice problems: Hacker Rank, Leet Code, Data Lemur Games: SQL Island, SQL Murder Mystery Learn Data Analytics

2. Learn Microsoft Excel: Software: Microsoft Excel Online (it’s not the best, but it will do.) YouTube & Telegram channels to learn: Teacher’s Tech, Excel for Data Analysis, Kevin Stratvert, Leila Gharani, Alex the Analyst Learn Data Analytics

Full Data Analyst Roadmap (with free resources): 1. Optimize LinkedIn Add a friendly, professional profile photo; create a headline that starts with the job title you want (data analyst, not aspiring); add a banner photo (Canva can help with this); create an about section that goes deeper into your skills and background and why you’re good at what you do (add some personality); Highlight transferrable skills in your work and education history sections, quantify your impact where applicable; add your portfolio and anything else you want to show off to your featured section (once you have a portfolio); Use keywords throughout including tools, years of experience, and job titles (data analyst) Learn Data Analytics

🤯🥳ADVANTAGES OF LANDING INTO DATA ANALYTICS FIELD 1. Diverse Career Opportunities: Data analytics opens doors to various career paths 🌐. 2. High Demand: The field is in high demand, ensuring job stability 📈. 3. Lucrative Salaries: Data analysts often enjoy competitive salaries 💰. 4. Problem Solving: Analyzing data allows you to tackle complex problems with precision 🧩. 5. Industry Versatility: Applicable across industries, from healthcare to finance 🏥💼. 6. Continuous Learning: Constantly evolving field, ensuring ongoing skill development 📚. 7. Informed Decision-Making: Empowers businesses to make data-driven decisions 📊. 8. Global Impact: Contribute to solving real-world challenges on a global scale 🌍. 9. Flexibility: Opportunities for remote work and flexible schedules ⌚. 10. Community Engagement: Connect with a vibrant community of data enthusiasts 👩‍💻👨‍💻.

WebScraping with Gen AI During this session, we'll explore the following topics: 1️⃣ Basics of Web Scraping: Understand the f
WebScraping with Gen AI During this session, we'll explore the following topics: 1️⃣ Basics of Web Scraping: Understand the fundamental concepts and techniques of web scraping and its legal and ethical considerations. 2️⃣ Scraping with Gen AI: Discover how Gen AI revolutionizes the web scraping landscape with real-world examples. 3️⃣ Jina Reader API: Get acquainted with the Jina Reader API, a powerful tool for obtaining LLM-friendly input from URLs or web searches. 4️⃣ ScrapeGraphAI: Dive into ScrapeGraphAI, a groundbreaking Python library that combines LLMs and direct graph logic for creating robust scraping pipelines. Event Details: 🗓 Date: 22 June, Saturday ⏰ Time: 11:00 AM IST 🔗 Register now: https://www.buildfastwithai.com/events/web-scraping-with-gen-ai Connect with Founder from IIT Delhi; https://www.linkedin.com/in/satvik-paramkusham/

Why learn Excel? Why learn SQL? Why learn a BI tool? Here's why... Excel → Great foundation for the other data tools → It is assumed you know spreadsheets in most data jobs → Just because you don't need it, other teams in the company might SQL → Chances are data is stored in a warehouse. This is how you get it → This is how you clean, manipulate, and aggregate the data for reporting BI Tool → Format the data so it's easier to read → This is specifically for visualizing data → This is where you can SHOW insights and give direction Learning these gets you from stagnant data to insights that answer business questions BONUS: Don't focus on which tool is best. Pick one you like to work with and focus more on becoming good at what you picked I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope this helps you 😊

Selected Scenario Question: Scenario: You are working as a data analyst for a retail company. The company wants to understand the sales performance across different regions and product categories. You have access to a SQL database that stores order details and a Power BI setup for reporting. Your task is to create a comprehensive report that shows: Total sales by product category. Total sales by region. Total number of orders placed by each customer. Identify the top 5 products contributing to sales in each region. Comprehensive Answer: Step 1: SQL Queries to Retrieve Data Total Sales by Product Category: SELECT ProductCategory, SUM(SalesAmount) AS TotalSales FROM Orders GROUP BY ProductCategory; Total Sales by Region: SELECT Region, SUM(SalesAmount) AS TotalSales FROM Orders GROUP BY Region; Total Number of Orders Placed by Each Customer: SELECT CustomerID, COUNT(*) AS TotalOrders FROM Orders GROUP BY CustomerID; Top 5 Products Contributing to Sales in Each Region: SELECT Region, ProductID, ProductName, SUM(SalesAmount) AS TotalSales FROM Orders GROUP BY Region, ProductID, ProductName ORDER BY Region, TotalSales DESC LIMIT 5; Step 2: Import Data into Power BI Load Data: Open Power BI Desktop. Use the "Get Data" feature to connect to your SQL database. Import the result sets from the SQL queries into Power BI. Create Relationships (if necessary): Ensure that the data tables are properly related. For example, link the Orders table to Customers, Products, and Regions tables if they exist separately. Step 3: Create Visualizations Total Sales by Product Category: Create a bar chart. Drag ProductCategory to the Axis. Drag TotalSales to the Values. Total Sales by Region: Create a pie chart. Drag Region to the Legend. Drag TotalSales to the Values. Total Number of Orders Placed by Each Customer: Create a table. Drag CustomerID to the Rows. Drag TotalOrders to the Values. Top 5 Products Contributing to Sales in Each Region: Create a clustered bar chart. Drag Region to the Axis. Drag ProductName to the Legend. Drag TotalSales to the Values. Apply a Top N filter to show only the top 5 products in each region. Step 4: Optimize Performance Data Model Optimization: Reduce the number of columns and rows by filtering unnecessary data. Use summarized tables to pre-aggregate data. DAX Optimization: Simplify calculations by using measures and avoiding complex DAX queries. Visualization Optimization: Limit the number of visuals on each report page. Avoid using too many slicers or custom visuals that can slow down the performance. Scheduled Refresh: Set up scheduled refreshes to ensure the data is up-to-date without manual intervention. By following these steps, you will create a comprehensive and optimized Power BI report that provides valuable insights into sales performance across different regions and product categories for the retail company. I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope this helps you

To become a successful data analyst, you need a combination of technical skills, analytical skills, and soft skills. Here are some key skills required to excel in a data analyst role: 1. Statistical Analysis: Understanding statistical concepts and being able to apply them to analyze data sets is essential for a data analyst. Knowledge of probability, hypothesis testing, regression analysis, and other statistical techniques is important. 2. Data Manipulation: Proficiency in tools like SQL for querying databases and manipulating data is crucial. Knowledge of data cleaning, transformation, and preparation techniques is also important. 3. Data Visualization: Being able to create meaningful visualizations using tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn is essential for effectively communicating insights from data. 4. Programming: Strong programming skills in languages like Python or R are often required for data analysis tasks. Knowledge of libraries like Pandas, NumPy, and scikit-learn in Python can be beneficial. 5. Machine Learning(optional):  Understanding machine learning concepts and being able to apply algorithms for predictive modeling, clustering, and classification tasks is becoming increasingly important for data analysts. 6. Database Management: Knowledge of database systems like MySQL, PostgreSQL, or MongoDB is useful for working with large datasets and understanding how data is stored and retrieved. 7. Critical Thinking: Data analysts need to be able to think critically and approach problems analytically. Being able to identify patterns, trends, and outliers in data is important for drawing meaningful insights. 8. Business Acumen: Understanding the business context and objectives behind the data analysis is crucial. Data analysts should be able to translate data insights into actionable recommendations for business decision-making. 9. Communication Skills: Data analysts need to effectively communicate their findings to non-technical stakeholders. Strong written and verbal communication skills are essential for presenting complex data analysis results in a clear and understandable manner. 10. Continuous Learning: The field of data analysis is constantly evolving, so a willingness to learn new tools, techniques, and technologies is important for staying current and adapting to changes in the industry. I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope it helps :)

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How to create passive income on Telegram? You can make it with @Whale! 🥰 The best part is that you can invite as many friend
How to create passive income on Telegram? You can make it with @Whale! 🥰 The best part is that you can invite as many friends as you want and make tons of money while they play 🎲 What does your income consist of and how does it work? 🌟 You receive 10% of Whale's earnings from each direct referral. 🌟 1% for each 2nd level referral. 🌟 Monthly paid earnings in $TON. The more friends you invite, the more chances you have to hit the big jackpot — get a share of the @whale jackpot when someone wins it! Sometimes it happens 👍 Referrals are counted when:Your friends follow your referral link. Their wallets and Telegram accounts were not previously members of the Whale system. They link their Telegram account to the bot. They participate in some Whale games. How to invite friends? Get a unique invitation link by clicking “Earn” in the application itself or in the bot, and share this link with your friends! 🐳

I finally got my first Data Analytics JOB ....said no one ever after ⟶ Applying to just a handful of jobs ⟶ Overlooking the importance of problem-solving skills ⟶ Ignoring business acumen ⟶ Dismissing the advice of industry experts ⟶ Enrolling in a "JOB GUARANTEE Course" ⟶ Spending a fortune on courses with minimal hands-on experience The reality is If you want your FIRST job, you need to ⟶ gain practical experience ⟶ develop a strong understanding of business acumen ⟶ master "problem-solving" and "attention to detail" skills You may not be an expert in EVERYTHING, but you should at least be proficient in the basics and have a broad understanding of key concepts. If you know WHAT to do, you can figure out HOW to do it later. I have created 100-Day Roadmap & Resources for Data Analyst 👇👇 https://topmate.io/analyst/981703 Hope it helps :)

The key to starting your data analysis career: ❌It's not your education ❌It's not your experience It's how you apply these principles: 1. Learn the job through "doing" 2. Build a portfolio 3. Make yourself known No one starts an expert, but everyone can become one. If you're looking for a career in data analysis, start by: ⟶ Watching videos ⟶ Reading experts advice ⟶ Doing internships ⟶ Building a portfolio ⟶ Learning from seniors You'll be amazed at how fast you'll learn and how quickly you'll become an expert. So, start today and let the data analysis career begin I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope it helps :)