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不要被骗子欺骗!Telemetrio 会找到并标记这些频道 👉 如果想查看标记,请订阅

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 53 313 名订阅者,在 教育 类别中位列第 3 298,并在 印度 地区排名第 6 417 位。

📊 受众指标与增长动态

自 невідомо 创建以来,项目保持高速增长,吸引了 53 313 名订阅者。

根据 02 十一月, 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 44,过去 24 小时变化为 20,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 7.12%。内容发布后 24 小时内通常能获得 1.16% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 0 次浏览,首日通常累积 616 次浏览。
  • 互动与反馈: 受众积极参与,单帖平均反应数为 0。
  • 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 03 十一月, 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。

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53 313
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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.

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study-guide-data-visualization-with-python.pdf3.87 KB

Data visualization is one of the steps of the data science process, which states that after data has been collected, processed and modeled, it must be visualized for conclusions to be made. When a data scientist is writing advanced predictive analytics or machine learning (ML) algorithms, it becomes important to visualize the outputs to monitor results and ensure that models are performing as intended. This is because visualizations of complex algorithms are generally easier to interpret than numerical outputs.

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You don't need to know everything about every data tool. Focus on what will help land you your job. For Excel: - IFS (all variations) - XLOOKUP - IMPORTRANGE (in GSheets) - Pivot Tables - Dynamic functions like TODAY() For SQL: - Sum - Group By - Window Functions - CTEs - Joins For Tableau: - Calculated Columns - Sets - Groups - Formatting For Power BI: - Power Query for data transformation - DAX (Data Analysis Expressions) for creating custom calculations - Relationships between tables - Creating interactive and dynamic dashboards - Utilizing slicers and filters effectively

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Data Analysis vs Data Science Data analysis often focuses on interpreting and summarizing existing data, requiring skills like statistical analysis, SQL, and data visualization. On the other hand, data science involves a broader set of skills, including machine learning, predictive modeling, and advanced programming. In essence, data analysis is a subset of data science, with data scientists often having a more extensive toolkit for handling complex and unstructured data. Free Resources to become data analyst -> https://www.linkedin.com/posts/sql-analysts_freecertificates-dataanalysts-python-activity-7113004712412524545-Uw4k Steps to become data scientist -> https://t.me/learndataanalysis/559

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Here is a glimpse of what these resources contains. It covers the top-notch Data Analytics Resources to learn SQL, Python, Ex
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Here is a glimpse of what these resources contains. It covers the top-notch Data Analytics Resources to learn SQL, Python, Excel, Power BI, Data Science, Machine Learning, BI Templates, Data Visualization, Tableau, Artificial Intelligence, and Deep Learning.