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

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

Network:Data Analytics India6 945 Education3 328...

📈 Analytical overview of Telegram channel Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 52 002 subscribers.

According to the latest data from 30 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 407 over the last 30 days and by 51 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 6.36%. Within the first 24 hours after publication, content typically collects 1.23% reactions from the total number of subscribers.
Post reach: On average, each post receives 3 306 views. Within the first day, a publication typically gains 638 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 7.
Thematic interests: Content is focused on key topics such as analyst, |--, excel, visualization, analytic.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Data Analysis Useful Resources #dataanalysis #dataanalysisbooks #sqlbooks #pythonbooks #tableau #powerbi #datavisualization For promotions: @coderfun”

Thanks to the high frequency of updates (latest data received on 01 July, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.

52 002

Subscribers

+5124 hours

+937 days

+40730 days

3 306

Post views

~ 63824 hours

~ 92548 hours

6.36%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

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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 :)

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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

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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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Skills need for everyday data analysis jobs

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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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Supercharge Your Career: Master SQL Data Analytics for High-Paying Tech Jobs! 🚀 "Introduction to SQL for Data Analytics" is a 2-hour workshop by Piyush Garg, by IIT Jodhpur Alumni and former Software Engineer at Optum. 🗓️ Date: 15th March ⏰ Time: 8 PM to 10 PM What will you learn? 📕 ✅introduction to SQL ✅Data Visualization with SQL ✅Basic SQL syntax and structure ✅Introduction To MYSQL Learn from tech experts, acquire new skills, and connect with like-minded individuals in the field. Register Here: https://tally.so/r/meedLO Only a few seats left ⚠️

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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.

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