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
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Network:📈 Analytical overview of Telegram channel Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources
Channel Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources (@learndataanalysis) in the English language segment is an active participant. Currently, the community unites 53 308 subscribers, ranking 3 302 in the Education category and 6 460 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 53 308 subscribers.
According to the latest data from 29 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 59 over the last 30 days and by -14 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 7.05%. Within the first 24 hours after publication, content typically collects N/A% reactions from the total number of subscribers.
- Post reach: On average, each post receives 0 views. Within the first day, a publication typically gains 0 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 0.
- 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 30 October, 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.
53 308
Subscribers
-1424 hours
-497 days
+5930 days
Posts Archive
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“While noting that I labeled myself as proficient in SQL, I can’t tell you the difference right off the top of my head. A quick search to refresh my memory on JOINs would enable me to answer that for you. While I may not remember 100% of the details of SQL, I am not afraid to do research for a question or process I don’t have a clear answer to.” 😂
𝟱 𝗙𝗥𝗘𝗘 𝗧𝗲𝗰𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗙𝗿𝗼𝗺 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁, 𝗔𝗪𝗦, 𝗜𝗕𝗠, 𝗖𝗶𝘀𝗰𝗼, 𝗮𝗻𝗱 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱. 😍
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I said:
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I blacked out.
She smiled. I think it was pity.
Lesson?
Lie if you must. But memorize the script.
And never lie about tech. They will test you immediately.
5 misconceptions about data analytics (and what's actually true):
❌ The more sophisticated the tool, the better the analyst
✅ Many analysts do their jobs with "basic" tools like Excel
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✅ You need to be able to tell a story with the data
❌ You need super advanced math skills
✅ Understanding basic math and statistics is a good place to start
❌ Data is always clean and accurate
✅ Data is never clean and 100% accurate (without lots of prep work)
❌ You'll work in isolation and not talk to anyone
✅ Communication with your team and your stakeholders is essential
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In Excel, you'd use =AVERAGE(TableName[ColumnName]) to find the average.
In Python:
TableName['ColumnName'].mean()
One line.
Works even if you have 10 million rows.
Data Analyst Tech stack
Data Structure in Python
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Repost from Python Projects & Resources
𝟰 𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍
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For data analysts working with Python, mastering these top 10 concepts is essential:
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2. Data Cleaning and Preprocessing: Learn techniques for cleaning and preprocessing data, including handling missing values, removing duplicates, and standardizing data formats.
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8. Data Wrangling with Regular Expressions: Understand how to use regular expressions (regex) in Python to extract, clean, and manipulate text data efficiently.
9. SQL and Database Integration: Acquire basic SQL skills for querying databases directly from Python using libraries like SQLAlchemy or integrating with databases such as SQLite or MySQL.
10. Web Scraping and API Integration: Explore methods for retrieving data from websites using web scraping libraries like BeautifulSoup or interacting with APIs to access and analyze data from various sources.
Give credits while sharing: https://t.me/pythonanalyst
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Projects to boost your resume for data roles
