Available now! Telegram Research 2025 โ the year's key insights 
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
Show more๐ 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 51 852 subscribers, ranking 3 362 in the Education category and 7 262 in the India region.
๐ Audience metrics and dynamics
Since its creation on ะฝะตะฒัะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 51 852 subscribers.
According to the latest data from 14 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 525 over the last 30 days and by 20 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 7.70%. Within the first 24 hours after publication, content typically collects 1.28% reactions from the total number of subscribers.
- Post reach: On average, each post receives 3 991 views. Within the first day, a publication typically gains 665 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 8.
- Thematic interests: Content is focused on key topics such as analyst, |--, excel, visualization, analytic.
๐ Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
โData Analysis Useful Resources
#dataanalysis
#dataanalysisbooks
#sqlbooks
#pythonbooks
#tableau
#powerbi
#datavisualization
For promotions: @coderfunโ
Thanks to the high frequency of updates (latest data received on 15 June, 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.
51 852
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+52530 days
Posts Archive
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A - Always check your assumptions
B - Backup your data
C - Check your code
D - Do you know your data?
E - Evaluate your results
F - Find the anomalies
G - Get help when you need it
H - Have a backup plan
I - Investigate your outliers
J - Justify your methods
K - Keep your data clean
L - Let your data tell a story
M - Make your visualizations impactful
N - No one knows everything
O - Outline your analysis
P - Practice good documentation
Q - Quality control is key
R - Review your work
S - Stay organized
T - Test your assumptions
U - Use the right tools
V - Verify your results
W - Write clear and concise reports
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Y - Yield to the evidence
Z - Zero in on your findings
If you can master the ABCs of data analysis, you will be well on your way to being a successful Data Analyst.
๐ฆ๐ค๐ ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐๐ ๐ง๐ต๐ฎ๐ ๐๐ฎ๐ป ๐๐ฐ๐๐๐ฎ๐น๐น๐ ๐๐ฒ๐ ๐ฌ๐ผ๐ ๐๐ถ๐ฟ๐ฒ๐ฑ!๐
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2. Python
https://www.learnpython.org/
https://t.me/pythondevelopersindia/873
https://bit.ly/3T7y4ta
https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
3. R
https://www.datacamp.com/courses/free-introduction-to-r
4. Data Structures
https://leetcode.com/study-plan/data-structure/
https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513
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https://www.freecodecamp.org/learn/data-visualization/
https://t.me/Data_Visual/2
https://www.tableau.com/learn/training/20223
https://www.workout-wednesday.com/power-bi-challenges/
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๐. ๐๐ฑ๐๐๐ฅ: ๐๐จ๐ฎ๐ซ ๐๐จ๐ซ๐ ๐๐จ๐จ๐ฅ
Master Excel skills for effective data analysis by focusing on:
ใปCleaning and organizing data
ใปUsing pivot tables for summaries
ใปAdvanced functions like VLOOKUP, INDEX, and MATCH
ใปDesigning impactful visualizations
๐. ๐๐ฎ๐ข๐ฅ๐ ๐๐ญ๐๐ญ๐ข๐ฌ๐ญ๐ข๐๐ฌ ๐
๐จ๐ฎ๐ง๐๐๐ญ๐ข๐จ๐ง
Statistics are essential for interpreting data. Learn:
ใปDescriptive statistics (mean, median, mode)
ใปProbability distributions
ใปHypothesis testing and confidence intervals
๐. ๐๐จ๐ฆ๐ข๐ง๐๐ญ๐ ๐๐ฒ๐ญ๐ก๐จ๐ง ๐จ๐ซ ๐
Choose Python or R to boost your analysis game:
ใปClean and structure datasets
ใปCreate visualizations (Matplotlib, Seaborn, or Tidyverse)
ใปLeverage powerful libraries for in-depth analysis
๐. ๐๐๐ฌ๐ญ๐๐ซ ๐๐๐
SQL is vital for working with databases. Hone these skills:
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ใปBuild insightful dashboards
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ใปCraft compelling, data-driven narratives
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ใปHandle missing values
ใปDetect and manage outliers
ใปNormalize and format data for analysis
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Translate data insights into actionable recommendations:
ใปWrite clear, concise reports
ใปPresent to non-technical audiences
ใปDeliver impactful, data-backed decisions
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Knowing Excel, SQL, PowerBI, Python is great.
But if you donโt know how to "sell" your analysis there's a high chance you'll fail.
Here's what to do:
- Come up with questions to investigate.
- Create easy-to-understand answers.
- Explain what to do next.
It's that simple.
Learn these to become a
1. Data analyst:
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๐SQL
๐Data viz tool (Power BI/Tableau)
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๐Cloud platform (AWS/Azure/GCP)
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๐Python/R
๐Statistics/machine learning
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Guide to Become a Data Analyst!
๐ Foundation: Build Your Basics
1. Understanding Data Fundamentals: Dive into the basics of data types, structures, and formats.
2. Learn Data Tools: Familiarize yourself with popular tools like Excel, SQL, and Python.
3. Master Data Visualization: Develop skills in creating insightful charts and graphs to communicate findings effectively.
4. Introduction to Statistics: Get comfortable with key statistical concepts like mean, median, and standard deviation.
๐ Intermediate: Deepen Your Skills
5. Advanced Data Manipulation: Level up your data wrangling abilities with techniques like pivot tables and data cleaning.
6. Statistical Analysis: Dive deeper into hypothesis testing, regression analysis, and probability distributions.
7. Machine Learning Basics: Explore the fundamentals of machine learning algorithms and their applications in data analysis.
8. Data Storytelling: Hone your ability to craft compelling narratives from data insights.
๐ Advanced: Specialize and Excel
9. Specialize in a Domain: Choose a niche area such as marketing analytics, financial analysis, or healthcare data.
10. Advanced Machine Learning: Deepen your understanding of complex algorithms like neural networks and ensemble methods.
11. Big Data Technologies: Explore tools and platforms for handling large-scale datasets such as Hadoop and Spark.
12. Ethics and Privacy: Understand the ethical considerations and legal implications of handling sensitive data.
๐ฑ ๐ ๐๐๐-๐๐ผ ๐ฆ๐ค๐ ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐๐ ๐๐ผ ๐๐บ๐ฝ๐ฟ๐ฒ๐๐ ๐ฅ๐ฒ๐ฐ๐ฟ๐๐ถ๐๐ฒ๐ฟ๐!๐
If youโre aiming for a Data Analyst, Business Analyst, or Data Scientist role, mastering SQL is non-negotiable. ๐
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Breaking into Data Analysis can be very confusing in 2024!
Should I learn SQL or NoSQL? Tableau or Power BI? Excel or Google Sheets? Python or R?
Fundamental principles are more important than tools:
Understanding data cleaning and preprocessing is more important than SQL vs NoSQL.
Understanding data visualization concepts is more important than Tableau vs Power BI.
Understanding statistical analysis is more important than Excel vs R.
Understanding programming for data manipulation is more important than Python vs R.
Knowing these will allow you to pick up new emerging tools easily.
Stick to fundamentals first.
๐๐ฟ๐ฒ๐ฒ ๐ฉ๐ถ๐ฟ๐๐๐ฎ๐น ๐๐ป๐๐ฒ๐ฟ๐ป๐๐ต๐ถ๐ฝ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป๐ ๐๐ ๐ง๐ผ๐ฝ ๐๐ผ๐บ๐ฝ๐ฎ๐ป๐ถ๐ฒ๐๐
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