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Data Analysis Useful Resources #dataanalysis #dataanalysisbooks #sqlbooks #pythonbooks #tableau #powerbi #datavisualization For promotions: @coderfun
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📈 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 53 310 subscribers, ranking 3 304 in the Education category and 6 419 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 53 310 subscribers.
According to the latest data from 03 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 23 over the last 30 days and by -1 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 7.12%. Within the first 24 hours after publication, content typically collects 1.16% reactions from the total number of subscribers.
- Post reach: On average, each post receives 0 views. Within the first day, a publication typically gains 616 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 04 November, 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.
53 310
Subscribers
-124 hours
-167 days
+2330 days
Posts Archive
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!
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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
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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.
Hello everyone here is everything that you need to know if you are planning to learn Business Analytics
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https://t.me/analystcommunity/3
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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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Skills need for everyday data analysis jobs
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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+1Here 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.
