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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 moreπ 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 51 866 subscribers, ranking 3 355 in the Education category and 7 219 in the India region.
π Audience metrics and dynamics
Since its creation on Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 51 866 subscribers.
According to the latest data from 16 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 537 over the last 30 days and by 19 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 7.21%. Within the first 24 hours after publication, content typically collects 1.26% reactions from the total number of subscribers.
- Post reach: On average, each post receives 3 740 views. Within the first day, a publication typically gains 654 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.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βData Analysis Useful Resources
#dataanalysis
#dataanalysisbooks
#sqlbooks
#pythonbooks
#tableau
#powerbi
#datavisualization
For promotions: @coderfunβ
Thanks to the high frequency of updates (latest data received on 17 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.
51 866
Subscribers
+1924 hours
+1567 days
+53730 days
Posts Archive
Some basic concepts regarding data and database
Data is representation of the facts, measurements, figures, or concepts in a formalized manner having no
specific meaning.
Database is an organized collection of the data stored and can be accessed electronically in a computer system.
DBMS are software systems that enable users to store, retrieve, define and manage data in a database easily.
RDBMS is a type of DBMS that stores data in a row-based table structure which connects related data elements.
SQL is a database query language used for storing and managing data in RDBMS.
This channels is for Programmers, Coders, Software Engineers.
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7οΈβ£ Deep Learning
8οΈβ£ programming Languages
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Top 5 skills to become a data analyst
1. Proficiency in programming languages like Python, R, or SQL.
2. Strong analytical and problem-solving skills.
3. Ability to work with data manipulation and visualization tools like Pandas, NumPy, Matplotlib, and Seaborn.
4. Knowledge of statistical analysis and machine learning techniques.
5. Effective communication and storytelling skills to convey insights from data to stakeholders.
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Hope it helps:)
SQL Roadmap for Data Analyst
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Learning data analytics in 2024 can be an exciting and rewarding journey. Here are some steps you can take to start learning data analytics:
1. Understand the Basics: Begin by familiarizing yourself with the basic concepts of data analytics, such as data types, data visualization, statistical analysis, and machine learning.
2. Take Online Courses: There are many online platforms that offer courses in data analytics, such as Coursera, Udemy, and edX. Look for courses that cover topics like data manipulation, data visualization, and predictive modeling.
3. Practice with Real Data: To truly understand data analytics, you need to practice with real datasets. You can find datasets on websites like Kaggle or UCI Machine Learning Repository to work on real-world projects.
4. Learn Tools and Software: Familiarize yourself with popular data analytics tools and software like Python, R, SQL, Tableau, and Power BI. These tools are commonly used in the industry for data analysis.
5. Join Data Analytics Communities: Join online communities like Reddit, LinkedIn groups, or local meetups to connect with other data analysts and learn from their experiences.
6. Build a Portfolio: Create a portfolio of your data analytics projects to showcase your skills to potential employers. Include detailed descriptions of the problem you solved, the data analysis techniques you used, and the results you achieved.
7. Stay Updated: Data analytics is a rapidly evolving field, so it's important to stay updated on the latest trends and technologies. Follow industry blogs, attend webinars, and participate in online forums to stay informed.
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Let me know in comments if you want to continue the series or need answers as well. Also ping your answers if you know any answer.
These are bit advanced questions, so I don't expect everyone to solve it but some of you can try π
If youβre 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 ππ
Hi Iβm [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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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.
Hello everyone here is everything that you need to know if you are planning to learn Business Analytics
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