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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 53 133 subscribers, ranking 3 217 in the Education category and 6 581 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 53 133 subscribers.
According to the latest data from 05 September, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 441 over the last 30 days and by 13 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 5.40%. Within the first 24 hours after publication, content typically collects 1.03% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 867 views. Within the first day, a publication typically gains 545 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 5.
- 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 06 September, 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.
53 133
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Posts Archive
Repost from Data Analyst Jobs
Many people ask this common question “Can I get a job with just SQL and Excel?” or “Can I get a job with just Power BI and Python?”.
The answer to all of those questions is yes.
There are jobs that use only SQL, Tableau, Power BI, Excel, Python, or R or some combination of those.
However, the combination of tools you learn impacts the total number of jobs you are qualified for.
For example, let’s say with just SQL and Excel you are qualified for 10 jobs, but if you add Tableau to that, you are qualified for 50 jobs.
If you have a success rate of landing a job you’re qualified for of 4%, having 5 times as many jobs to go for greatly improves your odds of landing a job.
Does this mean you should go out there and learn every single skill any data analyst job requires?
NO!
It’s about finding the core tools that many jobs want.
And, in my opinion, those tools are SQL, Excel, and a visualization tool.
With these three tools, you are qualified for the majority of entry level data jobs and many higher level jobs.
So, you can land a job with whatever tools you’re comfortable with.
But if you have the three tools above in your toolbelt, you will have many more jobs to apply for and greatly improve your chances of snagging one.
If I had to start learning #dataanalyst all over again, I'd follow this:
1- Learn SQL:
---- Joins (Inner, Left, Full outer and Self)
---- Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
---- Group by and Having clause
---- CTE and Subquery
---- Windows Function (Rank, Dense Rank, Row number, Lead, Lag etc)
2- Learn Excel:
---- Mathematical (COUNT, SUM, AVG, MIN, MAX, etc)
---- Logical Functions (IF, AND, OR, NOT)
---- Lookup and Reference (VLookup, INDEX, MATCH etc)
---- Pivot Table, Filters, Slicers
3- Learn BI Tools:
---- Data Integration and ETL (Extract, Transform, Load)
---- Report Generation
---- Data Exploration and Ad-hoc Analysis
---- Dashboard Creation
4- Learn Python (Pandas) Optional:
---- Data Structures, Data Cleaning and Preparation
---- Data Manipulation
---- Merging and Joining Data (Merging and joining DataFrames -similar to SQL joins)
---- Data Visualization (Basic plotting using Matplotlib and Seaborn)
5 star ratings with 150+ sales, you guys are just amazing. Thanks for showing your immense love and support 😄❤️
5 most asked SQL Interview Questions for Data Engineer jobs
👇👇
https://t.me/sql_engineer/76
Data Analysis Vocabulary.pdf0.97 KB
Complete Roadmap to learn SQL in 2024 👇👇
https://t.me/free4unow_backup/831
𝐇𝐨𝐰 𝐭𝐨 𝐏𝐫𝐞𝐩𝐚𝐫𝐞 𝐭𝐨 𝐁𝐞𝐜𝐨𝐦𝐞 𝐚 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭
𝟏. 𝐄𝐱𝐜𝐞𝐥- Learn formulas, Pivot tables, Lookup, VBA Macros.
𝟐. 𝐒𝐐𝐋- Joins, Windows, CTE is the most important
𝟑. 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈- Power Query Editor(PQE), DAX, MCode, RLS
𝟒. 𝐏𝐲𝐭𝐡𝐨𝐧- Basics & Libraries(mainly pandas, numpy, matplotlib and seaborn libraries)
5. Practice SQL and Python questions on platforms like 𝐇𝐚𝐜𝐤𝐞𝐫𝐑𝐚𝐧𝐤 or 𝐖𝟑𝐒𝐜𝐡𝐨𝐨𝐥𝐬.
6. Know the basics of descriptive statistics(mean, median, mode, Probability, normal, binomial, Poisson distributions etc).
7. Learn to use 𝐀𝐈/𝐂𝐨𝐩𝐢𝐥𝐨𝐭 𝐭𝐨𝐨𝐥𝐬 like GitHub Copilot or Power BI's AI features to automate tasks, generate insights, and improve your projects(Most demanding in Companies now)
8. Get hands-on experience with one cloud platform: 𝐀𝐳𝐮𝐫𝐞, 𝐀𝐖𝐒, 𝐨𝐫 𝐆𝐂𝐏
9. Work on at least two end-to-end projects.
10. Prepare an ATS-friendly resume and start applying for jobs.
11. Prepare for interviews by going through common interview questions on Google and YouTube.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
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Hope this helps you 😊
Websites to practice SQL queries
👇👇
https://www.linkedin.com/posts/sql-analysts_learning-sql-is-not-enough-you-need-to-practice-activity-7217873462596386816-E65q?
Like for more ❤️
Repost from Data Analytics
Someone asked me today if they need to learn Python & Data Structures to become a data analyst. What's the right time to start applying for data analyst interview?
I think this is the common question which many of the other freshers might think of. So, I think it's better to answer it here for everyone's benefit.
The right time to start applying for data analyst positions depends on a few factors:
1. Skills and Experience: Ensure you have the necessary skills (e.g., SQL, Excel, Python/R, data visualization tools like Power BI or Tableau) and some relevant experience, whether through projects, internships, or previous jobs.
2. Preparation: Make sure your resume and LinkedIn profile are updated, and you have a portfolio showcasing your projects and skills. It's also important to prepare for common interview questions and case studies.
3. Job Market: Pay attention to the job market trends. Certain times of the year, like the beginning and middle of the fiscal year, might have more openings due to budget cycles.
4. Personal Readiness: Consider your current situation, including any existing commitments or obligations. You should be able to dedicate time to the job search process.
Generally, a good time to start applying is around 3-6 months before you aim to start a new job. This gives you ample time to go through the application process, which can include multiple interview rounds and potentially some waiting periods.
Also, if you know SQL & have a decent data portfolio, then you don't need to worry much on Python & Data Structures. It's good if you know these but they are not mandatory. You can still confidently apply for data analyst positions without being an expert in Python or data structures. Focus on highlighting your current skills along with hands-on projects in your resume.
Hope it helps :)
I hate to tell you this but...
Bootcamps that tell you they can get you a 6-figure data analyst job within 6 weeks (or even 6 months) are lying to you.
Don't focus on the salary that you might get.
Instead, focus on...
- learning the tools
- starting your portfolio
- revamping your resume
- getting active on LinkedIn
- putting the skills into practice
I guarantee you'll be more successful.
Being analytical is a skill, but it's more of a mindset and a second nature
Focusing on just numbers could be analysis, but doesn't necessarily mean you're analytical. E.g. "Sales dropped in Q1 by 5% as compared to Q1 last year in XYZ Region."
What caused this exactly? Season? Event? Product reviews/quality? Customer service decline? Marketing spend? PR? Supply chain? Stock depletion? Price increase? Rebranding?
Or, when validating the data and understanding the root causes, having a very limited approach. "If that data is missing, it's missing..."
Why is it missing? Is it the source? Is it the business decision to not undertake an activity for a time period? Was it there yesterday? Was it supposed to be there? Who can I talk to for understanding the root cause?
A LOT of business users I know are more analytical than the data people in their teams.
So what makes you analytical?
- It's the questions you ask yourself
- It's the dots you connect
- It's the different avenues you explore
- It's the inferences you make
- It's the bigger picture you look at
It's not just numbers or data.
Hey guys 👋
Since many of you requested for data analytics recorded video lectures, here you go!
👇👇
https://topmate.io/analyst/1068350
It contains comprehensive recorded video lectures on Data Analytics, covering key tools and languages like SQL, Python, Excel, and Power BI along with hands-on projects to ensure you gain practical experience alongside theoretical knowledge.
Please use the above link to avail them!👆
NOTE: -Most data aspirants hoard resources without actually opening them even once! The reason for keeping a small price for these resources is to ensure that you value the content available inside this and encourage you to make the best out of it.
Hope this helps in your data analytics journey... All the best!👍✌️
Data Analysis is not Power BI.
Data Analysis is not Python.
Data Analysis is not Excel.
Data Analysis is not SQL.
Data Analysis is the silent hero pulling strings behind the curtain to transform raw, unstructured data into meaningful insights.
It's an art form that goes beyond the tools. Perhaps it's time we shift our focus from the tools to the art and science of data analysis itself.
10 Steps to Landing a High Paying Job in Data Analytics
1. Learn SQL - joins & windowing functions is most important
2. Learn Excel- pivoting, lookup, vba, macros is must
3. Learn Dashboarding on POWER BI/ Tableau
4. Learn Python basics- mainly pandas, numpy, matplotlib and seaborn libraries
5. Know basics of descriptive statistics
6. With AI/ copilot integrated in every tool, know how to use it and add to your projects
7. Have hands on any 1 cloud platform- AZURE/AWS/GCP
8. WORK on atleast 2 end to end projects and create a portfolio of it
9. Prepare an ATS friendly resume & start applying
10. Attend interviews (you might fail in first 2-3 interviews thats fine),make a list of questions you could not answer & prepare those.
Give more interview to boost your chances through consistent practice & feedback 😄👍
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