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Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources

Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources

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Covering all technical and popular stuff about anything related to Data Science: AI, Big Data, Machine Learning, Statistics, general Math and the applications of former. Ads/ Promo: @love_data

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๐Ÿ“ˆ Analytical overview of Telegram channel Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources

Channel Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources (@sqlproject) in the English language segment is an active participant. Currently, the community unites 39 491 subscribers, ranking 4 749 in the Education category and 10 441 in the India region.

๐Ÿ“Š Audience metrics and dynamics

Since its creation on ะฝะตะฒั–ะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 39 491 subscribers.

According to the latest data from 08 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 202 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 2.66%. Within the first 24 hours after publication, content typically collects 0.96% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 1 052 views. Within the first day, a publication typically gains 378 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
  • Thematic interests: Content is focused on key topics such as analytic, dataset, visualization, sql, learning.

๐Ÿ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
โ€œCovering all technical and popular stuff about anything related to Data Science: AI, Big Data, Machine Learning, Statistics, general Math and the applications of former. Ads/ Promo: @love_dataโ€

Thanks to the high frequency of updates (latest data received on 09 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.

39 491
Subscribers
-1424 hours
+357 days
+20230 days
Posts Archive
๐—œ๐—ป๐—ฑ๐—ถ๐—ฎโ€™๐˜€ ๐—•๐—ถ๐—ด๐—ด๐—ฒ๐˜€๐˜ ๐—›๐—ฎ๐—ฐ๐—ธ๐—ฎ๐˜๐—ต๐—ผ๐—ป | ๐—”๐—œ ๐—œ๐—บ๐—ฝ๐—ฎ๐—ฐ๐˜ ๐—•๐˜‚๐—ถ๐—น๐—ฑ๐—ฎ๐˜๐—ต๐—ผ๐—ป๐Ÿ˜ Participate in the national AI hac
๐—œ๐—ป๐—ฑ๐—ถ๐—ฎโ€™๐˜€ ๐—•๐—ถ๐—ด๐—ด๐—ฒ๐˜€๐˜ ๐—›๐—ฎ๐—ฐ๐—ธ๐—ฎ๐˜๐—ต๐—ผ๐—ป | ๐—”๐—œ ๐—œ๐—บ๐—ฝ๐—ฎ๐—ฐ๐˜ ๐—•๐˜‚๐—ถ๐—น๐—ฑ๐—ฎ๐˜๐—ต๐—ผ๐—ป๐Ÿ˜ Participate in the national AI hackathon under the India AI Impact Summit 2026 Submission deadline: 5th February 2026 Grand Finale: 16th February 2026, New Delhi ๐—ฅ๐—ฒ๐—ด๐—ถ๐˜€๐˜๐—ฒ๐—ฟ ๐—ก๐—ผ๐˜„๐Ÿ‘‡:-  https://pdlink.in/4qQfAOM a flagship initiative of the Government of India ๐Ÿ‡ฎ๐Ÿ‡ณ

โœ… 5 Power BI Projects for Beginners ๐Ÿ“Š๐ŸŸก 1๏ธโƒฃ Sales Dashboard โ†’ Track revenue, profit, top products & sales by region โ†’ Practice: bar charts, slicers, KPIs, date filters 2๏ธโƒฃ Customer Analysis Report โ†’ Analyze customer demographics, behavior, and retention โ†’ Practice: pie charts, filters, clustering 3๏ธโƒฃ HR Analytics Dashboard โ†’ Monitor employee count, attrition rate, department stats โ†’ Practice: cards, stacked bars, trend lines 4๏ธโƒฃ Financial Statement Report โ†’ Visualize income, expenses, cash flow trends โ†’ Practice: waterfall chart, time intelligence 5๏ธโƒฃ Social Media Performance Dashboard โ†’ Track engagement, followers, reach by platform โ†’ Practice: multi-page reports, custom visuals, drill-through ๐Ÿ’ก Tip: Use sample datasets from Kaggle, Microsoft, or mock Excel files. ๐Ÿ‘ Tap โค๏ธ if you found this helpful!

๐—ง๐—ผ๐—ฝ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป๐˜€ ๐—ข๐—ณ๐—ณ๐—ฒ๐—ฟ๐—ฒ๐—ฑ ๐—•๐˜† ๐—œ๐—œ๐—ง ๐—ฅ๐—ผ๐—ผ๐—ฟ๐—ธ๐—ฒ๐—ฒ & ๐—œ๐—œ๐—  ๐— ๐˜‚๐—บ๐—ฏ๐—ฎ๐—ถ๐Ÿ˜ Placement Assistance Wi
๐—ง๐—ผ๐—ฝ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป๐˜€ ๐—ข๐—ณ๐—ณ๐—ฒ๐—ฟ๐—ฒ๐—ฑ ๐—•๐˜† ๐—œ๐—œ๐—ง ๐—ฅ๐—ผ๐—ผ๐—ฟ๐—ธ๐—ฒ๐—ฒ & ๐—œ๐—œ๐—  ๐— ๐˜‚๐—บ๐—ฏ๐—ฎ๐—ถ๐Ÿ˜ Placement Assistance With 5000+ Companies  Deadline: 25th January 2026 ๐——๐—ฎ๐˜๐—ฎ ๐—ฆ๐—ฐ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ & ๐—”๐—œ :- https://pdlink.in/49UZfkX ๐—ฆ๐—ผ๐—ณ๐˜๐˜„๐—ฎ๐—ฟ๐—ฒ ๐—˜๐—ป๐—ด๐—ถ๐—ป๐—ฒ๐—ฒ๐—ฟ๐—ถ๐—ป๐—ด:- https://pdlink.in/4pYWCEK ๐——๐—ถ๐—ด๐—ถ๐˜๐—ฎ๐—น ๐— ๐—ฎ๐—ฟ๐—ธ๐—ฒ๐˜๐—ถ๐—ป๐—ด & ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ :- https://pdlink.in/4tcUPia Hurry..Up Only Limited Seats Available

Everyone thinks being a great data analyst is about advanced algorithms and complex dashboards. But real data excellence comes from methodical habits that build trust and deliver real insights. Here are 20 signs of a truly effective analyst ๐Ÿ‘‡ โœ… They document every step of their analysis โž Clear notes make their work reproducible and trustworthy. โœ… They check data quality before the analysis begins โž Garbage in = garbage out. Always validate first. โœ… They use version control religiously โž Every code change is tracked. Nothing gets lost. โœ… They explore data thoroughly before diving in โž Understanding context prevents costly misinterpretations. โœ… They create automated scripts for repetitive tasks โž Efficiency isnโ€™t a luxuryโ€”itโ€™s a necessity. โœ… They maintain a reusable code library โž Smart analysts never solve the same problem twice. โœ… They test assumptions with multiple validation methods โž One test isnโ€™t enough; they triangulate confidence. โœ… They organize project files logically โž Their work is navigable by anyone, not just themselves. โœ… They seek peer reviews on critical work โž Fresh eyes catch blind spots. โœ… They continuously absorb industry knowledge โž Learning never stops. Trends change too quickly. โœ… They prioritize business-impacting projects โž Every analysis must drive real decisions. โœ… They explain complex findings simply โž Technical brilliance is useless without clarity. โœ… They write readable, well-commented code โž Their work is accessible to others, long after they're gone. โœ… They maintain robust backup systems โž Data loss is never an option. โœ… They learn from analytical mistakes โž Errors become stepping stones, not roadblocks. โœ… They build strong stakeholder relationships โž Data is only valuable when people use it. โœ… They break complex projects into manageable chunks โž Progress happens through disciplined, incremental work. โœ… They handle sensitive data with proper security โž Compliance isnโ€™t optionalโ€”itโ€™s foundational. โœ… They create visualizations that tell clear stories โž A chart without a narrative is just decoration. โœ… They actively seek evidence against their conclusions โž Confirmation bias is their biggest enemy. The best analysts arenโ€™t the ones with the most toolsโ€”theyโ€™re the ones with the most rigorous practices.

๐—ง๐—ผ๐—ฝ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป๐˜€ ๐—ง๐—ผ ๐—š๐—ฒ๐˜ ๐—›๐—ถ๐—ด๐—ต ๐—ฃ๐—ฎ๐˜†๐—ถ๐—ป๐—ด ๐—๐—ผ๐—ฏ ๐—œ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฒ๐Ÿ˜ Opportunities With 500+ Hiring P
๐—ง๐—ผ๐—ฝ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป๐˜€ ๐—ง๐—ผ ๐—š๐—ฒ๐˜ ๐—›๐—ถ๐—ด๐—ต ๐—ฃ๐—ฎ๐˜†๐—ถ๐—ป๐—ด ๐—๐—ผ๐—ฏ ๐—œ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฒ๐Ÿ˜ Opportunities With 500+ Hiring Partners  ๐—™๐˜‚๐—น๐—น๐˜€๐˜๐—ฎ๐—ฐ๐—ธ:- https://pdlink.in/4hO7rWY ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€:- https://pdlink.in/4fdWxJB ๐Ÿ“ˆ Start learning today, build job-ready skills, and get placed in leading tech companies.

๐—™๐—ฅ๐—˜๐—˜ ๐—–๐—ฎ๐—ฟ๐—ฒ๐—ฒ๐—ฟ ๐—–๐—ฎ๐—ฟ๐—ป๐—ถ๐˜ƒ๐—ฎ๐—น ๐—ฏ๐˜† ๐—›๐—–๐—Ÿ ๐—š๐—จ๐—ฉ๐—œ๐Ÿ˜ Prove your skills in an online hackathon, clear tech interviews
๐—™๐—ฅ๐—˜๐—˜ ๐—–๐—ฎ๐—ฟ๐—ฒ๐—ฒ๐—ฟ ๐—–๐—ฎ๐—ฟ๐—ป๐—ถ๐˜ƒ๐—ฎ๐—น ๐—ฏ๐˜† ๐—›๐—–๐—Ÿ ๐—š๐—จ๐—ฉ๐—œ๐Ÿ˜ Prove your skills in an online hackathon, clear tech interviews, and get hired faster Highlightes:-  - 21+ Hiring Companies & 100+ Open Positions to Grab - Get hired for roles in AI, Full Stack, & more Experience the biggest online job fair with Career Carnival by HCL GUVI ๐—ฅ๐—ฒ๐—ด๐—ถ๐˜€๐˜๐—ฒ๐—ฟ ๐—™๐—ผ๐—ฟ ๐—™๐—ฅ๐—˜๐—˜๐Ÿ‘‡:-  https://pdlink.in/4bQP5Ee Hurry Up๐Ÿƒโ€โ™‚๏ธ.....Limited Slots Available

Master ๐—ฃ๐—ผ๐˜„๐—ฒ๐—ฟ ๐—•๐—œ in just ๐Ÿฏ๐Ÿฌ ๐——๐—ฎ๐˜†๐˜€ and boost your data skills! Here's a clear, step-by-step plan for youโ€ฆ ๐—ช๐—ฒ๐—ฒ๐—ธ ๐Ÿญ: ๐—•๐—ฎ๐˜€๐—ถ๐—ฐ๐˜€ ๐—ผ๐—ณ ๐—ฃ๐—ผ๐˜„๐—ฒ๐—ฟ ๐—•๐—œ โž› Day 1-2: Introduction to Power BI, installation, and understanding the interface. โž› Day 3-4: Connecting to data sources and importing data. โž› Day 5-7: Data cleaning and transforming using Power Query Editor. ๐—ช๐—ฒ๐—ฒ๐—ธ ๐Ÿฎ: ๐——๐—ฎ๐˜๐—ฎ ๐— ๐—ผ๐—ฑ๐—ฒ๐—น๐—ถ๐—ป๐—ด โž› Day 8-10: Creating relationships between tables. โž› Day 11-13: DAX basics โ€“ Calculated columns, measures, and key functions like SUM, COUNT. โž› Day 14: Practice building a simple data model. ๐—ช๐—ฒ๐—ฒ๐—ธ ๐Ÿฏ: ๐—ฅ๐—ฒ๐—ฝ๐—ผ๐—ฟ๐˜๐—ถ๐—ป๐—ด ๐—ฎ๐—ป๐—ฑ ๐—ฉ๐—ถ๐˜€๐˜‚๐—ฎ๐—น๐—ถ๐˜‡๐—ฎ๐˜๐—ถ๐—ผ๐—ป โž› Day 15-17: Building visualizations โ€“ bar charts, pie charts, and line graphs. โž› Day 18-20: Using slicers, filters, and drill-through to create interactive reports. โž› Day 21: Design a dashboard โ€“ bringing everything together. ๐—ช๐—ฒ๐—ฒ๐—ธ ๐Ÿฐ: ๐—”๐—ฑ๐˜ƒ๐—ฎ๐—ป๐—ฐ๐—ฒ๐—ฑ ๐—ง๐—ผ๐—ฝ๐—ถ๐—ฐ๐˜€ ๐—ฎ๐—ป๐—ฑ ๐—–๐—ฎ๐—ฝ๐˜€๐˜๐—ผ๐—ป๐—ฒ โž› Day 22-24: Advanced DAX โ€“ Time intelligence, IF statements, and nested functions. โž› Day 25-27: Publishing to Power BI Service, sharing, and setting up scheduled refresh. โž› Day 28-30: Capstone project โ€“ Build a full Power BI report from real data, complete with interactive visuals and insights. You can refer these Power BI Interview Resources to learn more: https://t.me/DataSimplifier Like this post if you want me to continue this Power BI series ๐Ÿ‘โ™ฅ๏ธ Share with credits: https://t.me/sqlspecialist Hope it helps :)

๐—œ๐—œ๐—ง ๐—ฅ๐—ผ๐—ผ๐—ฟ๐—ธ๐—ฒ๐—ฒ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ถ๐—ป ๐——๐—ฎ๐˜๐—ฎ ๐—ฆ๐—ฐ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ ๐—ฎ๐—ป๐—ฑ ๐—”๐—œ ๐—ฃ๐—ฟ๐—ผ๐—ด๐—ฟ๐—ฎ๐—บ๐Ÿ˜ Eligibility: Open
๐—œ๐—œ๐—ง ๐—ฅ๐—ผ๐—ผ๐—ฟ๐—ธ๐—ฒ๐—ฒ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ถ๐—ป ๐——๐—ฎ๐˜๐—ฎ ๐—ฆ๐—ฐ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ ๐—ฎ๐—ป๐—ฑ ๐—”๐—œ ๐—ฃ๐—ฟ๐—ผ๐—ด๐—ฟ๐—ฎ๐—บ๐Ÿ˜ Eligibility: Open to everyone Duration: 6 Months Program Mode: Online Taught By: IIT Roorkee Professors Companies majorly hire candidates with Data Science and AI knowledge these days. Deadline: 25th January 2026 ๐—ฅ๐—ฒ๐—ด๐—ถ๐˜€๐˜๐—ฟ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—Ÿ๐—ถ๐—ป๐—ธ๐Ÿ‘‡:  https://pdlink.in/4qHVFkI Only Limited Seats Available!

๐Ÿ’ก ๐— ๐—ฎ๐—ฐ๐—ต๐—ถ๐—ป๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐—ถ๐˜€ ๐—ผ๐—ป๐—ฒ ๐—ผ๐—ณ ๐˜๐—ต๐—ฒ ๐—บ๐—ผ๐˜€๐˜ ๐—ถ๐—ป-๐—ฑ๐—ฒ๐—บ๐—ฎ๐—ป๐—ฑ ๐˜€๐—ธ๐—ถ๐—น๐—น๐˜€ ๐—ถ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฒ! Start learn
๐Ÿ’ก ๐— ๐—ฎ๐—ฐ๐—ต๐—ถ๐—ป๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐—ถ๐˜€ ๐—ผ๐—ป๐—ฒ ๐—ผ๐—ณ ๐˜๐—ต๐—ฒ ๐—บ๐—ผ๐˜€๐˜ ๐—ถ๐—ป-๐—ฑ๐—ฒ๐—บ๐—ฎ๐—ป๐—ฑ ๐˜€๐—ธ๐—ถ๐—น๐—น๐˜€ ๐—ถ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฒ! Start learning ML for FREE and boost your resume with a certification ๐Ÿ† ๐Ÿ“Š Hands-on learning ๐ŸŽ“ Certificate included ๐Ÿš€ Career-ready skills ๐Ÿ”— ๐—˜๐—ป๐—ฟ๐—ผ๐—น๐—น ๐—™๐—ผ๐—ฟ ๐—™๐—ฅ๐—˜๐—˜ ๐Ÿ‘‡:- https://pdlink.in/4bhetTu ๐Ÿ‘‰ Donโ€™t miss this opportunity

๐Ÿ“ˆ 7 Mini Data Analytics Projects You Should Try 1. YouTube Channel Analysis โ€“ Use public data or your own channel. โ€“ Track views, likes, top content, and growth trends. 2. Supermarket Sales Dashboard โ€“ Work with sales + inventory data. โ€“ Build charts for daily sales, category-wise revenue, and profit margin. 3. Job Posting Analysis (Indeed/LinkedIn) โ€“ Scrape or download job data. โ€“ Identify most in-demand skills, locations, and job titles. 4. Netflix Viewing Trends โ€“ Use IMDb/Netflix dataset. โ€“ Analyze genre popularity, rating patterns, and actor frequency. 5. Personal Expense Tracker โ€“ Clean your own bank/UPI statements. โ€“ Categorize expenses, visualize spending habits, and set budgets. 6. Weather Trends by City โ€“ Use open API (like OpenWeatherMap). โ€“ Analyze temperature, humidity, or rainfall across time. 7. IPL Match Stats Explorer โ€“ Download IPL datasets. โ€“ Explore win rates, player performance, and toss vs outcome insights. Tools to Use: Excel | SQL | Power BI | Python | Tableau React โค๏ธ for more!

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Data Analytics Interview Preparation [Questions with Answers] How did you get your job? I was hired after an internship.  To get the internship, I prepared a bunch for general Python questions (LeetCode etc.) and studied the basics of machine learning (several different algorithms, how they work, when they're useful, metrics  to measure their performance, how to train them in practice etc.).  To get the internship I had to pass a technical interview as well as a take-home machine learning (ML) exercise. Then, it was just a question of doing a good job in the internship!  What are your data related responsibilities in your job?  I work on our recommendation system. Itโ€™s deep learning based. I work on a lot of features to try and  improve it (reinforcement learning & NLP etc). Since I'm in a start-up, it's also up to our team to put the models we design into production. So, after a phase of research & development and model design, in notebooks, it's time to create a real pipeline, by creating scripts.  This enables us to define, train, replace, compare and check the status of the models in production. It's basically all in Python, using Keras/TensorFlow, Pandas, Scikit-learn and NumPy. We also do a lot of analysis for the business team to help them compute metrics of interest (related to  revenue, acquisition etc.). For that, we use an external utility called Metabase. It is is hooked up to our database where we write SQL queries and visualize the results and create dashboards (using  Tableau/Looker etc).  I would say my role is quite "full-stack" since we are all involved from the phase of R&D to deployment on our cluster.  Was it difficult to get this role? I got hired after an internship. If you come from a scientific background, it's not that hard to transition into data science. All the math is something you will probably have seen already (especially if you're  doing maths or physics). So, with some preparation and coding practice, you can start applying to internships.  It took me maybe a month or two of preparation to get some basic ideas of the typical Python data stack (Pandas, Keras, SciKit-learn etc) before I started to send out CVs. Then, if you get an internship, try your best to do the best you can and then maybe you'll be hired after! I have curated best 80+ top-notch Data Analytics Resources ๐Ÿ‘‡๐Ÿ‘‡ https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02 Hope it helps :)

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โœ… Step-by-Step Approach to Learn Data Analytics ๐Ÿ“ˆ๐Ÿง  โžŠ Excel Fundamentals: โœ” Master formulas, pivot tables, data validation, charts, and graphs. โž‹ SQL Basics: โœ” Learn to query databases, use SELECT, FROM, WHERE, JOIN, GROUP BY, and aggregate functions. โžŒ Data Visualization: โœ” Get proficient with tools like Tableau or Power BI to create insightful dashboards. โž Statistical Concepts: โœ” Understand descriptive statistics (mean, median, mode), distributions, and hypothesis testing. โžŽ Data Cleaning & Preprocessing: โœ” Learn how to handle missing data, outliers, and data inconsistencies. โž Exploratory Data Analysis (EDA): โœ” Explore datasets, identify patterns, and formulate hypotheses. โž Python for Data Analysis (Optional but Recommended): โœ” Learn Pandas and NumPy for data manipulation and analysis. โž‘ Real-World Projects: โœ” Analyze datasets from Kaggle, UCI Machine Learning Repository, or your own collection. โž’ Business Acumen: โœ” Understand key business metrics and how data insights impact business decisions. โž“ Build a Portfolio: โœ” Showcase your projects on GitHub, Tableau Public, or a personal website. Highlight the impact of your analysis. ๐Ÿ‘ Tap โค๏ธ for more!

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