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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Network:π 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 39 698 subscribers, ranking 4 591 in the Education category and 9 708 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 39 698 subscribers.
According to the latest data from 29 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 36 over the last 30 days and by 17 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 1.87%. Within the first 24 hours after publication, content typically collects 0.73% reactions from the total number of subscribers.
- Post reach: On average, each post receives 744 views. Within the first day, a publication typically gains 290 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
- Thematic interests: Content is focused on key topics such as analytic, dataset, visualization, sql, learning.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βCovering 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_dataβ
Thanks to the high frequency of updates (latest data received on 30 August, 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.
39 698
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+3630 days
Posts Archive
Join fast if interested
Free Stock Marketing Resources
ππ
https://chat.whatsapp.com/FKndaTDDPhZ7DPuBiqAdBI
AI/ML (Daily Schedule) π¨π»βπ»
Morning:
- 9:00 AM - 10:30 AM: ML Algorithms Practice
- 10:30 AM - 11:00 AM: Break
- 11:00 AM - 12:30 PM: AI/ML Theory Study
Lunch:
- 12:30 PM - 1:30 PM: Lunch and Rest
Afternoon:
- 1:30 PM - 3:00 PM: Project Development
- 3:00 PM - 3:30 PM: Break
- 3:30 PM - 5:00 PM: Model Training/Testing
Evening:
- 5:00 PM - 6:00 PM: Review and Debug
- 6:00 PM - 7:00 PM: Dinner and Rest
Late Evening:
- 7:00 PM - 8:00 PM: Research and Reading
- 8:00 PM - 9:00 PM: Reflect and Plan
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ππ
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Stock Market Price prediction using Regression.pdf1.16 MB
Regression_basic_project .pdf1.63 KB
50 SQL Queries for interview.pdf
Free Stock Marketing Resources only for Indian users
ππ
https://chat.whatsapp.com/GMnE6elUlK9B7PfXUuqiHC
A_Practical_Introduction_to_Python_Programming_Heinold.pdf1.98 MB
βWhat dataset should I useβ
Think business scenarios such as:
β’ Product performance
β’ Marketing campaigns
β’ Customer behaviour
β’ Customer retention
β’ Sales performance
For datasets, you can scroll this channel & find the one which suits your interest
SQL Retail sales Project.pdf1.25 KB
π Project Ideas for a data analyst
Customer Segmentation: Analyze customer data to segment them based on their behaviors, preferences, or demographics, helping businesses tailor their marketing strategies.
Churn Prediction: Build a model to predict customer churn, identifying factors that contribute to churn and proposing strategies to retain customers.
Sales Forecasting: Use historical sales data to create a predictive model that forecasts future sales, aiding inventory management and resource planning.
Market Basket Analysis: Analyze
transaction data to identify associations between products often purchased together, assisting retailers in optimizing product placement and cross-selling.
Sentiment Analysis: Analyze social media or customer reviews to gauge public sentiment about a product or service, providing valuable insights for brand reputation management.
Healthcare Analytics: Examine medical records to identify trends, patterns, or correlations in patient data, aiding in disease prediction, treatment optimization, and resource allocation.
Financial Fraud Detection: Develop algorithms to detect anomalous transactions and patterns in financial data, helping prevent fraud and secure transactions.
A/B Testing Analysis: Evaluate the results of A/B tests to determine the effectiveness of different strategies or changes on websites, apps, or marketing campaigns.
Energy Consumption Analysis: Analyze energy usage data to identify patterns and inefficiencies, suggesting strategies for optimizing energy consumption in buildings or industries.
Real Estate Market Analysis: Study housing market data to identify trends in property prices, rental rates, and demand, assisting buyers, sellers, and investors in making informed decisions.
Remember to choose a project that aligns with your interests and the domain you're passionate about.
Data Analyst Roadmap
https://t.me/sqlspecialist/379
ENJOY LEARNING ππ
5β£ Project ideas for a data analyst in the investment banking domain
M&A Deal Analysis: Analyze historical mergers and acquisitions (M&A) data to identify trends, such as deal size, industries involved, or geographical regions. Create visualizations and reports to assist in making informed investment decisions.
Risk Assessment Model: Develop a risk assessment model using financial indicators and market data. Predict potential financial risks for investment opportunities, such as stocks, bonds, or startups, and provide recommendations based on risk levels.
Portfolio Performance Analysis: Evaluate the performance of investment portfolios over time. Calculate key performance indicators (KPIs) like Sharpe ratio, alpha, and beta to assess how well portfolios are performing relative to the market.
Sentiment Analysis for Trading: Use natural language processing (NLP) techniques to analyze news articles, social media posts, and financial reports to gauge market sentiment. Develop trading strategies based on sentiment analysis results.
IPO Analysis: Analyze data related to initial public offerings (IPOs), including company financials, industry comparisons, and market conditions. Create a scoring system or model to assess the potential success of IPO investments.
Hope it helps :)
SQL Practice PDF ππ
It has 24+ SQL Questions to Practice,
With Dataset + Code..β€βπ₯
You can also Download the Dataset from the Link in Page 1...πβ‘
Also Share this π
SQL_Practice.pdf3.89 MB
How to Crack Data Engineering Interviews with these 5 Easy Steps ππ
https://medium.com/@data_analyst/how-to-crack-data-engineering-interviews-with-these-5-easy-steps-a3e997d08e1c?sk=8e4b098c5db666d1c8c642877e1ee058
2 Years of SQL Experience in one article
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https://medium.com/@data_analyst/2-years-of-sql-experience-in-one-article-1af484d262d6
As a data analytics enthusiast, the end goal is not just to learn SQL, Power BI, Python, Excel, etc. but to get a job as a Data Analystπ¨π»
Back then, when I was trying to switch my career into data analytics, I used to keep aside 1:00-1:30 hours of my day aside so that I can utilize those hours to search for job openings related to Data analytics and Business Intelligence.
Before going to bed, I used to utilize the first 30 minutes by going through various job portals such as naukri, LinkedIn, etc to find relevant openings and next 1 hour by collecting the keywords from the job description to curate the resume accordingly and searching for profile of people who can refer me for the role.
π I will advise every aspiring data analyst to have a dedicated timing for searching and applying for the jobs.
πTo get into data analytics, applying for jobs is as important as learning and upskilling.
If you are not applying for the jobs, you are simply delaying your success to get into data analyticsπ¨π»π
I have curated best 80+ top-notch Data Analytics Resources ππ
https://topmate.io/analyst/861634
Hope this helps you π
