Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources
Kanalga Telegram’da o‘tish
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
Ko'proq ko'rsatish
Tarmoq:📈 Telegram kanali Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources analitikasi
Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources (@sqlproject) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 39 683 obunachidan iborat bo'lib, Taʼlim toifasida 4 595-o'rinni va Hindiston mintaqasida 9 745-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 39 683 obunachiga ega bo‘ldi.
28 Avgust, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 27 ga, so‘nggi 24 soatda esa 3 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 1.85% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 0.73% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 736 marta ko‘riladi; birinchi sutkada odatda 289 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 2 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent analytic, dataset, visualization, sql, learning kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“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”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 29 Avgust, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
39 683
Obunachilar
+324 soatlar
-317 kunlar
+2730 kunlar
Postlar arxiv
Join fast if interested
Free Stock Marketing Resources
👇👇
https://chat.whatsapp.com/FKndaTDDPhZ7DPuBiqAdBI
AI/ML (Daily Schedule) 👨🏻💻
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 👍👍
Enjoy our content? Advertise on this channel and reach a highly engaged audience! 👉🏻
It's easy with Telega.io. As the leading platform for native ads and integrations on Telegram, it provides user-friendly and efficient tools for quick and automated ad launches.
⚡️ Place your ad here in three simple steps:
1 Sign up
2 Top up the balance in a convenient way
3 Create your advertising post
If your ad aligns with our content, we’ll gladly publish it.
Start your promotion journey now!
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
👇👇
https://chat.whatsapp.com/GMnE6elUlK9B7PfXUuqiHC
A_Practical_Introduction_to_Python_Programming_Heinold.pdf1.98 MB
“What dataset should I use”
Think business scenarios such as:
• Product performance
• Marketing campaigns
• Customer behaviour
• Customer retention
• Sales performance
For datasets, you can scroll this channel & find the one which suits your interest
SQL Retail sales Project.pdf1.25 KB
🔟 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 👍👍
5⃣ 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 👆👆
It has 24+ SQL Questions to Practice,
With Dataset + Code..❤🔥
You can also Download the Dataset from the Link in Page 1...👆⚡
Also Share this 💗
SQL_Practice.pdf3.89 MB
How to Crack Data Engineering Interviews with these 5 Easy Steps 👇👇
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
👇👇
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 Analyst👨💻
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.
📍 I will advise every aspiring data analyst to have a dedicated timing for searching and applying for the jobs.
📍To 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 analytics👨💻📊
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://topmate.io/analyst/861634
Hope this helps you 😊
