Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources
Ir al canal en Telegram
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
Mostrar más
Red:📈 Análisis del canal de Telegram Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources
El canal Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources (@sqlproject) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 39 683 suscriptores, ocupando la posición 4 595 en la categoría Educación y el puesto 9 745 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 39 683 suscriptores.
Según los últimos datos del 28 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 27, y en las últimas 24 horas de 3, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 1.85%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.73% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 736 visualizaciones. En el primer día suele acumular 289 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 2.
- Intereses temáticos: El contenido se centra en temas clave como analytic, dataset, visualization, sql, learning.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“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”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 29 agosto, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.
39 683
Suscriptores
+324 horas
-317 días
+2730 días
Archivo de publicaciones
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 😊
