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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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📈 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 39 505 suscriptores, ocupando la posición 4 747 en la categoría Educación y el puesto 10 383 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 39 505 suscriptores.

Según los últimos datos del 11 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 205, y en las últimas 24 horas de 11, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.87%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.98% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 1 133 visualizaciones. En el primer día suele acumular 388 visualizaciones.
  • Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 3.
  • 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 12 junio, 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.

39 505
Suscriptores
+1124 horas
+367 días
+20530 días
Archivo de publicaciones
🚨30 FREE Dataset Sources for Data Science Projects🔥 Data Simplifier: https://datasimplifier.com/best-data-analyst-projects-for-freshers/ US Government Dataset: https://www.data.gov/ Open Government Data (OGD) Platform India: https://data.gov.in/ The World Bank Open Data: https://data.worldbank.org/ Data World: https://data.world/ BFI - Industry Data and Insights: https://www.bfi.org.uk/data-statistics The Humanitarian Data Exchange (HDX): https://data.humdata.org/ Data at World Health Organization (WHO): https://www.who.int/data FBI’s Crime Data Explorer: https://crime-data-explorer.fr.cloud.gov/ AWS Open Data Registry: https://registry.opendata.aws/ FiveThirtyEight: https://data.fivethirtyeight.com/ IMDb Datasets: https://www.imdb.com/interfaces/ Kaggle: https://www.kaggle.com/datasets UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/index.php Google Dataset Search: https://datasetsearch.research.google.com/ Nasdaq Data Link: https://data.nasdaq.com/ Recommender Systems and Personalization Datasets: https://cseweb.ucsd.edu/~jmcauley/datasets.html Reddit - Datasets: https://www.reddit.com/r/datasets/ Open Data Network by Socrata: https://www.opendatanetwork.com/ Climate Data Online by NOAA: https://www.ncdc.noaa.gov/cdo-web/ Azure Open Datasets: https://azure.microsoft.com/en-us/services/open-datasets/ IEEE Data Port: https://ieee-dataport.org/ Wikipedia: Database: https://dumps.wikimedia.org/ BuzzFeed News: https://github.com/BuzzFeedNews/everything Academic Torrents: https://academictorrents.com/ Yelp Open Dataset: https://www.yelp.com/dataset The NLP Index by Quantum Stat: https://index.quantumstat.com/ Computer Vision Online: http://www.computervisiononline.com/dataset Visual Data Discovery: https://www.visualdata.io/ Roboflow Public Datasets: https://public.roboflow.com/ Computer Vision Group, TUM: https://vision.in.tum.de/data/datasets

Happy to announce that we are now the community of 30 subscribers on Youtube https://youtube.com/@dataanalyticsrock?sub_confirmation=1 A long way to go ☺️

You need a portfolio to get a data analytics job. You need projects for your portfolio. Here's how many to do... A total of 5! Here is the breakdown:- - 1 Project using just Excel - 1 Project using just SQL - 3 Dashboards (Power BI/Tableau)

MUST ADD these 5 POWER Bl projects to your resume to get hired Here are 5 mini projects that not only help you to gain experience but also it will help you to build your resume stronger 📌Customer Churn Analysis 🔗 https://www.kaggle.com/code/fabiendaniel/customer-segmentation/input 📌Credit Card Fraud 🔗 https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud 📌Movie Sales Analysis 🔗https://www.kaggle.com/datasets/PromptCloudHQ/imdb-data 📌Airline Sector 🔗https://www.kaggle.com/datasets/yuanyuwendymu/airline- 📌Financial Data Analysis 🔗https://www.kaggle.com/datasets/qks1%7Cver/financial-data- Simple guide 1. Data Utilization: - Initiate the process by using the provided datasets for a comprehensive analysis. 2. Domain Research: - Conduct thorough research within the domain to identify crucial metrics and KPIs for analysis. 3. Dashboard Blueprint: - Outline the structure and aesthetics of your dashboard, drawing inspiration from existing online dashboards for enhanced design and functionality. 4. Data Handling: - Import data meticulously, ensuring accuracy. Proceed with cleaning, modeling, and the creation of essential measures and calculations. 5. Question Formulation: - Brainstorm a list of insightful questions your dashboard aims to answer, covering trends, comparisons, aggregations, and correlations within the data. 6. Platform Integration: - Utilize Novypro.com as the hosting platform for your dashboard, ensuring seamless integration and accessibility. 7. LinkedIn Visibility: - Share your dashboard on LinkedIn with a concise post providing context. Include a link to your Novypro-hosted dashboard to foster engagement and professional connections. Join for more: https://t.me/DataPortfolio Hope this helps you :)

I have created this 100-Day Roadmap & Resources for Data Analytics today 👇👇 https://topmate.io/analyst/981703 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 job search journey... All the best!👍✌️

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Data Science Crash Course for Beginners with Python.pdf12.15 MB

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Fraud reduction using machine learning

Selected Scenario Question: Scenario: You are working as a data analyst for a retail company. The company wants to understand the sales performance across different regions and product categories. You have access to a SQL database that stores order details and a Power BI setup for reporting. Your task is to create a comprehensive report that shows: Total sales by product category. Total sales by region. Total number of orders placed by each customer. Identify the top 5 products contributing to sales in each region. Comprehensive Answer: Step 1: SQL Queries to Retrieve Data Total Sales by Product Category: SELECT ProductCategory, SUM(SalesAmount) AS TotalSales FROM Orders GROUP BY ProductCategory; Total Sales by Region: SELECT Region, SUM(SalesAmount) AS TotalSales FROM Orders GROUP BY Region; Total Number of Orders Placed by Each Customer: SELECT CustomerID, COUNT(*) AS TotalOrders FROM Orders GROUP BY CustomerID; Top 5 Products Contributing to Sales in Each Region: SELECT Region, ProductID, ProductName, SUM(SalesAmount) AS TotalSales FROM Orders GROUP BY Region, ProductID, ProductName ORDER BY Region, TotalSales DESC LIMIT 5; Step 2: Import Data into Power BI Load Data: Open Power BI Desktop. Use the "Get Data" feature to connect to your SQL database. Import the result sets from the SQL queries into Power BI. Create Relationships (if necessary): Ensure that the data tables are properly related. For example, link the Orders table to Customers, Products, and Regions tables if they exist separately. Step 3: Create Visualizations Total Sales by Product Category: Create a bar chart. Drag ProductCategory to the Axis. Drag TotalSales to the Values. Total Sales by Region: Create a pie chart. Drag Region to the Legend. Drag TotalSales to the Values. Total Number of Orders Placed by Each Customer: Create a table. Drag CustomerID to the Rows. Drag TotalOrders to the Values. Top 5 Products Contributing to Sales in Each Region: Create a clustered bar chart. Drag Region to the Axis. Drag ProductName to the Legend. Drag TotalSales to the Values. Apply a Top N filter to show only the top 5 products in each region. Step 4: Optimize Performance Data Model Optimization: Reduce the number of columns and rows by filtering unnecessary data. Use summarized tables to pre-aggregate data. DAX Optimization: Simplify calculations by using measures and avoiding complex DAX queries. Visualization Optimization: Limit the number of visuals on each report page. Avoid using too many slicers or custom visuals that can slow down the performance. Scheduled Refresh: Set up scheduled refreshes to ensure the data is up-to-date without manual intervention. By following these steps, you will create a comprehensive and optimized Power BI report that provides valuable insights into sales performance across different regions and product categories for the retail company. I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Hope this helps you

𝟭𝟬 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝟰𝗼 𝗣𝗿𝗼𝗺𝗽𝘁𝘀 That Will Make You a Superhuman 👇👇 AI Prompts Master

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