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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 39 505 subscribers, ranking 4 747 in the Education category and 10 383 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 39 505 subscribers.

According to the latest data from 11 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 205 over the last 30 days and by 11 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.87%. Within the first 24 hours after publication, content typically collects 0.98% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 1 133 views. Within the first day, a publication typically gains 388 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 12 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.

39 505
Subscribers
+1124 hours
+367 days
+20530 days
Posts Archive
๐Ÿšจ30 FREE Dataset Sources for Data Science Projects๐Ÿ”ฅ 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 FBIโ€™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 โ˜บ๏ธ

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 ๐Ÿ“ŒCustomer Churn Analysis ๐Ÿ”— https://www.kaggle.com/code/fabiendaniel/customer-segmentation/input ๐Ÿ“ŒCredit Card Fraud ๐Ÿ”— https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud ๐Ÿ“ŒMovie Sales Analysis ๐Ÿ”—https://www.kaggle.com/datasets/PromptCloudHQ/imdb-data ๐Ÿ“ŒAirline Sector ๐Ÿ”—https://www.kaggle.com/datasets/yuanyuwendymu/airline- ๐Ÿ“ŒFinancial Data Analysis ๐Ÿ”—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 ๐Ÿ‘‡๐Ÿ‘‡ https://topmate.io/analyst/981703 Please use the above link to avail them!๐Ÿ‘† 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!๐Ÿ‘โœŒ๏ธ

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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 ๐Ÿ‘‡๐Ÿ‘‡ https://topmate.io/analyst/861634 Hope this helps you

๐Ÿญ๐Ÿฌ ๐—–๐—ต๐—ฎ๐˜๐—š๐—ฃ๐—ง ๐Ÿฐ๐—ผ ๐—ฃ๐—ฟ๐—ผ๐—บ๐—ฝ๐˜๐˜€ That Will Make You a Superhuman ๐Ÿ‘‡๐Ÿ‘‡ AI Prompts Master

Exploratory Data Analysis .pdf5.70 KB