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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

前往频道在 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

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📈 Telegram 频道 Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources 的分析概览

频道 Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources (@sqlproject) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 39 505 名订阅者,在 教育 类别中位列第 4 747,并在 印度 地区排名第 10 383

📊 受众指标与增长动态

невідомо 创建以来,项目保持高速增长,吸引了 39 505 名订阅者。

根据 11 六月, 2026 的最新数据,频道保持稳定运转。过去 30 天订阅人数变化为 205,过去 24 小时变化为 11,整体触达仍然可观。

  • 认证状态: 未认证
  • 互动率 (ER): 平均受众互动率为 2.87%。内容发布后 24 小时内通常能获得 0.98% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 1 133 次浏览,首日通常累积 388 次浏览。
  • 互动与反馈: 受众积极参与,单帖平均反应数为 3
  • 主题关注点: 内容集中在 analytic, dataset, visualization, sql, learning 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台:
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

凭借高频更新(最新数据采集于 12 六月, 2026),频道始终保持新鲜度与高覆盖。分析显示受众积极互动,使其成为 教育 类别中的关键影响点。

39 505
订阅者
+1124 小时
+367
+20530
帖子存档
🚨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

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

Exploratory Data Analysis .pdf5.70 KB