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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 39 681 名订阅者,在 教育 类别中位列第 4 600,并在 印度 地区排名第 9 817

📊 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 39 681 名订阅者。

根据 27 八月, 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 37,过去 24 小时变化为 -1,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 1.80%。内容发布后 24 小时内通常能获得 0.74% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 716 次浏览,首日通常累积 292 次浏览。
  • 互动与反馈: 受众积极参与,单帖平均反应数为 2
  • 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 28 八月, 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。

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39 681
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-124 小时
-407
+3730
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𝗛𝗶𝗴𝗵𝗹𝘆 𝗗𝗲𝗺𝗮𝗻𝗱𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 - 𝗘𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘😍 Industry-ap
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Data Analytics project ideas to build your portfolio in 2025: 1. Sales Data Analysis Dashboard Analyze sales trends, seasonal patterns, and product performance. Use Power BI, Tableau, or Python (Dash/Plotly) for visualization. 2. Customer Segmentation Use clustering (K-means, hierarchical) on customer data to identify groups. Provide actionable marketing insights. 3. Social Media Sentiment Analysis Analyze tweets or reviews using NLP to gauge public sentiment. Visualize positive, negative, and neutral trends over time. 4. Churn Prediction Model Analyze customer data to predict who might leave a service. Use logistic regression, decision trees, or random forest. 5. Financial Data Analysis Study stock prices, moving averages, and volatility. Create an interactive dashboard with key metrics. 6. Healthcare Analytics Analyze patient data for disease trends or hospital resource usage. Use visualization to highlight key findings. 7. Website Traffic Analysis Use Google Analytics data to identify user behavior patterns. Suggest improvements for user engagement and conversion. 8. Employee Attrition Analysis Analyze HR data to find factors leading to employee turnover. Use statistical tests and visualization. React ❤️ for more

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Learn SQL from basic to advanced level in 30 days Week 1: SQL Basics Day 1: Introduction to SQL and Relational Databases Overview of SQL Syntax Setting up a Database (MySQL, PostgreSQL, or SQL Server) Day 2: Data Types (Numeric, String, Date, etc.) Writing Basic SQL Queries: SELECT, FROM Day 3: WHERE Clause for Filtering Data Using Logical Operators: AND, OR, NOT Day 4: Sorting Data: ORDER BY Limiting Results: LIMIT and OFFSET Understanding DISTINCT Day 5: Aggregate Functions: COUNT, SUM, AVG, MIN, MAX Day 6: Grouping Data: GROUP BY and HAVING Combining Filters with Aggregations Day 7: Review Week 1 Topics with Hands-On Practice Solve SQL Exercises on platforms like HackerRank, LeetCode, or W3Schools Week 2: Intermediate SQL Day 8: SQL JOINS: INNER JOIN, LEFT JOIN Day 9: SQL JOINS Continued: RIGHT JOIN, FULL OUTER JOIN, SELF JOIN Day 10: Working with NULL Values Using Conditional Logic with CASE Statements Day 11: Subqueries: Simple Subqueries (Single-row and Multi-row) Correlated Subqueries Day 12: String Functions: CONCAT, SUBSTRING, LENGTH, REPLACE Day 13: Date and Time Functions: NOW, CURDATE, DATEDIFF, DATEADD Day 14: Combining Results: UNION, UNION ALL, INTERSECT, EXCEPT Review Week 2 Topics and Practice Week 3: Advanced SQL Day 15: Common Table Expressions (CTEs) WITH Clauses and Recursive Queries Day 16: Window Functions: ROW_NUMBER, RANK, DENSE_RANK, NTILE Day 17: More Window Functions: LEAD, LAG, FIRST_VALUE, LAST_VALUE Day 18: Creating and Managing Views Temporary Tables and Table Variables Day 19: Transactions and ACID Properties Working with Indexes for Query Optimization Day 20: Error Handling in SQL Writing Dynamic SQL Queries Day 21: Review Week 3 Topics with Complex Query Practice Solve Intermediate to Advanced SQL Challenges Week 4: Database Management and Advanced Applications Day 22: Database Design and Normalization: 1NF, 2NF, 3NF Day 23: Constraints in SQL: PRIMARY KEY, FOREIGN KEY, UNIQUE, CHECK, DEFAULT Day 24: Creating and Managing Indexes Understanding Query Execution Plans Day 25: Backup and Restore Strategies in SQL Role-Based Permissions Day 26: Pivoting and Unpivoting Data Working with JSON and XML in SQL Day 27: Writing Stored Procedures and Functions Automating Processes with Triggers Day 28: Integrating SQL with Other Tools (e.g., Python, Power BI, Tableau) SQL in Big Data: Introduction to NoSQL Day 29: Query Performance Tuning: Tips and Tricks to Optimize SQL Queries Day 30: Final Review of All Topics Attempt SQL Projects or Case Studies (e.g., analyzing sales data, building a reporting dashboard) Since SQL is one of the most essential skill for data analysts, I have decided to teach each topic daily in this channel for free. Like this post if you want me to continue this SQL series 👍♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

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𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 & 𝗟𝗲𝗮𝗱𝗶𝗻𝗴 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗢𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍 Harward :- https://pdlink.in/4kmYOn1 MIT :- https://pdlink.in/45cvR95 HP :- https://pdlink.in/45ci02k Google :- https://pdlink.in/3YsujTV Microsoft :- https://pdlink.in/441GCKF Standford :- https://pdlink.in/3ThPwNw IIM :- https://pdlink.in/4nfXDrV Enroll for FREE & Get Certified 🎓

𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗔𝗽𝗽𝗿𝗼𝘃𝗲𝗱 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 😍 Whether you’re interested in AI, Data Analytics, C
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𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗙𝗥𝗘𝗘 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 ,𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 ,𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 & 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗚𝘂𝗶𝗱𝗲😍 Roadmap:- https://pdlink.in/41c1Kei Certifications:- https://pdlink.in/3Fq7E4p Projects:- https://pdlink.in/3ZkXetO Interview Q/A :- https://pdlink.in/4jLOJ2a Enroll For FREE & Become a Certified Data Analyst In 2025🎓

Don't forget to check these 10 SQL projects with corresponding datasets that you could use to practice your SQL skills: 1. Analysis of Sales Data: (https://www.kaggle.com/kyanyoga/sample-sales-data) 2. HR Analytics: (https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset) 3. Social Media Analytics: (https://www.kaggle.com/datasets/ramjasmaurya/top-1000-social-media-channels) 4. Financial Data Analysis: (https://www.kaggle.com/datasets/nitindatta/finance-data) 5. Healthcare Data Analysis: (https://www.kaggle.com/cdc/mortality) 6. Customer Relationship Management: (https://www.kaggle.com/pankajjsh06/ibm-watson-marketing-customer-value-data) 7. Web Analytics: (https://www.kaggle.com/zynicide/wine-reviews) 8. E-commerce Analysis: (https://www.kaggle.com/olistbr/brazilian-ecommerce) 9. Supply Chain Management: (https://www.kaggle.com/datasets/harshsingh2209/supply-chain-analysis) 10. Inventory Management: (https://www.kaggle.com/datasets?search=inventory+management) Share this channel with your friends 🤝🤩 Join for more -> https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z ENJOY LEARNING 👍👍

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Top 5 data science projects for freshers 1. Predictive Analytics on a Dataset:    - Use a dataset to predict future trends or outcomes using machine learning algorithms. This could involve predicting sales, stock prices, or any other relevant domain. 2. Customer Segmentation:    - Analyze and segment customers based on their behavior, preferences, or demographics. This project could provide insights for targeted marketing strategies. 3. Sentiment Analysis on Social Media Data:    - Analyze sentiment in social media data to understand public opinion on a particular topic. This project helps in mastering natural language processing (NLP) techniques. 4. Recommendation System:    - Build a recommendation system, perhaps for movies, music, or products, using collaborative filtering or content-based filtering methods. 5. Fraud Detection:    - Develop a fraud detection system using machine learning algorithms to identify anomalous patterns in financial transactions or any domain where fraud detection is crucial. Free Datsets -> https://t.me/DataPortfolio/2?single These projects showcase practical application of data science skills and can be highlighted on a resume for entry-level positions. Join @pythonspecialist for more data science projects

𝗖𝗜𝗦𝗖𝗢 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍 - Data Analytics - Data Science - Python - Javascript - Cyber
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🔰 Data Science Roadmap for Beginners 2025 ├── 📘 What is Data Science? ├── 🧠 Data Science vs Data Analytics vs Machine Learning ├── 🛠 Tools of the Trade (Python, R, Excel, SQL) ├── 🐍 Python for Data Science (NumPy, Pandas, Matplotlib) ├── 🔢 Statistics & Probability Basics ├── 📊 Data Visualization (Matplotlib, Seaborn, Plotly) ├── 🧼 Data Cleaning & Preprocessing ├── 🧮 Exploratory Data Analysis (EDA) ├── 🧠 Introduction to Machine Learning ├── 📦 Supervised vs Unsupervised Learning ├── 🤖 Popular ML Algorithms (Linear Reg, KNN, Decision Trees) ├── 🧪 Model Evaluation (Accuracy, Precision, Recall, F1 Score) ├── 🧰 Model Tuning (Cross Validation, Grid Search) ├── ⚙️ Feature Engineering ├── 🏗 Real-world Projects (Kaggle, UCI Datasets) ├── 📈 Basic Deployment (Streamlit, Flask, Heroku) ├── 🔁 Continuous Learning: Blogs, Research Papers, Competitions Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D Like for more ❤️

𝟲 𝗙𝗿𝗲𝗲 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗣𝗹𝗮𝘆𝗹𝗶𝘀𝘁𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗤𝗟 𝗳𝗿𝗼𝗺 𝗦𝗰𝗿𝗮𝘁𝗰𝗵 𝗶𝗻 𝟮𝟬𝟮𝟱😍 Want to break
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7 Baby Steps to Become a Data Analyst 👇👇 1. Understand the Role of a Data Analyst: Learn what a data analyst does, including collecting, cleaning, analyzing, and interpreting data to support decision-making. Familiarize yourself with key terms like KPIs, dashboards, and business intelligence. Research industries where data analysts work, such as finance, marketing, healthcare, and e-commerce. 2. Learn the Essential Tools: Excel: Start with basics like formulas, functions, and pivot tables, then advance to using Power Query and macros. SQL: Learn to write queries for retrieving, filtering, and aggregating data from databases. Data Visualization Tools: Master tools like Power BI or Tableau to create dashboards and reports. 3. Develop Analytical Thinking: Practice identifying trends, patterns, and outliers in datasets. Learn to ask the right questions about what the data reveals and how it can guide decision-making. Strengthen problem-solving skills through real-world case studies or challenges. 4. Master a Programming Language (Python or R): Learn Python libraries like pandas, NumPy, and matplotlib for data manipulation and visualization. Alternatively, learn R for statistical analysis and its packages like ggplot2 and dplyr. Work on projects like cleaning messy datasets or creating automated analysis scripts. 5. Work with Real-World Data: Explore open datasets from platforms like Kaggle or Google Dataset Search. Practice analyzing datasets related to your area of interest (e.g., sales, customer feedback, or healthcare). Create sample reports or dashboards to showcase insights. 6. Build a Portfolio: Document your projects in a way that demonstrates your skills. Include: Data cleaning and transformation examples. Visualization dashboards using Power BI, Tableau, or Excel. Analysis reports with actionable insights. Use GitHub or Tableau Public to showcase your work. 7. Engage with the Data Analytics Community: Join forums like Kaggle, Reddit’s r/dataanalysis, or LinkedIn groups. Participate in challenges to solve real-world problems, such as Kaggle competitions. Additional Tips: Gain domain knowledge relevant to your target industry (e.g., marketing analytics or financial analysis). Focus on communication skills to present insights effectively to non-technical stakeholders. Continuously learn and upskill as new tools and techniques emerge in the data analytics field. Join our WhatsApp channel 👇 https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02 Like this post for more content like this 👍♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

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Don't forget to check these 10 SQL projects with corresponding datasets that you could use to practice your SQL skills: 1. Analysis of Sales Data: (https://www.kaggle.com/kyanyoga/sample-sales-data) 2. HR Analytics: (https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset) 3. Social Media Analytics: (https://www.kaggle.com/datasets/ramjasmaurya/top-1000-social-media-channels) 4. Financial Data Analysis: (https://www.kaggle.com/datasets/nitindatta/finance-data) 5. Healthcare Data Analysis: (https://www.kaggle.com/cdc/mortality) 6. Customer Relationship Management: (https://www.kaggle.com/pankajjsh06/ibm-watson-marketing-customer-value-data) 7. Web Analytics: (https://www.kaggle.com/zynicide/wine-reviews) 8. E-commerce Analysis: (https://www.kaggle.com/olistbr/brazilian-ecommerce) 9. Supply Chain Management: (https://www.kaggle.com/datasets/harshsingh2209/supply-chain-analysis) 10. Inventory Management: (https://www.kaggle.com/datasets?search=inventory+management) Share this channel with your friends 🤝🤩 Join for more -> https://whatsapp.com/channel/0029VaxbzNFCxoAmYgiGTL3Z ENJOY LEARNING 👍👍

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