Data Analytics Projects - SQL, Excel, Tableau, Python & Power BI Interview Resources

Data Analytics Interview Preparation [Questions with Answers] How did you get your job? I was hired after an internship.  To get the internship, I prepared a bunch for general Python questions (LeetCode etc.) and studied the basics of machine learning (several different algorithms, how they work, when they're useful, metrics  to measure their performance, how to train them in practice etc.).  To get the internship I had to pass a technical interview as well as a take-home machine learning (ML) exercise. Then, it was just a question of doing a good job in the internship!  What are your data related responsibilities in your job?  I work on our recommendation system. It’s deep learning based. I work on a lot of features to try and  improve it (reinforcement learning & NLP etc). Since I'm in a start-up, it's also up to our team to put the models we design into production. So, after a phase of research & development and model design, in notebooks, it's time to create a real pipeline, by creating scripts.  This enables us to define, train, replace, compare and check the status of the models in production. It's basically all in Python, using Keras/TensorFlow, Pandas, Scikit-learn and NumPy. We also do a lot of analysis for the business team to help them compute metrics of interest (related to  revenue, acquisition etc.). For that, we use an external utility called Metabase. It is is hooked up to our database where we write SQL queries and visualize the results and create dashboards (using  Tableau/Looker etc).  I would say my role is quite "full-stack" since we are all involved from the phase of R&D to deployment on our cluster.  Was it difficult to get this role? I got hired after an internship. If you come from a scientific background, it's not that hard to transition into data science. All the math is something you will probably have seen already (especially if you're  doing maths or physics). So, with some preparation and coding practice, you can start applying to internships.  It took me maybe a month or two of preparation to get some basic ideas of the typical Python data stack (Pandas, Keras, SciKit-learn etc) before I started to send out CVs. Then, if you get an internship, try your best to do the best you can and then maybe you'll be hired after! I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02 Hope it helps :)

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✅ Step-by-Step Approach to Learn Data Analytics 📈🧠 ➊ Excel Fundamentals: ✔ Master formulas, pivot tables, data validation, charts, and graphs. ➋ SQL Basics: ✔ Learn to query databases, use SELECT, FROM, WHERE, JOIN, GROUP BY, and aggregate functions. ➌ Data Visualization: ✔ Get proficient with tools like Tableau or Power BI to create insightful dashboards. ➍ Statistical Concepts: ✔ Understand descriptive statistics (mean, median, mode), distributions, and hypothesis testing. ➎ Data Cleaning & Preprocessing: ✔ Learn how to handle missing data, outliers, and data inconsistencies. ➏ Exploratory Data Analysis (EDA): ✔ Explore datasets, identify patterns, and formulate hypotheses. ➐ Python for Data Analysis (Optional but Recommended): ✔ Learn Pandas and NumPy for data manipulation and analysis. ➑ Real-World Projects: ✔ Analyze datasets from Kaggle, UCI Machine Learning Repository, or your own collection. ➒ Business Acumen: ✔ Understand key business metrics and how data insights impact business decisions. ➓ Build a Portfolio: ✔ Showcase your projects on GitHub, Tableau Public, or a personal website. Highlight the impact of your analysis. 👍 Tap ❤️ for more!

𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝗰𝗲 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 𝗶𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗯𝘆 𝗜𝗜𝗧 𝗥𝗼𝗼𝗿𝗸𝗲𝗲😍 Deadline: 18th January 2026 Eligibility: Open to everyone Duration: 6 Months Program Mode: Online Taught By: IIT Roorkee Professors Companies majorly hire candidates having Data Science and Artificial Intelligence knowledge these days. 𝗥𝗲𝗴𝗶𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗟𝗶𝗻𝗸👇:  https://pdlink.in/4qHVFkI Only Limited Seats Available!

📊 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲😍 🚀Upgrade your skills with industry-relevant Data Analytics training at ZERO cost  ✅ Beginner-friendly ✅ Certificate on completion ✅ High-demand skill in 2026 𝐋𝐢𝐧𝐤 👇:-  https://pdlink.in/497MMLw 📌 100% FREE – Limited seats available!

SQL Beginner Roadmap 🗄️ 📂 Start Here ∟📂 Install SQL Server / MySQL / SQLite ∟📂 Learn How to Run SQL Queries 📂 SQL Basics ∟📂 What is SQL? ∟📂 Basic SELECT Statements ∟📂 Filtering with WHERE Clause ∟📂 Sorting with ORDER BY ∟📂 Using LIMIT / TOP 📂 Data Manipulation ∟📂 INSERT INTO ∟📂 UPDATE ∟📂 DELETE 📂 Table Management ∟📂 CREATE TABLE ∟📂 ALTER TABLE ∟📂 DROP TABLE 📂 SQL Joins ∟📂 INNER JOIN ∟📂 LEFT JOIN ∟📂 RIGHT JOIN ∟📂 FULL OUTER JOIN 📂 Advanced Queries ∟📂 GROUP BY & HAVING ∟📂 Subqueries ∟📂 Aggregate Functions (COUNT, SUM, AVG) 📂 Practice Projects ∟📌 Build a Simple Library DB ∟📌 Employee Management System ∟📌 Sales Report Analysis 📂 ✅ Move to Next Level (Only After Basics) ∟📂 Learn Indexing & Performance Tuning ∟📂 Stored Procedures & Triggers ∟📂 Database Design & Normalization Credits: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v React "❤️" For More!

𝗛𝗶𝗴𝗵 𝗗𝗲𝗺𝗮𝗻𝗱𝗶𝗻𝗴 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗪𝗶𝘁𝗵 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝗰𝗲😍 Learn from IIT faculty and industry experts. IIT Roorkee DS & AI Program :- https://pdlink.in/4qHVFkI IIT Patna AI & ML :- https://pdlink.in/4pBNxkV IIM Mumbai DM & Analytics :- https://pdlink.in/4jvuHdE IIM Rohtak Product Management:- https://pdlink.in/4aMtk8i IIT Roorkee Agentic Systems:- https://pdlink.in/4aTKgdc Upskill in today’s most in-demand tech domains and boost your career 🚀

📊 Data Analyst Roadmap (2025) Master the Skills That Top Companies Are Hiring For! 📍 1. Learn Excel / Google Sheets Basic formulas & formatting VLOOKUP, Pivot Tables, Charts Data cleaning & conditional formatting 📍 2. Master SQL SELECT, WHERE, ORDER BY JOINs (INNER, LEFT, RIGHT) GROUP BY, HAVING, LIMIT Subqueries, CTEs, Window Functions 📍 3. Learn Data Visualization Tools Power BI / Tableau (choose one) Charts, filters, slicers Dashboards & storytelling 📍 4. Get Comfortable with Statistics Mean, Median, Mode, Std Dev Probability basics A/B Testing, Hypothesis Testing Correlation & Regression 📍 5. Learn Python for Data Analysis (Optional but Powerful) Pandas & NumPy for data handling Seaborn, Matplotlib for visuals Jupyter Notebooks for analysis 📍 6. Data Cleaning & Wrangling Handle missing values Fix data types, remove duplicates Text processing & date formatting 📍 7. Understand Business Metrics KPIs: Revenue, Churn, CAC, LTV Think like a business analyst Deliver actionable insights 📍 8. Communication & Storytelling Present insights with clarity Simplify complex data Speak the language of stakeholders 📍 9. Version Control (Git & GitHub) Track your projects Build a data portfolio Collaborate with the community 📍 10. Interview & Resume Preparation Excel, SQL, case-based questions Mock interviews + real projects Resume with measurable achievements ✨ React ❤️ for more

𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 𝗢𝗻 𝗟𝗮𝘁𝗲𝘀𝘁 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀😍 - Data Science  - AI/ML - Data Analytics - UI/UX - Full-stack Development  Get Job-Ready Guidance in Your Tech Journey 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:-  https://pdlink.in/4sw5Ev8 Date :- 11th January 2026

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✅ Complete Roadmap to Mastering SQL 🚀 🗄️ 📂 1. SQL Fundamentals – What is a database & DBMS – Basic Syntax: SELECT, FROM, WHERE – Data Types: INT, VARCHAR, DATE, etc. – Operators: =, >, <, LIKE, IN – Aliases & Comments 📂 2. Filtering & Sorting – WHERE Clause: Advanced conditions – ORDER BY: Sorting results – LIMIT: Restricting rows – DISTINCT: Unique values 📂 3. Aggregate Functions – COUNT(), SUM(), AVG(), MIN(), MAX() – GROUP BY: Grouping data – HAVING: Filtering grouped data 📂 4. Joins & Relationships – INNER JOIN: Matching rows – LEFT/RIGHT JOIN: All rows from one table – FULL OUTER JOIN: All rows from both tables – Self Join: Joining a table to itself – Subqueries: Queries within queries 📂 5. Advanced Filtering – IN, BETWEEN, LIKE operators – NULL values: IS NULL, IS NOT NULL – EXISTS operator 📂 6. Subqueries & CTEs – Subqueries in SELECT, FROM, WHERE – Common Table Expressions (CTEs): Reusable queries 📂 7. Window Functions – RANK(), DENSE_RANK(), ROW_NUMBER() – LAG(), LEAD() – OVER() clause: Defining the window – Partitioning: PARTITION BY 📂 8. Data Manipulation – INSERT: Adding new data – UPDATE: Modifying existing data – DELETE: Removing data – MERGE: Combining data (upsert) 📂 9. Database Design – Normalization: Reducing redundancy – Primary & Foreign Keys: Relationships – Data types & Constraints – Indexing: Improving query performance 📂 10. Advanced Topics – Stored Procedures: Precompiled SQL – Triggers: Automatic actions – Views: Virtual tables – Performance Tuning: Optimizing queries – Security: User permissions 📂 11. Practice & Projects – Solve coding challenges on platforms like *LeetCode, HackerRank* – Work on real-world projects using datasets from *Kaggle, Data.gov* – Build a portfolio to showcase your SQL skills 💬 Tap ❤️ if you found this helpful!

𝗧𝗼𝗽 𝟱 𝗜𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 𝗦𝗸𝗶𝗹𝗹𝘀 𝘁𝗼 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗶𝗻 𝟮𝟬𝟮𝟲😍 Start learning industry-relevant data skills today at zero cost! 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀:- https://pdlink.in/497MMLw 𝗔𝗜 & 𝗠𝗟 :- https://pdlink.in/4bhetTu 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴:- https://pdlink.in/3LoutZd 𝗖𝘆𝗯𝗲𝗿 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆:- https://pdlink.in/3N9VOyW 𝗢𝘁𝗵𝗲𝗿 𝗧𝗲𝗰𝗵 𝗖𝗼𝘂𝗿𝘀𝗲𝘀:- https://pdlink.in/4qgtrxU 🎓 Enroll Now & Get Certified

Data Analytics Roadmap | |-- Fundamentals |   |-- Mathematics |   |   |-- Descriptive Statistics |   |   |-- Inferential Statistics |   |   |-- Probability Theory |   | |   |-- Programming |   |   |-- Python (Focus on Libraries like Pandas, NumPy) |   |   |-- R (For Statistical Analysis) |   |   |-- SQL (For Data Extraction) | |-- Data Collection and Storage |   |-- Data Sources |   |   |-- APIs |   |   |-- Web Scraping |   |   |-- Databases |   | |   |-- Data Storage |   |   |-- Relational Databases (MySQL, PostgreSQL) |   |   |-- NoSQL Databases (MongoDB, Cassandra) |   |   |-- Data Lakes and Warehousing (Snowflake, Redshift) | |-- Data Cleaning and Preparation |   |-- Handling Missing Data |   |-- Data Transformation |   |-- Data Normalization and Standardization |   |-- Outlier Detection | |-- Exploratory Data Analysis (EDA) |   |-- Data Visualization Tools |   |   |-- Matplotlib |   |   |-- Seaborn |   |   |-- ggplot2 |   | |   |-- Identifying Trends and Patterns |   |-- Correlation Analysis | |-- Advanced Analytics |   |-- Predictive Analytics (Regression, Forecasting) |   |-- Prescriptive Analytics (Optimization Models) |   |-- Segmentation (Clustering Techniques) |   |-- Sentiment Analysis (Text Data) | |-- Data Visualization and Reporting |   |-- Visualization Tools |   |   |-- Power BI |   |   |-- Tableau |   |   |-- Google Data Studio |   | |   |-- Dashboard Design |   |-- Interactive Visualizations |   |-- Storytelling with Data | |-- Business Intelligence (BI) |   |-- KPI Design and Implementation |   |-- Decision-Making Frameworks |   |-- Industry-Specific Use Cases (Finance, Marketing, HR) | |-- Big Data Analytics |   |-- Tools and Frameworks |   |   |-- Hadoop |   |   |-- Apache Spark |   | |   |-- Real-Time Data Processing |   |-- Stream Analytics (Kafka, Flink) | |-- Domain Knowledge |   |-- Industry Applications |   |   |-- E-commerce |   |   |-- Healthcare |   |   |-- Supply Chain | |-- Ethical Data Usage |   |-- Data Privacy Regulations (GDPR, CCPA) |   |-- Bias Mitigation in Analysis |   |-- Transparency in Reporting Free Resources to learn Data Analytics skills👇👇 1. SQL https://mode.com/sql-tutorial/introduction-to-sql https://t.me/sqlspecialist/738 2. Python https://www.learnpython.org/ https://t.me/pythondevelopersindia/873 https://bit.ly/3T7y4ta https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial 3. R https://datacamp.pxf.io/vPyB4L 4. Data Structures https://leetcode.com/study-plan/data-structure/ https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513 5. Data Visualization https://www.freecodecamp.org/learn/data-visualization/ https://t.me/Data_Visual/2 https://www.tableau.com/learn/training/20223 https://www.workout-wednesday.com/power-bi-challenges/ 6. Excel https://excel-practice-online.com/ https://t.me/excel_data https://www.w3schools.com/EXCEL/index.php Join @free4unow_backup for more free courses Like for more ❤️ ENJOY LEARNING 👍👍

𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 𝗕𝘆 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗘𝘅𝗽𝗲𝗿𝘁𝘀 😍 Roadmap to land your dream job in top product-based companies 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝗲𝘀:- - 90-Day Placement Plan - Tech & Non-Tech Career Path - Interview Preparation Tips - Live Q&A 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:-  https://pdlink.in/3Ltb3CE Date & Time:- 06th January 2026 , 7PM

Datasets for Data Science Projects

Kandinsky 5.0 Video Lite and Kandinsky 5.0 Video Pro generative models on the global text-to-video landscape 🔘Pro is currently the #1 open-source model worldwide 🔘Lite (2B parameters) outperforms Sora v1. 🔘Only Google (Veo 3.1, Veo 3), OpenAI (Sora 2), Alibaba (Wan 2.5), and KlingAI (Kling 2.5, 2.6) outperform Pro — these are objectively the strongest video generation models in production today. We are on par with Luma AI (Ray 3) and MiniMax (Hailuo 2.3): the maximum ELO gap is 3 points, with a 95% CI of ±21. Useful links 🔘Full leaderboard: LM Arena 🔘Kandinsky 5.0 details: technical report 🔘Open-source Kandinsky 5.0: GitHub and Hugging Face

🚀 If you’re entering an AI career right now, here’s the truth: It’s not about learning “everything.” It’s about learning the right technical foundations — the ones the industry actually uses. These are the core skills that will matter for the next 5–10 years, no matter how fast AI evolves 👇 1️⃣ Learn how modern LLMs actually work You don’t need to know the math behind transformers, but you must understand: • tokens & embeddings • context windows • attention • prompting vs reasoning • fine-tuning vs RAG • when models hallucinate (and why) If you don’t know how the engine works, you can’t drive it well. 2️⃣ Learn Retrieval — the real backbone of enterprise AI Most AI applications in companies rely on RAG, not fine-tuning. Focus on: • chunking strategies • embedding models • hybrid retrieval (dense + sparse) • vector databases • knowledge graphs • context filtering • evaluation of retrieved docs If you master retrieval, you instantly become valuable. 3️⃣ Learn how to evaluate AI systems, not just build them Engineers build models. Professionals who can evaluate them are the ones who get promoted. Learn to measure: • grounding accuracy • relevance • completeness • tool-use correctness • consistency across runs • latency • safety This is where the real skill gap is. 4️⃣ Learn prompting as an engineering discipline Not “try random prompts.” But systematic methods like: • template prompts • tool-calling prompts • guardrail prompts • chain-of-thought • reflection prompts • constraint-based prompting Prompting is becoming the new API design. 5️⃣ Learn how to build agentic workflows AI is moving from answers → decisions → actions. You should know: • planner → executor → verifier agent structure • tool routing • action space design • human-in-the-loop workflows • permissioning • error recovery loops This is what separates beginners from real AI engineers. 6️⃣ Learn Python + APIs deeply You don’t need to be a software engineer, but you must be comfortable with: • Python basics • API calls • JSON • LangChain / LlamaIndex / DSPy • building small scripts • reading logs • debugging AI pipelines This is the “plumbing” behind AI systems. 7️⃣ Build real projects, not toy demos Instead of “build a chatbot,” build: • a support email classifier • a RAG system on company policies • a customer insights extractor • an automatic meeting summarizer • a multimodal analyzer (text + image) • an internal tool-calling agent Projects that solve real problems get you hired. 8️⃣ Learn one domain deeply AI generalists struggle. AI + domain experts win. Choose one: • finance • healthcare • retail • manufacturing • real estate • cybersecurity • operations • supply chain • HR tech AI skill + domain depth = career acceleration. If you’re entering AI today: Focus on retrieval, reasoning, evaluation, agents, and real projects. These are the skills companies are desperate for.

✅ Top Projects Every Data Analyst Should Build 🧪📊 1️⃣ Sales Dashboard Dive into revenue trends, product performance, and regional sales breakdowns. Tools: Excel, Power BI, SQL 2️⃣ Customer Churn Analysis Spot patterns in customer drop-off and predict who might leave next. Skills: Pandas, Logistic Regression, Data Cleaning 3️⃣ Marketing Campaign Report Measure ad ROI, CTRs, and conversion funnels for better targeting. Tools: Google Sheets, Tableau, SQL 4️⃣ HR Analytics Track turnover, hiring efficiency, and team performance metrics. Tools: Python, Excel, Power BI 5️⃣ E-commerce Order Analysis Analyze order flows, delivery delays, and return rates. Skills: SQL joins, Data Wrangling 6️⃣ Survey Data Analysis Process feedback, visualize sentiment, and pull key insights. Tools: Python (Pandas, Seaborn), Excel 7️⃣ Financial Performance Tracker Monitor monthly P&L, expense trends, and profitability. Tools: Excel dashboards or Tableau 8️⃣ COVID-19 Data Tracker Explore time series, regional impacts, and recovery patterns (timeless for public health analysis). Skills: APIs, Pandas, Plotly 9️⃣ Movie/Book Rating Analysis Uncover genre trends, rating correlations, and recommendation basics. Tools: Python, SQL, Matplotlib 🔟 Real-time Data Dashboard Build live feeds for stocks, weather, or crypto with interactive updates. Tools: Python, Streamlit, APIs These projects are straight from 2025 guides like DataCamp and GeeksforGeeks—start with public datasets to build your portfolio and land that analyst gig! 💬 Tap ❤️ for more! Which one are you tackling first? 😊

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 & 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀😍 Kickstart Your Data Science Career This Masterclass will help you build a strong foundation in Data Science Eligibility :- Students ,Freshers & Working Professionals  𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:-  https://pdlink.in/3XDI0ie Date & Time:- 5th Dec 2025 ,7PM