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

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🚀 How to Land a Data Analyst Job Without Experience? Many people asked me this question, so I thought to answer it here to help everyone. Here is the step-by-step approach i would recommend: ✅ Step 1: Master the Essential Skills You need to build a strong foundation in: 🔹 SQL – Learn how to extract and manipulate data 🔹 Excel – Master formulas, Pivot Tables, and dashboards 🔹 Python – Focus on Pandas, NumPy, and Matplotlib for data analysis 🔹 Power BI/Tableau – Learn to create interactive dashboards 🔹 Statistics & Business Acumen – Understand data trends and insights Where to learn? 📌 Google Data Analytics Course 📌 SQL – Mode Analytics (Free) 📌 Python – Kaggle or DataCamp ✅ Step 2: Work on Real-World Projects Employers care more about what you can do rather than just your degree. Build 3-4 projects to showcase your skills. 🔹 Project Ideas: ✅ Analyze sales data to find profitable products ✅ Clean messy datasets using SQL or Python ✅ Build an interactive Power BI dashboard ✅ Predict customer churn using machine learning (optional) Use Kaggle, Data.gov, or Google Dataset Search to find free datasets! ✅ Step 3: Build an Impressive Portfolio Once you have projects, showcase them! Create: 📌 A GitHub repository to store your SQL/Python code 📌 A Tableau or Power BI Public Profile for dashboards 📌 A Medium or LinkedIn post explaining your projects A strong portfolio = More job opportunities! 💡 ✅ Step 4: Get Hands-On Experience If you don’t have experience, create your own! 📌 Do freelance projects on Upwork/Fiverr 📌 Join an internship or volunteer for NGOs 📌 Participate in Kaggle competitions 📌 Contribute to open-source projects Real-world practice > Theoretical knowledge! ✅ Step 5: Optimize Your Resume & LinkedIn Profile Your resume should highlight: ✔️ Skills (SQL, Python, Power BI, etc.) ✔️ Projects (Brief descriptions with links) ✔️ Certifications (Google Data Analytics, Coursera, etc.) Bonus Tip: 🔹 Write "Data Analyst in Training" on LinkedIn 🔹 Start posting insights from your learning journey 🔹 Engage with recruiters & join LinkedIn groups ✅ Step 6: Start Applying for Jobs Don’t wait for the perfect job—start applying! 📌 Apply on LinkedIn, Indeed, and company websites 📌 Network with professionals in the industry 📌 Be ready for SQL & Excel assessments Pro Tip: Even if you don’t meet 100% of the job requirements, apply anyway! Many companies are open to hiring self-taught analysts. You don’t need a fancy degree to become a Data Analyst. Skills + Projects + Networking = Your job offer! 🔥 Your Challenge: Start your first project today and track your progress! Share with credits: https://t.me/sqlspecialist Hope it helps :)

𝗪𝗼𝗿𝗸 𝗙𝗿𝗼𝗺 𝗛𝗼𝗺𝗲 𝗝𝗼𝗯 𝗢𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝘆 𝘄𝗶𝘁𝗵 𝗮𝗻 𝗘-𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲 𝗕𝗿𝗮𝗻𝗱!😍  Role: SEPO - Transaction Risk Investigator  Salary: ₹3.2–₹4 LPA Eligibility: All graduates are welcome  Location:- Work From Home 𝗔𝗽𝗽𝗹𝘆 𝗟𝗶𝗻𝗸👇:- https://pdlink.in/4mGpCAn Apply before the link expires💫 ✅ Take a quick online assessment to get started!

Sharing 20+ Diverse Datasets📊 for Data Science and Analytics practice! 1. How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview 2. Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand 3. Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction 4. Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data 5. Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction 6. Iris Dataset: https://archive.ics.uci.edu/ml/datasets/iris 7. Titanic Dataset: https://www.kaggle.com/c/titanic 8. Wine Quality Dataset: https://archive.ics.uci.edu/ml/datasets/Wine+Quality 9. Heart Disease Dataset: https://archive.ics.uci.edu/ml/datasets/Heart+Disease 10. Bengaluru House Price Dataset: https://www.kaggle.com/amitabhajoy/bengaluru-house-price-data 11. Breast Cancer Dataset: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29 12. Credit Card Fraud Detection: https://www.kaggle.com/mlg-ulb/creditcardfraud 13. Netflix Movies and TV Shows: https://www.kaggle.com/shivamb/netflix-shows 14. Trending YouTube Video Statistics: https://www.kaggle.com/datasnaek/youtube-new 15. Walmart Store Sales Forecasting: https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting 16. FIFA 19 Complete Player Dataset: https://www.kaggle.com/karangadiya/fifa19 17. World Happiness Report: https://www.kaggle.com/unsdsn/world-happiness 18. TMDB 5000 Movie Dataset: https://www.kaggle.com/tmdb/tmdb-movie-metadata 19. Students Performance in Exams: https://www.kaggle.com/spscientist/students-performance-in-exams 20. Twitter Sentiment Analysis Dataset: https://www.kaggle.com/kazanova/sentiment140 21. Digit Recognizer: https://www.kaggle.com/c/digit-recognizer 💻🔍 Don't miss out on these valuable resources for advancing your data science journey!

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𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗛𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀😍 𝗔𝗽𝗽𝗹𝘆 𝗟𝗶𝗻𝗸𝘀:-👇 S&P Global :- https://pdlink.in/3ZddwVz IBM :- https://pdlink.in/4kDmMKE TVS Credit :- https://pdlink.in/4mI0JVc Sutherland :- https://pdlink.in/4mGYBgg Other Jobs :- https://pdlink.in/44qEIDu Apply before the link expires 💫

Here is a list of 50 data science interview questions that can help you prepare for a data science job interview. These questions cover a wide range of topics and levels of difficulty, so be sure to review them thoroughly and practice your answers. Mathematics and Statistics: 1. What is the Central Limit Theorem, and why is it important in statistics? 2. Explain the difference between population and sample. 3. What is probability and how is it calculated? 4. What are the measures of central tendency, and when would you use each one? 5. Define variance and standard deviation. 6. What is the significance of hypothesis testing in data science? 7. Explain the p-value and its significance in hypothesis testing. 8. What is a normal distribution, and why is it important in statistics? 9. Describe the differences between a Z-score and a T-score. 10. What is correlation, and how is it measured? 11. What is the difference between covariance and correlation? 12. What is the law of large numbers? Machine Learning: 13. What is machine learning, and how is it different from traditional programming? 14. Explain the bias-variance trade-off. 15. What are the different types of machine learning algorithms? 16. What is overfitting, and how can you prevent it? 17. Describe the k-fold cross-validation technique. 18. What is regularization, and why is it important in machine learning? 19. Explain the concept of feature engineering. 20. What is gradient descent, and how does it work in machine learning? 21. What is a decision tree, and how does it work? 22. What are ensemble methods in machine learning, and provide examples. 23. Explain the difference between supervised and unsupervised learning. 24. What is deep learning, and how does it differ from traditional neural networks? 25. What is a convolutional neural network (CNN), and where is it commonly used? 26. What is a recurrent neural network (RNN), and where is it commonly used? 27. What is the vanishing gradient problem in deep learning? 28. Describe the concept of transfer learning in deep learning. Data Preprocessing: 29. What is data preprocessing, and why is it important in data science? 30. Explain missing data imputation techniques. 31. What is one-hot encoding, and when is it used? 32. How do you handle categorical data in machine learning? 33. Describe the process of data normalization and standardization. 34. What is feature scaling, and why is it necessary? 35. What is outlier detection, and how can you identify outliers in a dataset? Data Exploration: 36. What is exploratory data analysis (EDA), and why is it important? 37. Explain the concept of data distribution. 38. What are box plots, and how are they used in EDA? 39. What is a histogram, and what insights can you gain from it? 40. Describe the concept of data skewness. 41. What are scatter plots, and how are they useful in data analysis? 42. What is a correlation matrix, and how is it used in EDA? 43. How do you handle imbalanced datasets in machine learning? Model Evaluation: 44. What are the common metrics used for evaluating classification models? 45. Explain precision, recall, and F1-score. 46. What is ROC curve analysis, and what does it measure? 47. How do you choose the appropriate evaluation metric for a regression problem? 48. Describe the concept of confusion matrix. 49. What is cross-entropy loss, and how is it used in classification problems? 50. Explain the concept of AUC-ROC. React ❤️ for more

𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗶𝗻 𝗝𝘂𝘀𝘁 𝟯 𝗠𝗼𝗻𝘁𝗵𝘀 𝘄𝗶𝘁𝗵 𝗧𝗵𝗶𝘀 𝗙𝗿𝗲𝗲 𝗚𝗶𝘁𝗛𝘂𝗯 𝗥𝗼𝗮𝗱𝗺𝗮𝗽😍 🎯 Want to Master Data Science in Just 3 Months?📊 Feeling overwhelmed by the sheer volume of resources and don’t know where to start? You’re not alone🚀 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/43uHPrX This FREE GitHub roadmap is a game-changer for anyone✅️

Please go through this top 10 SQL projects with Datasets that you can practice and can add in your resume 📌1. Social Media Analytics: (https://www.kaggle.com/amanajmera1/framingham-heart-study-dataset) 🚀2. Web Analytics: (https://www.kaggle.com/zynicide/wine-reviews) 📌3. HR Analytics: (https://www.kaggle.com/pavansubhasht/ibm-hr-analytics- attrition-dataset) 🚀4. Healthcare Data Analysis: (https://www.kaggle.com/cdc/mortality) 📌5. E-commerce Analysis: (https://www.kaggle.com/olistbr/brazilian-ecommerce) 🚀6. Inventory Management: (https://www.kaggle.com/datasets? search=inventory+management) 📌 7.Customer Relationship Management: (https://www.kaggle.com/pankajjsh06/ibm-watson- marketing-customer-value-data) 🚀8. Financial Data Analysis: (https://www.kaggle.com/awaiskalia/banking-database) 📌9. Supply Chain Management: (https://www.kaggle.com/shashwatwork/procurement-analytics) 🚀10. Analysis of Sales Data: (https://www.kaggle.com/kyanyoga/sample-sales-data) Small suggestion from my side for non tech students: kindly pick those datasets which you like the subject in general, that way you will be more excited to practice it, instead of just doing it for the sake of resume, you will learn SQL more passionately, since it’s a programming language try to make it more exciting for yourself. Join for more: https://t.me/DataPortfolio Hope this piece of information helps you

𝟴 𝗕𝗲𝘀𝘁 𝗙𝗿𝗲𝗲 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗿𝗼𝗺 𝗛𝗮𝗿𝘃𝗮𝗿𝗱, 𝗠𝗜𝗧 & 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱😍 🎓 Learn Data Science for Free from the World’s Best Universities🚀 Top institutions like Harvard, MIT, and Stanford are offering world-class data science courses online — and they’re 100% free. 🎯📍 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/3Hfpwjc All The Best 👍

𝟮𝟳 𝗥𝗲𝗮𝗹 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗳𝗿𝗼𝗺 𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗟𝗶𝗸𝗲 𝗜𝗕𝗠, 𝗖𝗮𝗽𝗴𝗲𝗺𝗶𝗻𝗶 & 𝗗𝗲𝗹𝗼𝗶𝘁𝘁𝗲😍 This blog brings you 27 real Power BI interview questions asked by top companies like IBM, Capgemini, Deloitte, and more🗣📌 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4dFem3o Most important—interview questions✅️

To start with Machine Learning:    1. Learn Python    2. Practice using Google Colab     Take these free courses: https://t.me/datasciencefun/290 If you need a bit more time before diving deeper, finish the Kaggle tutorials. At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle. If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed. From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit. The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them: https://t.me/datasciencefree/259 Many different books will help you. The attached image will give you an idea of my favorite ones. Finally, keep these three ideas in mind: 1. Start by working on solved problems so you can find help whenever you get stuck. 2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice. 3. Find a community on LinkedIn or 𝕏 and share your work. Ask questions, and help others. During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right. Here is the good news: Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space. Focus on finding your path, and Write. More. Code. That's how you win.✌️✌️

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𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗧𝗲𝗰𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍 🚀 Learn In-Demand Tech Skills for Free — Certified by Microsoft! These free Microsoft-certified online courses are perfect for beginners, students, and professionals looking to upskill 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/3Hio2Vg Enroll For FREE & Get Certified🎓️

𝗧𝗼𝗽 𝗠𝗡𝗖𝘀 𝗢𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍 Google :- https://pdlink.in/3H2YJX7 Microsoft :- https://pdlink.in/4iq8QlM Infosys :- https://pdlink.in/4jsHZXf IBM :- https://pdlink.in/3QyJyqk Cisco :- https://pdlink.in/4fYr1xO Enroll For FREE & Get Certified 🎓

7 Free Kaggle Micro-Courses for Data Science Beginners with Certification Python https://www.kaggle.com/learn/python Pandas https://www.kaggle.com/learn/pandas Data visualization https://www.kaggle.com/learn/data-visualization Intro to sql https://www.kaggle.com/learn/intro-to-sql Advanced Sql https://www.kaggle.com/learn/advanced-sql Intro to ML https://www.kaggle.com/learn/intro-to-machine-learning Advanced ML https://www.kaggle.com/learn/intermediate-machine-learning More data analytics resources https://t.me/sqlspecialist Hope it helps :)

𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗘𝗻𝗿𝗼𝗹𝗹 𝗜𝗻 𝟮𝟬𝟮𝟱 😍 Data Analytics :- https://pdlink.in/3Fq7E4p Data Science :- https://pdlink.in/4iSWjaP SQL :- https://pdlink.in/3EyjUPt Python :- https://pdlink.in/4c7hGDL Web Dev :- https://bit.ly/4ffFnJZ AI :- https://pdlink.in/4d0SrTG Enroll For FREE & Get Certified 🎓

Some helpful Data science projects for beginners https://www.kaggle.com/c/house-prices-advanced-regression-techniques https://www.kaggle.com/c/digit-recognizer https://www.kaggle.com/c/titanic Intermediate Level Data science Projects Black Friday Data : https://www.kaggle.com/sdolezel/black-friday Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset Million Song Data : https://www.kaggle.com/c/msdchallenge Census Income Data : https://www.kaggle.com/c/census-income/data Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2 Text mining : https://www.kaggle.com/kanncaa1/applying-text-mining

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A step-by-step guide on getting set up 👇 𝗦𝘁𝗲𝗽 𝟭: 𝗜𝗻𝘀𝘁𝗮𝗹𝗹 𝗮 𝗦𝗤𝗟 𝗱𝗿𝗶𝘃𝗲𝗿 You’ll need to a database engine first. This is what will 𝘳𝘶𝘯 your SQL queries. Pick 𝘦𝘪𝘵𝘩𝘦𝘳 PostgreSQL 𝘰𝘳 mySQL. Both are free & widely used. Download PostgreSQL driver: https://lnkd.in/gbvqNSWr Download mySQL driver: https://lnkd.in/gCct2Qw3 During installation, make note of: • Your username • Your password • The port number 𝗦𝘁𝗲𝗽 𝟮: 𝗜𝗻𝘀𝘁𝗮𝗹𝗹 𝗮 𝗦𝗤𝗟 𝗰𝗹𝗶𝗲𝗻𝘁 Next, you’ll need a SQL client to ↳ Connect to a database ↳ Explore your data ↳ Run queries My recommendations (both free!) ↳ PostgreSQL use 𝘱𝘨𝘈𝘥𝘮𝘪𝘯: https://lnkd.in/gs8u594Z ↳ MySQL use 𝘔𝘺𝘚𝘘𝘓 𝘞𝘰𝘳𝘬𝘣𝘦𝘯𝘤𝘩: https://lnkd.in/gtG3UCaZ 𝗦𝘁𝗲𝗽 𝟯: 𝗖𝗼𝗻𝗻𝗲𝗰𝘁 𝘁𝗼 𝘆𝗼𝘂𝗿 𝘀𝗲𝗿𝘃𝗲𝗿 Now, you’ll connect your DB engine & client. Open your SQL client & create a new connection. Use these details from Step 1: • 𝗛𝗼𝘀𝘁: likely 𝘭𝘰𝘤𝘢𝘭𝘩𝘰𝘴𝘵 • 𝗣𝗼𝗿𝘁 • 𝗨𝘀𝗲𝗿𝗻𝗮𝗺𝗲 • 𝗣𝗮𝘀𝘀𝘄𝗼𝗿𝗱 Then hit "Connect!" 𝗦𝘁𝗲𝗽 𝟰: 𝗖𝗿𝗲𝗮𝘁𝗲 𝗮 𝗻𝗲𝘄 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲 Now you’re connected, let’s create a database. 𝘙𝘦𝘤𝘢𝘭𝘭: 𝘵𝘩𝘦 𝘥𝘢𝘵𝘢𝘣𝘢𝘴𝘦 𝘪𝘴 𝘸𝘩𝘦𝘳𝘦 𝘺𝘰𝘶’𝘭𝘭 𝘢𝘥𝘥 𝘺𝘰𝘶𝘳 𝘵𝘢𝘣𝘭𝘦𝘴. Run this SQL command: 𝘊𝘙𝘌𝘈𝘛𝘌 𝘋𝘈𝘛𝘈𝘉𝘈𝘚𝘌 𝘴𝘲𝘭_𝘱𝘳𝘰𝘫𝘦𝘤𝘵 𝗦𝘁𝗲𝗽 𝟱. 𝗜𝗺𝗽𝗼𝗿𝘁 𝘆𝗼𝘂𝗿 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 Assume you have your data in CSV file. 𝗙𝗶𝗿𝘀𝘁, create a table with column names + types: 𝘊𝘙𝘌𝘈𝘛𝘌 𝘛𝘈𝘉𝘓𝘌 𝘮𝘺_𝘵𝘢𝘣𝘭𝘦 ( 𝘤𝘰𝘭𝘶𝘮𝘯1 𝘛𝘌𝘟𝘛, 𝘤𝘰𝘭𝘶𝘮𝘯2 𝘐𝘕𝘛, ... 𝘦𝘵𝘤 ); 𝗦𝗲𝗰𝗼𝗻𝗱, import the data using the Data Import tools. Need a dataset? Here are 3 starter datasets: • Global country information: https://lnkd.in/gWpmgkZb • Restaurant sales: https://lnkd.in/gwWnKYw4 • Fight routes: https://lnkd.in/gwp3c4aE 𝗦𝘁𝗲𝗽 𝟲. 𝗩𝗲𝗿𝗶𝗳𝘆 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 Check that your data loaded correctly: 𝘚𝘌𝘓𝘌𝘊𝘛 * 𝘍𝘙𝘖𝘔 𝘮𝘺_𝘵𝘢𝘣𝘭𝘦 𝘓𝘐𝘔𝘐𝘛 10; Look at the data — is this what you were expecting? 𝗦𝘁𝗲𝗽 𝟳. 𝗦𝘁𝗮𝗿𝘁 𝘆𝗼𝘂𝗿 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 Now you're all set up & ready for your analysis!