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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 39 490 subscribers, ranking 4 752 in the Education category and 10 399 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 39 490 subscribers.

According to the latest data from 09 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 197 over the last 30 days and by 10 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.73%. Within the first 24 hours after publication, content typically collects 1.01% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 1 079 views. Within the first day, a publication typically gains 400 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 10 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.

39 490
Subscribers
+1024 hours
+457 days
+19730 days
Posts Archive
๐—”๐—ฐ๐—ฒ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—ฆ๐—ค๐—Ÿ ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐˜ƒ๐—ถ๐—ฒ๐˜„ ๐˜„๐—ถ๐˜๐—ต ๐—ง๐—ต๐—ฒ๐˜€๐—ฒ ๐Ÿฏ๐Ÿฌ ๐— ๐—ผ๐˜€๐˜-๐—”๐˜€๐—ธ๐—ฒ๐—ฑ ๐—ค๐˜‚๐—ฒ๐˜€๐˜๐—ถ๐—ผ๐—ป๐˜€! ๐Ÿ˜ ๐Ÿคฆ๐Ÿปโ€โ™€๏ธStruggli
๐—”๐—ฐ๐—ฒ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—ฆ๐—ค๐—Ÿ ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐˜ƒ๐—ถ๐—ฒ๐˜„ ๐˜„๐—ถ๐˜๐—ต ๐—ง๐—ต๐—ฒ๐˜€๐—ฒ ๐Ÿฏ๐Ÿฌ ๐— ๐—ผ๐˜€๐˜-๐—”๐˜€๐—ธ๐—ฒ๐—ฑ ๐—ค๐˜‚๐—ฒ๐˜€๐˜๐—ถ๐—ผ๐—ป๐˜€! ๐Ÿ˜ ๐Ÿคฆ๐Ÿปโ€โ™€๏ธStruggling with SQL interviews? Not anymore!๐Ÿ“ SQL interviews can be challenging, but preparation is the key to success. Whether youโ€™re aiming for a data analytics role or just brushing up, this resource has got your back!๐ŸŽŠ ๐‹๐ข๐ง๐ค๐Ÿ‘‡:- https://pdlink.in/4olhd6z Letโ€™s crack that interview together!โœ…๏ธ

๐ŸŒฎ Data Analyst Vs Data Engineer Vs Data Scientist ๐ŸŒฎ Skills required to become data analyst ๐Ÿ‘‰ Advanced Excel, Oracle/SQL ๐Ÿ‘‰ Python/R Skills required to become data engineer ๐Ÿ‘‰ Python/ Java. ๐Ÿ‘‰ SQL, NoSQL technologies like Cassandra or MongoDB ๐Ÿ‘‰ Big data technologies like Hadoop, Hive/ Pig/ Spark Skills required to become data Scientist ๐Ÿ‘‰ In-depth knowledge of tools like R/ Python/ SAS. ๐Ÿ‘‰ Well versed in various machine learning algorithms like scikit-learn, karas and tensorflow ๐Ÿ‘‰ SQL and NoSQL Bonus skill required: Data Visualization (PowerBI/ Tableau) & Statistics

๐Ÿš€ ๐—š๐—ผ๐—ผ๐—ด๐—น๐—ฒ ๐Ÿญ๐Ÿฌ๐Ÿฌ% ๐—™๐—ฅ๐—˜๐—˜ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ | ๐—˜๐—ป๐—ฟ๐—ผ๐—น๐—น ๐—ก๐—ผ๐˜„ ๐Ÿ˜ Upgrade your tech skills
๐Ÿš€ ๐—š๐—ผ๐—ผ๐—ด๐—น๐—ฒ ๐Ÿญ๐Ÿฌ๐Ÿฌ% ๐—™๐—ฅ๐—˜๐—˜ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ | ๐—˜๐—ป๐—ฟ๐—ผ๐—น๐—น ๐—ก๐—ผ๐˜„ ๐Ÿ˜ Upgrade your tech skills with FREE certification courses from Google ๐Ÿ“š Courses Offered: 1๏ธโƒฃ Google Cloud โ€“ Generative AI 2๏ธโƒฃ Google Cloud Computing Foundations with Kubernetes ๐‹๐ข๐ง๐ค ๐Ÿ‘‡:-  https://pdlink.in/46uQii9 โœ… 100% Online | ๐ŸŽ“ Get Certified by Google Cloud

Top companies currently hiring data analysts Based on the current job market in 2025, here are the top companies hiring data analysts: ## Top Tech Companies - Meta: Investing heavily in AI with significant GPU investments - Amazon: Offers diverse data analyst roles with complex responsibilities - Google (Alphabet): Leverages massive data ecosystems - JP Morgan Chase & Co.: Strong focus on data-driven banking transformation ## Specialized Data Analytics Firms - Tiger Analytics: Specializes in AI/ML solutions - SG Analytics: Provides data-driven insights - Monte Carlo Data: Focuses on data observability - CB Insights: Excels in market intelligence ## Emerging Opportunities Companies like Samsara, ScienceSoft, and Forage are also actively recruiting data analysts, offering competitive salaries ranging from $85,000 to $207,000 annually. I have curated best 80+ top-notch Data Analytics Resources ๐Ÿ‘‡๐Ÿ‘‡ https://t.me/DataSimplifier Like this post for if you want me to continue the interview series ๐Ÿ‘โ™ฅ๏ธ Share with credits: https://t.me/sqlspecialist Hope it helps :)

๐—ช๐—ฎ๐—ป๐˜ ๐˜๐—ผ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป ๐—ง๐—ฒ๐—ฐ๐—ต ๐—ฎ๐—ป๐—ฑ ๐—ฆ๐—ผ๐—ณ๐˜ ๐—ฆ๐—ธ๐—ถ๐—น๐—น๐˜€ ๐—ณ๐—ผ๐—ฟ ๐—™๐—ฅ๐—˜๐—˜? ๐—œ๐—ป๐—ณ๐—ผ๐˜€๐˜†๐˜€ ๐—ฆ๐—ฝ๐—ฟ๐—ถ๐—ป๐—ด๐—ฏ๐—ผ๐—ฎ๐—ฟ๐—ฑ ๐—ถ๐˜€ ๐—ฎ
๐—ช๐—ฎ๐—ป๐˜ ๐˜๐—ผ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป ๐—ง๐—ฒ๐—ฐ๐—ต ๐—ฎ๐—ป๐—ฑ ๐—ฆ๐—ผ๐—ณ๐˜ ๐—ฆ๐—ธ๐—ถ๐—น๐—น๐˜€ ๐—ณ๐—ผ๐—ฟ ๐—™๐—ฅ๐—˜๐—˜? ๐—œ๐—ป๐—ณ๐—ผ๐˜€๐˜†๐˜€ ๐—ฆ๐—ฝ๐—ฟ๐—ถ๐—ป๐—ด๐—ฏ๐—ผ๐—ฎ๐—ฟ๐—ฑ ๐—ถ๐˜€ ๐—ฎ ๐—š๐—ฎ๐—บ๐—ฒ-๐—–๐—ต๐—ฎ๐—ป๐—ด๐—ฒ๐—ฟ๐Ÿ˜ ๐Ÿ’ธ Not everyone can afford expensive online coursesโ€”and honestly, you donโ€™t need to๐Ÿ’ซ In 2025, upskilling doesnโ€™t have to cost you a single rupee.๐Ÿ“Š๐Ÿ“Œ ๐‹๐ข๐ง๐ค๐Ÿ‘‡:- https://pdlink.in/46uRDWc Completely free access to high-quality learning resourcesโœ…๏ธ

๐ŸŽ“ ๐€๐œ๐œ๐ž๐ง๐ญ๐ฎ๐ซ๐ž ๐…๐‘๐„๐„ ๐‚๐ž๐ซ๐ญ๐ข๐Ÿ๐ข๐œ๐š๐ญ๐ข๐จ๐ง ๐‚๐จ๐ฎ๐ซ๐ฌ๐ž๐ฌ | ๐—˜๐—ป๐—ฟ๐—ผ๐—น๐—น ๐—ก๐—ผ๐˜„ ๐Ÿ˜ Boost your skills with 100%
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Here is the list of few projects (found on kaggle). They cover Basics of Python, Advanced Statistics, Supervised Learning (Regression and Classification problems) & Data Science Please also check the discussions and notebook submissions for different approaches and solution after you tried yourself. 1. Basic python and statistics Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness Automobile :- https://www.kaggle.com/toramky/automobile-dataset 2. Advanced Statistics Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset 3. Supervised Learning a) Regression Problems How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview b) Classification problems Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview Titanic :- https://www.kaggle.com/c/titanic San Francisco crime:- https://www.kaggle.com/c/sf-crime Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification Categorize cusine:- https://www.kaggle.com/c/whats-cooking 4. 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 5. 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 Share with credits: https://t.me/sqlproject ENJOY LEARNING ๐Ÿ‘๐Ÿ‘

๐—ง๐—ต๐—ฒ ๐—•๐—ฒ๐˜€๐˜ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐Ÿฏ๐Ÿฌ-๐——๐—ฎ๐˜† ๐—ฅ๐—ผ๐—ฎ๐—ฑ๐—บ๐—ฎ๐—ฝ ๐˜๐—ผ ๐—ฆ๐˜๐—ฎ๐—ฟ๐˜ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐——๐—ฎ๐˜๐—ฎ ๐—ฆ๐—ฐ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ ๐—๐—ผ๐˜‚๐—ฟ๐—ป๐—ฒ๐˜†๐Ÿ˜ ๐Ÿ“Š If I
๐—ง๐—ต๐—ฒ ๐—•๐—ฒ๐˜€๐˜ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐Ÿฏ๐Ÿฌ-๐——๐—ฎ๐˜† ๐—ฅ๐—ผ๐—ฎ๐—ฑ๐—บ๐—ฎ๐—ฝ ๐˜๐—ผ ๐—ฆ๐˜๐—ฎ๐—ฟ๐˜ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐——๐—ฎ๐˜๐—ฎ ๐—ฆ๐—ฐ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ ๐—๐—ผ๐˜‚๐—ฟ๐—ป๐—ฒ๐˜†๐Ÿ˜ ๐Ÿ“Š If I had to restart my Data Science journey in 2025, this is where Iโ€™d beginโœจ๏ธ Meet 30 Days of Data Science โ€” a free and beginner-friendly GitHub repository that guides you through the core fundamentals of data science in just one month๐Ÿง‘โ€๐ŸŽ“๐Ÿ“Œ ๐‹๐ข๐ง๐ค๐Ÿ‘‡:- https://pdlink.in/4mfNdXR Simply bookmark the page, pick Day 1, and begin your journeyโœ…๏ธ

๐—•๐—ฒ๐—ฐ๐—ผ๐—บ๐—ฒ ๐—ฎ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฒ๐—ฑ ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜€๐˜ ๐—œ๐—ป ๐—ง๐—ผ๐—ฝ ๐— ๐—ก๐—–๐˜€๐Ÿ˜ Learn Data Analytics, Data Science & AI Fro
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If you are interested to learn SQL for data analytics purpose and clear the interviews, just cover the following topics 1)Install MYSQL workbench 2) Select 3) From 4) where 5) group by 6) having 7) limit 8) Joins (Left, right , inner, self, cross) 9) Aggregate function ( Sum, Max, Min , Avg) 9) windows function ( row num, rank, dense rank, lead, lag, Sum () over) 10)Case 11) Like 12) Sub queries 13) CTE 14) Replace CTE with temp tables 15) Methods to optimize Sql queries 16) Solve problems and case studies at Ankit Bansal youtube channel Trick: Just copy each term and paste on youtube and watch any 10 to 15 minute on each topic and practise it while learning , By doing this , you get the basics understanding 17) Now time to go on youtube and search data analysis end to end project using sql 18) Watch them and practise them end to end. 17) learn integration with power bi In this way , you will not only memorize the concepts but also learn how to implement them in your current working and projects and will be able to defend it in your interviews as well. Like for more Here you can find essential SQL Interview Resources๐Ÿ‘‡ https://t.me/DataSimplifier Hope it helps :)

๐Ÿš€๐—ง๐—ผ๐—ฝ ๐Ÿฏ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—š๐—ผ๐—ผ๐—ด๐—น๐—ฒ-๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฒ๐—ฑ ๐—ฃ๐˜†๐˜๐—ต๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ๐Ÿ˜ Want to boost your tech career? L
๐Ÿš€๐—ง๐—ผ๐—ฝ ๐Ÿฏ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—š๐—ผ๐—ผ๐—ด๐—น๐—ฒ-๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฒ๐—ฑ ๐—ฃ๐˜†๐˜๐—ต๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ๐Ÿ˜ Want to boost your tech career? Learn Python for FREE with Google-certified courses! Perfect for beginnersโ€”no expensive bootcamps needed. ๐Ÿ”ฅ Learn Python for AI, Data, Automation & More! ๐Ÿ“๐—ฆ๐˜๐—ฎ๐—ฟ๐˜ ๐—ก๐—ผ๐˜„๐Ÿ‘‡ https://pdlink.in/42okGqG โœ… Future You Will Thank You!

Quick SQL functions cheat sheet for beginners Aggregate Functions COUNT(*): Counts rows. SUM(column): Total sum. AVG(column): Average value. MAX(column): Maximum value. MIN(column): Minimum value. String Functions CONCAT(a, b, โ€ฆ): Concatenates strings. SUBSTRING(s, start, length): Extracts part of a string. UPPER(s) / LOWER(s): Converts string case. TRIM(s): Removes leading/trailing spaces. Date & Time Functions CURRENT_DATE / CURRENT_TIME / CURRENT_TIMESTAMP: Current date/time. EXTRACT(unit FROM date): Retrieves a date part (e.g., year, month). DATE_ADD(date, INTERVAL n unit): Adds an interval to a date. Numeric Functions ROUND(num, decimals): Rounds to a specified decimal. CEIL(num) / FLOOR(num): Rounds up/down. ABS(num): Absolute value. MOD(a, b): Returns the remainder. Control Flow Functions CASE: Conditional logic. COALESCE(val1, val2, โ€ฆ): Returns the first non-null value. Like for more free Cheatsheets โค๏ธ Share with credits: https://t.me/sqlspecialist Hope it helps :) #dataanalytics

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Here are the questions With Answers โœจ 1. Write a query to get the EmpFname from the EmployeeInfo table in the upper case using the alias name as EmpName. [ SELECT UPPER(EmpFname) AS EmpName FROM EmployeeInfo; ] 2. Write a query to get the number of employees working in the department โ€˜HRโ€™. [ SELECT COUNT(*) FROM EmployeeInfo WHERE Department = 'HR'; ] 3. What query will you write to fetch the current date? [ -- For SQL Server: SELECT GETDATE(); -- For MySQL: SELECT SYSDATE(); ] 4. Write a query to fetch only the place name (string before brackets) from the Address column of the EmployeeInfo table. [ -- Using MID function in MySQL: SELECT MID(Address, 1, LOCATE('(', Address) - 1) FROM EmployeeInfo; -- Using SUBSTRING function: SELECT SUBSTRING(Address, 1, CHARINDEX('(', Address) - 1) FROM EmployeeInfo; ] 5. Write a query to create a new table whose data and structure are copied from another table. [ -- Using SELECT INTO in SQL Server: SELECT * INTO NewTable FROM EmployeeInfo WHERE 1 = 0; -- Using CREATE TABLE AS in MySQL: CREATE TABLE NewTable AS SELECT * FROM EmployeeInfo; ] 6. Write a query to display the names of employees that begin with โ€˜Sโ€™. [ SELECT * FROM EmployeeInfo WHERE EmpFname LIKE 'S%'; ] 7. Write a query to retrieve the top N records. [ -- Using TOP in SQL Server: SELECT TOP N * FROM EmployeePosition ORDER BY Salary DESC; -- Using LIMIT in MySQL: SELECT * FROM EmployeePosition ORDER BY Salary DESC LIMIT N; ] 8. Write a query to obtain relevant records from the EmployeeInfo table ordered by Department in ascending order and EmpLname in descending order. [ SELECT * FROM EmployeeInfo ORDER BY Department ASC, EmpLname DESC; ] 9. Write a query to get the details of employees whose EmpFname ends with โ€˜Aโ€™. [ SELECT * FROM EmployeeInfo WHERE EmpFname LIKE '%A'; ] 10. Create a query to fetch details of employees having โ€œDELHIโ€ as their address. [ SELECT * FROM EmployeeInfo WHERE Address LIKE '%DELHI%'; ] 11. Write a query to fetch all employees who also hold the managerial position. [ SELECT E.EmpFname, E.EmpLname, P.EmpPosition FROM EmployeeInfo E INNER JOIN EmployeePosition P ON E.EmpID = P.EmpID WHERE P.EmpPosition = 'Manager'; ] 12. Create a query to generate the first and last records from the EmployeeInfo table. [ -- First record: SELECT * FROM EmployeeInfo WHERE EmpID = (SELECT MIN(EmpID) FROM EmployeeInfo); -- Last record: SELECT * FROM EmployeeInfo WHERE EmpID = (SELECT MAX(EmpID) FROM EmployeeInfo); ] 13. Create a query to check if the passed value to the query follows the EmployeeInfo and EmployeePosition tablesโ€™ date format. [ SELECT ISDATE('01/04/2020') AS "MM/DD/YY"; ] 14. Create a query to obtain display employees having salaries equal to or greater than 150000. [ SELECT EmpName FROM EmployeePosition WHERE Salary >= 150000; ] 15. Write a query to fetch the year using a date. [ SELECT YEAR(GETDATE()) AS "Year"; ] 16. Create an SQL query to fetch EmpPosition and the total salary paid for each employee position. [ SELECT EmpPosition, SUM(Salary) FROM EmployeePosition GROUP BY EmpPosition; ] 17. Write a query to find duplicate records from a table. [ SELECT EmpID, EmpFname, Department, COUNT(*) FROM EmployeeInfo GROUP BY EmpID, EmpFname, Department HAVING COUNT(*) > 1; ] 18. Create a query to fetch the third-highest salary from the EmpPosition table. [ SELECT TOP 1 Salary FROM ( SELECT TOP 3 Salary FROM EmpPosition ORDER BY Salary DESC ) AS ThirdHighestSalary ORDER BY Salary ASC; ] 19. Write an SQL query to find even and odd records in the EmployeeInfo table. [ -- Even records: SELECT EmpID FROM (SELECT ROW_NUMBER() OVER (ORDER BY EmpID) AS rowno, EmpID FROM EmployeeInfo) AS T1 WHERE MOD(rowno, 2) = 0; -- Odd records: SELECT EmpID FROM (SELECT ROW_NUMBER() OVER (ORDER BY EmpID) AS rowno, EmpID FROM EmployeeInfo) AS T1 WHERE MOD(rowno, 2) = 1; ] 20. Create a query to fetch the list of employees of the same department. [ SELECT DISTINCT E1.EmpID, E1.EmpFname, E1.Department FROM EmployeeInfo E1 INNER JOIN EmployeeInfo E2 ON E1.Department = E2. ]

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Hey guys, Today, letโ€™s talk about SQL conceptual questions that are often asked in data analyst interviews. These questions test not only your technical skills but also your conceptual understanding of SQL and its real-world applications. 1. What is the difference between SQL and NoSQL? - SQL (Structured Query Language) is a relational database management system, meaning it uses tables (rows and columns) to store data. - NoSQL databases, on the other hand, handle unstructured data and donโ€™t rely on a schema, making them more flexible in terms of data storage and retrieval. - Interview Tip: Don't just memorize definitions. Be prepared to explain scenarios where youโ€™d use SQL over NoSQL, and vice versa. 2. What is the difference between INNER JOIN and OUTER JOIN? - An INNER JOIN returns records that have matching values in both tables. - An OUTER JOIN returns all records from one table and the matched records from the second table. If there's no match, NULL values are returned. 3. How do you optimize a SQL query for better performance? - Indexing: Create indexes on columns used frequently in WHERE, JOIN, or GROUP BY clauses. - Query optimization: Use appropriate WHERE clauses to reduce the data set and avoid unnecessary calculations. - Avoid SELECT *: Always specify the columns you need to reduce the amount of data retrieved. - Limit results: If you only need a subset of the data, use the LIMIT clause. 4. What are the different types of SQL constraints? Constraints are used to enforce rules on data in a table. They ensure the accuracy and reliability of the data. The most common types are: - PRIMARY KEY: Ensures each record is unique and not null. - FOREIGN KEY: Enforces a relationship between two tables. - UNIQUE: Ensures all values in a column are unique. - NOT NULL: Prevents NULL values from being entered into a column. - CHECK: Ensures a column's values meet a specific condition. 5. What is normalization? What are the different normal forms? Normalization is the process of organizing data to reduce redundancy and improve data integrity. Hereโ€™s a quick overview of normal forms: - 1NF (First Normal Form): Ensures that all values in a table are atomic (indivisible). - 2NF (Second Normal Form): Ensures that the table is in 1NF and that all non-key columns are fully dependent on the primary key. - 3NF (Third Normal Form): Ensures that the table is in 2NF and all columns are independent of each other except for the primary key. 6. What is a subquery? A subquery is a query within another query. It's used to perform operations that need intermediate results before generating the final query. Example:
SELECT employee_id, name
FROM employees
WHERE salary > (SELECT AVG(salary) FROM employees);
In this case, the subquery calculates the average salary, and the outer query selects employees whose salary is greater than the average. 7. What is the difference between a UNION and a UNION ALL? - UNION combines the result sets of two SELECT statements and removes duplicates. - UNION ALL combines the result sets and includes duplicates. 8. What is the difference between WHERE and HAVING clause? - WHERE filters rows before any groupings are made. Itโ€™s used with SELECT, INSERT, UPDATE, or DELETE statements. - HAVING filters groups after the GROUP BY clause. 9. How would you handle NULL values in SQL? NULL values can represent missing or unknown data. Hereโ€™s how to manage them: - Use IS NULL or IS NOT NULL in WHERE clauses to filter null values. - Use COALESCE() or IFNULL() to replace NULL values with default ones. Example:
SELECT name, COALESCE(age, 0) AS age
FROM employees;
10. What is the purpose of the GROUP BY clause? The GROUP BY clause groups rows with the same values into summary rows. Itโ€™s often used with aggregate functions like COUNT, SUM, AVG, etc. Example:
SELECT department, COUNT(*)
FROM employees
GROUP BY department;
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