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

Dataset Name: Fruit Detection Dataset Basic Description: Multilabel Fruits Detection 📖 FULL DATASET DESCRIPTION: ================================== The dataset includes 8479 images of 6 different fruits(Apple, Grapes, Pineapple, Orange, Banana, and Watermelon). Fruits are annotated in YOLOv8 format. The following pre-processing was applied to each image: The following augmentation was applied to create 3 versions of each source image: The following transformations were applied to the bounding boxes of each image: 📥 DATASET DOWNLOAD INFORMATION ================================== 🔴 Dataset Size: Download dataset as zip (525 MB) 🔰 Direct dataset download link: https://www.kaggle.com/api/v1/datasets/download/lakshaytyagi01/fruit-detection 📊 Additional information: ================================== Total files: 17,000 Views: 26,500 Downloads: 4,298 📚 RELATED NOTEBOOKS: ================================== 1. 🍍🍌🍓 YOLO-NAS 🏎💨 Fruit Detection 🍇🍒🍊 | Upvotes: 163    URL: https://www.kaggle.com/code/harpdeci/yolo-nas-fruit-detection 2. K-Fold Cross Validation and YoloV8 | Upvotes: 58    URL: https://www.kaggle.com/code/tataganesh/k-fold-cross-validation-and-yolov8 3. Fruits_objectdetection 🍍🍎 | Upvotes: 44    URL: https://www.kaggle.com/code/maryamayman20/fruits-objectdetection 4. Comprehensive Fruit Image Dataset | Upvotes: 13    URL: https://www.kaggle.com/datasets/evilspirit05/comprehensive-fruit-image-dataset 5. Fruit Infection Disease Dataset | Upvotes: 11    URL: https://www.kaggle.com/datasets/nikitkashyap/fruit-infection-disease-dataset ============================

Essential SQL Topics for Data Analysts SQL for Data Analysts Free Resources -> https://t.me/sqlanalyst - Basic Queries: SELECT, FROM, WHERE clauses. - Sorting and Filtering: ORDER BY, GROUP BY, HAVING. - Joins: INNER JOIN, LEFT JOIN, RIGHT JOIN. - Aggregation Functions: COUNT, SUM, AVG, MIN, MAX. - Subqueries: Embedding queries within queries. - Data Modification: INSERT, UPDATE, DELETE. - Indexes: Optimizing query performance. - Normalization: Ensuring efficient database design. - Views: Creating virtual tables for simplified queries. - Understanding Database Relationships: One-to-One, One-to-Many, Many-to-Many. Window functions are also important for data analysts. They allow for advanced data analysis and manipulation within specified subsets of data. Commonly used window functions include: - ROW_NUMBER(): Assigns a unique number to each row based on a specified order. - RANK() and DENSE_RANK(): Rank data based on a specified order, handling ties differently. - LAG() and LEAD(): Access data from preceding or following rows within a partition. - SUM(), AVG(), MIN(), MAX(): Aggregations over a defined window of rows. Here is an amazing resources to learn & practice SQL: https://bit.ly/3FxxKPz Share with credits: https://t.me/sqlspecialist Hope it helps :)

🤔Are you looking for some new project ideas to include in your Portfolio❓ 👉 Here are 3 unique ideas for you: 1️⃣ Summer Olympics Dataset : https://www.kaggle.com/datasets/divyansh22/summer-olympics-medals 2️⃣ Food Nutrition Dataset : https://www.kaggle.com/datasets/utsavdey1410/food-nutrition-dataset/data 3️⃣ Mental health Dataset : https://www.kaggle.com/datasets/programmerrdai/mental-health-dataset/data

Machine Learning Algorithm: 1. Linear Regression:    - Imagine drawing a straight line on a graph to show the relationship between two things, like how the height of a plant might relate to the amount of sunlight it gets. 2. Decision Trees:    - Think of a game where you have to answer yes or no questions to find an object. It's like a flowchart helping you decide what the object is based on your answers. 3. Random Forest:    - Picture a group of friends making decisions together. Random Forest is like combining the opinions of many friends to make a more reliable decision. 4. Support Vector Machines (SVM):    - Imagine drawing a line to separate different types of things, like putting all red balls on one side and blue balls on the other, with the line in between them. 5. k-Nearest Neighbors (kNN):    - Pretend you have a collection of toys, and you want to find out which toys are similar to a new one. kNN is like asking your friends which toys are closest in looks to the new one. 6. Naive Bayes:    - Think of a detective trying to solve a mystery. Naive Bayes is like the detective making guesses based on the probability of certain clues leading to the culprit. 7. K-Means Clustering:    - Imagine sorting your toys into different groups based on their similarities, like putting all the cars in one group and all the dolls in another. 8. Hierarchical Clustering:    - Picture organizing your toys into groups, and then those groups into bigger groups. It's like creating a family tree for your toys based on their similarities. 9. Principal Component Analysis (PCA):    - Suppose you have many different measurements for your toys, and PCA helps you find the most important ones to understand and compare them easily. 10. Neural Networks (Deep Learning):     - Think of a robot brain with lots of interconnected parts. Each part helps the robot understand different aspects of things, like recognizing shapes or colors. 11. Gradient Boosting algorithms:     - Imagine you are trying to reach the top of a hill, and each time you take a step, you learn from the mistakes of the previous step to get closer to the summit. XGBoost and LightGBM are like smart ways of learning from those steps. Share with credits: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D ENJOY LEARNING 👍👍

🔍 Real-World Data Analyst Tasks & How to Solve Them As a Data Analyst, your job isn’t just about writing SQL queries or making dashboards—it’s about solving business problems using data. Let’s explore some common real-world tasks and how you can handle them like a pro! 📌 Task 1: Cleaning Messy Data Before analyzing data, you need to remove duplicates, handle missing values, and standardize formats. ✅ Solution (Using Pandas in Python):

import pandas as pd  
df = pd.read_csv('sales_data.csv')  
df.drop_duplicates(inplace=True)  # Remove duplicate rows  
df.fillna(0, inplace=True)  # Fill missing values with 0  
print(df.head())

💡 Tip: Always check for inconsistent spellings and incorrect date formats! 📌 Task 2: Analyzing Sales Trends A company wants to know which months have the highest sales. ✅ Solution (Using SQL):

SELECT MONTH(SaleDate) AS Month, SUM(Quantity * Price) AS Total_Revenue  
FROM Sales  
GROUP BY MONTH(SaleDate)  
ORDER BY Total_Revenue DESC;

💡 Tip: Try adding YEAR(SaleDate) to compare yearly trends! 📌 Task 3: Creating a Business Dashboard Your manager asks you to create a dashboard showing revenue by region, top-selling products, and monthly growth. ✅ Solution (Using Power BI / Tableau): 👉 Add KPI Cards to show total sales & profit 👉 Use a Line Chart for monthly trends 👉 Create a Bar Chart for top-selling products 👉 Use Filters/Slicers for better interactivity 💡 Tip: Keep your dashboards clean, interactive, and easy to interpret! Like this post for more content like this ♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

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

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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 👇👇 https://t.me/DataSimplifier Like this post for if you want me to continue the interview series 👍♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

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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 👍👍

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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 Resources👇 https://t.me/DataSimplifier Hope it helps :)

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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, …): 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, …): Returns the first non-null value. Like for more free Cheatsheets ❤️ Share with credits: https://t.me/sqlspecialist Hope it helps :) #dataanalytics

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Here are the questions With Answers ✨ 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 ‘HR’. [ 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 ‘S’. [ 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 ‘A’. [ SELECT * FROM EmployeeInfo WHERE EmpFname LIKE '%A'; ] 10. Create a query to fetch details of employees having “DELHI” 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 tables’ 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. ]