Scenario based  Interview Questions & Answers for Data Analyst

1. Scenario: You are working on a SQL database that stores customer information. The database has a table called "Orders" that contains order details. Your task is to write a SQL query to retrieve the total number of orders placed by each customer.
  Question:
  - Write a SQL query to find the total number of orders placed by each customer.
Expected Answer:
    SELECT CustomerID, COUNT(*) AS TotalOrders
    FROM Orders
    GROUP BY CustomerID;

2. Scenario: You are working on a SQL database that stores employee information. The database has a table called "Employees" that contains employee details. Your task is to write a SQL query to retrieve the names of all employees who have been with the company for more than 5 years.
  Question:
  - Write a SQL query to find the names of employees who have been with the company for more than 5 years.
Expected Answer:
    SELECT Name
    FROM Employees
    WHERE DATEDIFF(year, HireDate, GETDATE()) > 5;

Power BI Scenario-Based Questions

1. Scenario: You have been given a dataset in Power BI that contains sales data for a company. Your task is to create a report that shows the total sales by product category and region.
    Expected Answer:
    - Load the dataset into Power BI.
    - Create relationships if necessary.
    - Use the "Fields" pane to select the necessary fields (Product Category, Region, Sales).
    - Drag these fields into the "Values" area of a new visualization (e.g., a table or bar chart).
    - Use the "Filters" pane to filter data as needed.
    - Format the visualization to enhance clarity and readability.

2. Scenario: You have been asked to create a Power BI dashboard that displays real-time stock prices for a set of companies. The stock prices are available through an API.
  Expected Answer:
    - Use Power BI Desktop to connect to the API.
    - Go to "Get Data" > "Web" and enter the API URL.
    - Configure the data refresh settings to ensure real-time updates (e.g., setting up a scheduled refresh or using DirectQuery if supported).
    - Create visualizations using the imported data.
    - Publish the report to the Power BI service and set up a data gateway if needed for continuous refresh.

3. Scenario: You have been given a Power BI report that contains multiple visualizations. The report is taking a long time to load and is impacting the performance of the application.
    Expected Answer:
    - Analyze the current performance using Performance Analyzer.
    - Optimize data model by reducing the number of columns and rows, and removing unnecessary calculations.
    - Use aggregated tables to pre-compute results.
    - Simplify DAX calculations.
    - Optimize visualizations by reducing the number of visuals per page and avoiding complex custom visuals.
    - Ensure proper indexing on the data source.

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Data Analyst Interview Questions 👇

1.How to create filters in Power BI?

Filters are an integral part of Power BI reports. They are used to slice and dice the data as per the dimensions we want. Filters are created in a couple of ways.

Using Slicers: A slicer is a visual under Visualization Pane. This can be added to the design view to filter our reports. When a slicer is added to the design view, it requires a field to be added to it. For example- Slicer can be added for Country fields. Then the data can be filtered based on countries.
Using Filter Pane: The Power BI team has added a filter pane to the reports, which is a single space where we can add different fields as filters. And these fields can be added depending on whether you want to filter only one visual(Visual level filter), or all the visuals in the report page(Page level filters), or applicable to all the pages of the report(report level filters)


2.How to sort data in Power BI?

Sorting is available in multiple formats. In the data view, a common sorting option of alphabetical order is there. Apart from that, we have the option of Sort by column, where one can sort a column based on another column. The sorting option is available in visuals as well. Sort by ascending and descending option by the fields and measure present in the visual is also available.


3.How to convert pdf to excel?

Open the PDF document you want to convert in XLSX format in Acrobat DC.
Go to the right pane and click on the “Export PDF” option.
Choose spreadsheet as the Export format.
Select “Microsoft Excel Workbook.”
Now click “Export.”
Download the converted file or share it.


4. How to enable macros in excel?

Click the file tab and then click “Options.”
A dialog box will appear. In the “Excel Options” dialog box, click on the “Trust Center” and then “Trust Center Settings.”
Go to the “Macro Settings” and select “enable all macros.”
Click OK to apply the macro settings.

📊 Top 10 Data Analytics Concepts Everyone Should Know 🚀

1️⃣ Data Cleaning 🧹
Removing duplicates, fixing missing or inconsistent data.
👉 Tools: Excel, Python (Pandas), SQL

2️⃣ Denoscriptive Statistics 📈
Mean, median, mode, standard deviation—basic measures to summarize data.
👉 Used for understanding data distribution

3️⃣ Data Visualization 📊
Creating charts and dashboards to spot patterns.
👉 Tools: Power BI, Tableau, Matplotlib, Seaborn

4️⃣ Exploratory Data Analysis (EDA) 🔍
Identifying trends, outliers, and correlations through deep data exploration.
👉 Step before modeling

5️⃣ SQL for Data Extraction 🗃️
Querying databases to retrieve specific information.
👉 Focus on SELECT, JOIN, GROUP BY, WHERE

6️⃣ Hypothesis Testing ⚖️
Making decisions using sample data (A/B testing, p-value, confidence intervals).
👉 Useful in product or marketing experiments

7️⃣ Correlation vs Causation 🔗
Just because two things are related doesn’t mean one causes the other!

8️⃣ Data Modeling 🧠
Creating models to predict or explain outcomes.
👉 Linear regression, decision trees, clustering

9️⃣ KPIs & Metrics 🎯
Understanding business performance indicators like ROI, retention rate, churn.

🔟 Storytelling with Data 🗣️

Translating raw numbers into insights stakeholders can act on.
👉 Use clear visuals, simple language, and real-world impact

❤️ React for more

Steps to 𝐆𝐞𝐭 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐂𝐚𝐥𝐥𝐬 from LinkedIn:

1. 𝐀𝐩𝐩𝐥𝐲 𝐃𝐚𝐢𝐥𝐲: Submit applications for 30-40 jobs daily to increase visibility.

2. 𝐃𝐢𝐯𝐞𝐫𝐬𝐢𝐟𝐲 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: Apply for various job types, not just "easy apply" options.

3. 𝐀𝐩𝐩𝐥𝐲 𝐏𝐫𝐨𝐦𝐩𝐭𝐥𝐲: Turn on job alerts and apply as soon as positions are posted.

4. 𝐒𝐞𝐞𝐤 𝐑𝐞𝐟𝐞𝐫𝐫𝐚𝐥𝐬: For dream companies, quickly request referrals from employees. Connect with several people for better chances.

5. 𝐁𝐞 𝐃𝐢𝐫𝐞𝐜𝐭 𝐟𝐨𝐫 𝐑𝐞𝐟𝐞𝐫𝐫𝐚𝐥s: Don't start with "Hi" or "Hello". Send a cold message (short and crisp) with what you need and the job link. If you get a response, you can share your resume for referral. Follow up after one day if needed.

6. 𝐀𝐩𝐩𝐥𝐲 𝐖𝐢𝐭𝐡𝐢𝐧 𝐄𝐥𝐢𝐠𝐢𝐛𝐢𝐥𝐢𝐭𝐲: Only apply or seek referrals for roles where you meet the qualifications (or close enough).

7. 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞 𝐘𝐨𝐮𝐫 𝐏𝐫𝐨𝐟𝐢𝐥𝐞: Build a network of 500+ connections, update experiences, use a professional photo, and list relevant skills.

8. 𝐂𝐨𝐧𝐧𝐞𝐜𝐭 𝐰𝐢𝐭𝐡 𝐑𝐞𝐜𝐫𝐮𝐢𝐭𝐞𝐫𝐬: After applying, connect with job posters and recruiters, and send your CV with a cold message (short and crisp).

9. 𝐄𝐧𝐡𝐚𝐧𝐜𝐞 𝐕𝐢𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲: Keep your profile visible, send connection requests, and share relevant content.

10. 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐞 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐨𝐧 𝐑𝐞𝐪𝐮𝐞𝐬𝐭𝐬: Customize requests to explain your interest.

11. 𝐄𝐧𝐠𝐚𝐠𝐞 𝐰𝐢𝐭𝐡 𝐂𝐨𝐧𝐭𝐞𝐧𝐭: Like, comment, and share posts to stay visible and expand your network.

12. 𝐒𝐡𝐨𝐰𝐜𝐚𝐬𝐞 𝐄𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞: Publish articles or posts about your field to attract potential employers.

13. 𝐉𝐨𝐢𝐧 𝐆𝐫𝐨𝐮𝐩𝐬: Participate in industry-related LinkedIn groups to engage and expand your network.

14. 𝐔𝐩𝐝𝐚𝐭𝐞 𝐇𝐞𝐚𝐝𝐥𝐢𝐧𝐞 𝐚𝐧𝐝 𝐒𝐮𝐦𝐦𝐚𝐫𝐲: Reflect your current role, skills, and aspirations with relevant keywords.

15. 𝐑𝐞𝐪𝐮𝐞𝐬𝐭 𝐑𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬: Get endorsements from colleagues, managers, and clients.

16. 𝐅𝐨𝐥𝐥𝐨𝐰 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬: Stay updated on job openings and company news by following your target companies.

Top 10 Advanced SQL Queries for Data Mastery

1. Recursive CTE (Common Table Expressions)
Use a recursive CTE to traverse hierarchical data, such as employees and their managers.

WITH RECURSIVE EmployeeHierarchy AS (
  SELECT employee_id, employee_name, manager_id
  FROM employees
  WHERE manager_id IS NULL
  UNION ALL
  SELECT e.employee_id, e.employee_name, e.manager_id
  FROM employees e
  JOIN EmployeeHierarchy eh ON e.manager_id = eh.employee_id
)
SELECT * 
FROM EmployeeHierarchy;

2. Pivoting Data
Turn row data into columns (e.g., show product categories as separate columns).

SELECT *
FROM (
    SELECT TO_CHAR(order_date, 'YYYY-MM') AS month, product_category, sales_amount
    FROM sales
) AS pivot_data
PIVOT (
    SUM(sales_amount)
    FOR product_category IN ('Electronics', 'Clothing', 'Books')
) AS pivoted_sales;

3. Window Functions
Calculate a running total of sales based on order date.

SELECT 
  order_date, 
  sales_amount, 
  SUM(sales_amount) OVER (ORDER BY order_date) AS running_total
FROM sales;

4. Ranking with Window Functions
Rank employees’ salaries within each department.

SELECT 
  department, 
  employee_name, 
  salary,
  RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS salary_rank
FROM employees;

5. Finding Gaps in Sequences
Identify missing values in a sequential dataset (e.g., order numbers).

WITH Sequences AS (
  SELECT MIN(order_number) AS start_seq, MAX(order_number) AS end_seq
  FROM orders
)
SELECT start_seq + 1 AS missing_sequence
FROM Sequences
WHERE NOT EXISTS (
  SELECT 1
  FROM orders o
  WHERE o.order_number = Sequences.start_seq + 1
);

6. Unpivoting Data
Convert columns into rows to simplify analysis of multiple attributes.

SELECT 
  product_id, 
  attribute_name, 
  attribute_value
FROM products
UNPIVOT (
  attribute_value FOR attribute_name IN (color, size, weight)
) AS unpivoted_data;

7. Finding Consecutive Events
Check for consecutive days/orders for the same product using LAG().

WITH ConsecutiveOrders AS (
  SELECT 
    product_id, 
    order_date,
    LAG(order_date) OVER (PARTITION BY product_id ORDER BY order_date) AS prev_order_date
  FROM orders
)
SELECT product_id, order_date, prev_order_date
FROM ConsecutiveOrders
WHERE order_date - prev_order_date = 1;

8. Aggregation with the FILTER Clause
Calculate selective averages (e.g., only for the Sales department).

SELECT 
  department,
  AVG(salary) FILTER (WHERE department = 'Sales') AS avg_salary_sales
FROM employees
GROUP BY department;

9. JSON Data Extraction
Extract values from JSON columns directly in SQL.

SELECT 
  order_id, 
  customer_id,
  order_details ->> 'product' AS product_name,
  CAST(order_details ->> 'quantity' AS INTEGER) AS quantity
FROM orders;

10. Using Temporary Tables
Create a temporary table for intermediate results, then join it with other tables.

-- Create a temporary table
CREATE TEMPORARY TABLE temp_product_sales AS
SELECT product_id, SUM(sales_amount) AS total_sales
FROM sales
GROUP BY product_id;

-- Use the temp table
SELECT p.product_name, t.total_sales
FROM products p
JOIN temp_product_sales t ON p.product_id = t.product_id;

Why These Matter
Advanced SQL queries let you handle complex data manipulation and analysis tasks with ease. From traversing hierarchical relationships to reshaping data (pivot/unpivot) and working with JSON, these techniques expand your ability to derive insights from relational databases.

Keep practicing these queries to solidify your SQL expertise and make more data-driven decisions!

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#sql #dataanalyst

SQL Cheatsheet ✅

The Only SQL You Actually Need For Your First Job DataAnalytics

The Learning Trap:
* Complex subqueries
* Advanced CTEs
* Recursive queries
* 100+ tutorials watched
* 0 practical experience

Reality Check:
75% of daily SQL tasks:
* Basic SELECT, FROM, WHERE
* JOINs
* GROUP BY
* ORDER BY
* Simple aggregations
* ROW_NUMBER

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#sql

Complete SQL Topics for Data Analysts 😄👇

1. Introduction to SQL:
- Basic syntax and structure
- Understanding databases and tables

2. Querying Data:
- SELECT statement
- Filtering data using WHERE clause
- Sorting data with ORDER BY

3. Joins:
- INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN
- Combining data from multiple tables

4. Aggregation Functions:
- GROUP BY
- Aggregate functions like COUNT, SUM, AVG, MAX, MIN

5. Subqueries:
- Using subqueries in SELECT, WHERE, and HAVING clauses

6. Data Modification:
- INSERT, UPDATE, DELETE statements
- Transactions and Rollback

7. Data Types and Constraints:
- Understanding various data types (e.g., INT, VARCHAR)
- Using constraints (e.g., PRIMARY KEY, FOREIGN KEY)

8. Indexes:
- Creating and managing indexes for performance optimization

9. Views:
- Creating and using views for simplified querying

10. Stored Procedures and Functions:
- Writing and executing stored procedures
- Creating and using functions

11. Normalization:
- Understanding database normalization concepts

12. Data Import and Export:
- Importing and exporting data using SQL

13. Window Functions:
- ROW_NUMBER(), RANK(), DENSE_RANK(), and others

14. Advanced Filtering:
- Using CASE statements for conditional logic

15. Advanced Join Techniques:
- Self-joins and other advanced join scenarios

16. Analytical Functions:
- LAG(), LEAD(), OVER() for advanced analytics

17. Working with Dates and Times:
- Date and time functions and formatting

18. Performance Tuning:
- Query optimization strategies

19. Security:
- Understanding SQL injection and best practices for security

20. Handling NULL Values:
- Dealing with NULL values in queries

Ensure hands-on practice on these topics to strengthen your SQL skills.

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Master SQL step-by-step! From basics to advanced, here are the key topics you need for a solid SQL foundation. 🚀

1. Foundations:
- Learn basic SQL syntax, including SELECT, FROM, WHERE clauses.
- Understand data types, constraints, and the basic structure of a database.

2. Database Design:
- Study database normalization to ensure efficient data organization.
- Learn about primary keys, foreign keys, and relationships between tables.

3. Queries and Joins:
- Practice writing simple to complex SELECT queries.
- Master different types of joins (INNER, LEFT, RIGHT, FULL) to combine data from multiple tables.

4. Aggregation and Grouping:
- Explore aggregate functions like COUNT, SUM, AVG, MAX, and MIN.
- Understand GROUP BY clause for summarizing data based on specific criteria.

5. Subqueries and Nested Queries:
- Learn how to use subqueries to perform operations within another query.
- Understand the concept of nested queries and their practical applications.

6. Indexing and Optimization:
- Study indexing for enhancing query performance.
- Learn optimization techniques, such as avoiding SELECT * and using appropriate indexes.

7. Transactions and ACID Properties:
- Understand the basics of transactions and their role in maintaining data integrity.
- Explore ACID properties (Atomicity, Consistency, Isolation, Durability) in database management.

8. Views and Stored Procedures:
- Create and use views to simplify complex queries.
- Learn about stored procedures for reusable and efficient query execution.

9. Security and Permissions:
- Understand SQL injection risks and how to prevent them.
- Learn how to manage user permissions and access control.

10. Advanced Topics:
- Explore advanced SQL concepts like window functions, CTEs (Common Table Expressions), and recursive queries.
- Familiarize yourself with database-specific features (e.g., PostgreSQL's JSON functions, MySQL's spatial data types).

11. Real-world Projects:
- Apply your knowledge to real-world scenarios by working on projects.
- Practice with sample databases or create your own to reinforce your skills.

12. Continuous Learning:
- Stay updated on SQL advancements and industry best practices.
- Engage with online communities, forums, and resources for ongoing learning and problem-solving.

Here are some free resources to learn & practice SQL 👇👇

SQL For Data Analysis: https://news.1rj.ru/str/sqlanalyst

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Data Analyst Interview QnA

1. Find avg of salaries department wise from table.

Answer-

SELECT department_id, AVG(salary) AS avg_salary
FROM employees
GROUP BY department_id;

2. What does Filter context in DAX mean?

Answer - Filter context in DAX refers to the subset of data that is actively being used in the calculation of a measure or in the evaluation of an expression. This context is determined by filters on the dashboard items like slicers, visuals, and filters pane which restrict the data being processed.

3. Explain how to implement Row-Level Security (RLS) in Power BI.

Answer - Row-Level Security (RLS) in Power BI can be implemented by:

- Creating roles within the Power BI service.
- Defining DAX expressions that specify the data each role can access.
- Assigning users to these roles either in Power BI or dynamically through AD group membership.

4. Create a dictionary, add elements to it, modify an element, and then print the dictionary in alphabetical order of keys.

Answer -

d = {'apple': 2, 'banana': 5}
d['orange'] = 3  # Add element
d['apple'] = 4   # Modify element
sorted_d = dict(sorted(d.items()))  # Sort dictionary
print(sorted_d)

5. Find and print duplicate values in a list of assorted numbers, along with the number of times each value is repeated.

Answer -

from collections import Counter

numbers = [1, 2, 2, 3, 4, 5, 1, 6, 7, 3, 8, 1]
count = Counter(numbers)
duplicates = {k: v for k, v in count.items() if v > 1}
print(duplicates)

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📚🚀Becoming a successful data analyst requires a blend of technical, analytical, and soft skills. Key competencies for excelling in this role include:

Statistical Analysis: Mastery of statistical concepts such as probability, hypothesis testing, and regression analysis is essential.

Data Manipulation: Proficiency in SQL for data querying and manipulation, along with skills in data cleaning and transformation techniques.

Data Visualization: Ability to create insightful visualizations using tools like Tableau, Power BI, or Python libraries such as Matplotlib and Seaborn.

Programming: Strong programming skills in languages like Python or R, along with knowledge of relevant libraries like Pandas and NumPy.

Machine Learning (optional): Understanding of machine learning principles for predictive modeling and classification tasks.

Database Management: Familiarity with database systems such as MySQL, PostgreSQL, or MongoDB for handling large datasets.
Critical Thinking: Ability to analyze data critically, identify patterns, trends, and outliers.

Business Acumen: Understanding the business context and translating data insights into actionable recommendations.

Communication Skills: Effective communication of findings to non-technical stakeholders through both written and verbal means.

Continuous Learning: Commitment to ongoing learning and staying abreast of new tools, techniques, and industry trends to remain competitive.

By honing these skills and gaining practical experience through projects or internships, individuals can build a robust portfolio for a thriving career in data analysis.

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