Here are some most popular Python libraries for data visualization:

Matplotlib – The most fundamental library for static charts. Best for basic visualizations like line, bar, and scatter plots. Highly customizable but requires more coding.

Seaborn – Built on Matplotlib, it simplifies statistical data visualization with beautiful defaults. Ideal for correlation heatmaps, categorical plots, and distribution analysis.

Plotly – Best for interactive visualizations with zooming, hovering, and real-time updates. Great for dashboards, web applications, and 3D plotting.

Bokeh – Designed for interactive and web-based visualizations. Excellent for handling large datasets, streaming data, and integrating with Flask/Django.

Altair – A declarative library that makes complex statistical plots easy with minimal code. Best for quick and clean data exploration.

For static charts, start with Matplotlib or Seaborn. If you need interactivity, use Plotly or Bokeh. For quick EDA, Altair is a great choice.

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SQL Interview Questions with detailed answers:

9️⃣ What is the difference between UNION and UNION ALL?

Both UNION and UNION ALL are used to combine the results of two or more SELECT queries, but they handle duplicate records differently.

1️⃣ UNION (Removes Duplicates)
Combines result sets and removes duplicate rows automatically.

It performs an implicit DISTINCT operation, which may affect performance.

SELECT employee_id, department_id FROM employees UNION SELECT employee_id, department_id FROM managers; 

2️⃣ UNION ALL (Keeps Duplicates)

Combines result sets without removing duplicates.
Faster than UNION because it doesn’t perform duplicate elimination.

SELECT employee_id, department_id FROM employees UNION ALL SELECT employee_id, department_id FROM managers; 

Key Differences:

UNION removes duplicates, which may cause performance overhead.

UNION ALL keeps all records, making it more efficient.

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SQL Interview Questions with detailed answers:

🔟 How do you calculate a running total in SQL?

A running total (also known as a cumulative sum) is the sum of values up to the current row. You can calculate it using window functions like SUM() OVER().

Using SUM() with OVER() (Best Approach)

SELECT employee_id, salary, SUM(salary) OVER (ORDER BY employee_id) AS running_total FROM employees;

Explanation:
SUM(salary) OVER (ORDER BY employee_id) calculates a cumulative sum.
The ORDER BY employee_id ensures the total is calculated sequentially.

Running Total Partitioned by a Category

To calculate the running total within groups (e.g., per department): 👇

SELECT department_id, employee_id, salary, SUM(salary) OVER (PARTITION BY department_id ORDER BY employee_id) AS running_total FROM employees;

Top 20 SQL Interview Questions

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If you want to Excel at using the most used database language in the world, learn these powerful SQL features:

• Wildcards (%, _) – Flexible pattern matching
• Window Functions – ROW_NUMBER(), RANK(), DENSE_RANK(), LEAD(), LAG()
• Common Table Expressions (CTEs) – WITH for better readability
• Recursive Queries – Handle hierarchical data
• STRING Functions – LEFT(), RIGHT(), LEN(), TRIM(), UPPER(), LOWER()
• Date Functions – DATEDIFF(), DATEADD(), FORMAT()
• Pivot & Unpivot – Transform row data into columns
• Aggregate Functions – SUM(), AVG(), COUNT(), MIN(), MAX()
• Joins & Self Joins – Master INNER, LEFT, RIGHT, FULL, SELF JOIN
• Indexing – Speed up queries with CREATE INDEX

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SQL Interview Questions with detailed answers:

1️⃣1️⃣ How does a self-join work? Give an example.

A self-join is when a table joins with itself. It is useful for comparing rows within the same table, such as finding employees and their managers.

Example:

Find Employee-Manager Relationships

SELECT e1.employee_id AS Employee, e1.name AS Employee_Name, e2.employee_id AS Manager, e2.name AS Manager_Name FROM employees e1 JOIN employees e2 ON e1.manager_id = e2.employee_id; 

Explanation:
e1 represents employees.
e2 represents managers.
The join condition e1.manager_id = e2.employee_id matches employees to their managers.

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If you want to Excel as a Data Analyst and land a high-paying job, master these essential skills:

1️⃣ Data Extraction & Processing:
• SQL – SELECT, JOIN, GROUP BY, CTE, WINDOW FUNCTIONS
• Python/R for Data Analysis – Pandas, NumPy, Matplotlib, Seaborn
• Excel – Pivot Tables, VLOOKUP, XLOOKUP, Power Query

2️⃣ Data Cleaning & Transformation:
• Handling Missing Data – COALESCE(), IFNULL(), DROPNA()
• Data Normalization – Removing duplicates, standardizing formats
• ETL Process – Extract, Transform, Load

3️⃣ Exploratory Data Analysis (EDA):
• Denoscriptive Statistics – Mean, Median, Mode, Variance, Standard Deviation
• Data Visualization – Bar Charts, Line Charts, Heatmaps, Histograms

4️⃣ Business Intelligence & Reporting:
• Power BI & Tableau – Dashboards, DAX, Filters, Drill-through
• Google Data Studio – Interactive reports

5️⃣ Data-Driven Decision Making:
• A/B Testing – Hypothesis testing, P-values
• Forecasting & Trend Analysis – Time Series Analysis
• KPI & Metrics Analysis – ROI, Churn Rate, Customer Segmentation

6️⃣ Data Storytelling & Communication:
• Presentation Skills – Explain insights to non-technical stakeholders
• Dashboard Best Practices – Clean UI, relevant KPIs, interactive visuals

7️⃣ Bonus: Automation & AI Integration
• SQL Query Optimization – Improve query performance
• Python Scripting – Automate repetitive tasks
• ChatGPT & AI Tools – Enhance productivity

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SQL Interview Questions with detailed answers:

1️⃣2️⃣ What is a window function, and how is it different from GROUP BY?

A window function performs calculations across a set of table rows related to the current row, without collapsing the result set like GROUP BY.

Key Differences Between Window Functions and GROUP BY:

1️⃣ Window functions retain all rows, while GROUP BY collapses data into a smaller result set.

2️⃣ Window functions use aggregate functions like SUM(), AVG(), and RANK(), but they do not group data; instead, they compute results for each row individually within a defined window.

3️⃣ GROUP BY does not allow row-wise calculations, whereas window functions can provide rankings, running totals, and moving averages while keeping the original data intact.

4️⃣ Window functions support partitions, meaning they can reset calculations within groups using PARTITION BY. In contrast, GROUP BY always groups the entire dataset based on specified columns.

Example of a Window Function (SUM() Over a Window)

SELECT employee_id, department_id, salary, SUM(salary) OVER (PARTITION BY department_id ORDER BY employee_id) AS running_total FROM employees; 

Here, SUM(salary) is calculated for each department separately, but all rows remain in the result.

Example of GROUP BY (Aggregates Data)

SELECT department_id, SUM(salary) FROM employees GROUP BY department_id; 

In this case, the result shows only one row per department, removing individual employee details.

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Let me start with teaching each topic one by one.

Let's start with SQL first, as it's one of the most important skills.

Topic 1: SQL Basics for Data Analysts

SQL (Structured Query Language) is used to retrieve, manipulate, and analyze data stored in databases.

1️⃣ Understanding Databases & Tables

Databases store structured data in tables.

Tables contain rows (records) and columns (fields).

Each column has a specific data type (INTEGER, VARCHAR, DATE, etc.).

2️⃣ Basic SQL Commands

Let's start with some fundamental queries:

🔹 SELECT – Retrieve Data

SELECT * FROM employees; -- Fetch all columns from 'employees' table SELECT name, salary FROM employees; -- Fetch specific columns 

🔹 WHERE – Filter Data

SELECT * FROM employees WHERE department = 'Sales'; -- Filter by department SELECT * FROM employees WHERE salary > 50000; -- Filter by salary 

🔹 ORDER BY – Sort Data

SELECT * FROM employees ORDER BY salary DESC; -- Sort by salary (highest first) SELECT name, hire_date FROM employees ORDER BY hire_date ASC; -- Sort by hire date (oldest first) 

🔹 LIMIT – Restrict Number of Results

SELECT * FROM employees LIMIT 5; -- Fetch only 5 rows SELECT * FROM employees WHERE department = 'HR' LIMIT 10; -- Fetch first 10 HR employees 

🔹 DISTINCT – Remove Duplicates

SELECT DISTINCT department FROM employees; -- Show unique departments 

Mini Task for You: Try to write an SQL query to fetch the top 3 highest-paid employees from an "employees" table.

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SQL Interview Questions with detailed answers

1️⃣3️⃣ How do you detect and remove duplicate records in SQL?

Detecting Duplicate Records:
To find duplicate rows based on specific columns, use GROUP BY with HAVING COUNT(*) > 1:

SELECT employee_id, department_id, COUNT(*) FROM employees GROUP BY employee_id, department_id HAVING COUNT(*) > 1; 

This retrieves records where the same employee_id and department_id appear more than once.

Removing Duplicates Using ROW_NUMBER():
To delete duplicates while keeping only one occurrence, use ROW_NUMBER():

WITH CTE AS ( SELECT *, ROW_NUMBER() OVER (PARTITION BY employee_id, department_id ORDER BY employee_id) AS row_num FROM employees ) DELETE FROM employees WHERE employee_id IN (SELECT employee_id FROM CTE WHERE row_num > 1); 

Alternative: Deleting Using DISTINCT and a Temp Table
If ROW_NUMBER() is not supported, you can create a temporary table:

CREATE TABLE employees_temp AS SELECT DISTINCT * FROM employees; DROP TABLE employees; ALTER TABLE employees_temp RENAME TO employees; 

This removes duplicates by keeping only distinct records.

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SQL Interview Questions with detailed answers:

1️⃣4️⃣ Explain the difference between EXISTS and IN.

Both EXISTS and IN are used to filter data based on a subquery, but they work differently in terms of performance and execution.

Key Differences Between EXISTS and IN:

1️⃣ EXISTS checks for the existence of rows in a subquery and returns TRUE if at least one row is found. It stops checking once a match is found, making it more efficient for large datasets.
2️⃣ IN checks if a value is present in a list of values returned by a subquery. It evaluates all rows, which can be slower if the subquery returns a large number of results.
3️⃣ EXISTS is preferred for correlated subqueries, where the inner query depends on the outer query.
4️⃣ IN is generally better for small, fixed lists of values but can be inefficient for large subquery results.

Example of EXISTS:

SELECT employee_id, name FROM employees e WHERE EXISTS ( SELECT 1 FROM departments d WHERE d.department_id = e.department_id ); 

Here, EXISTS checks if a matching department_id exists in the departments table and returns TRUE as soon as it finds a match.

Example of IN:

SELECT employee_id, name FROM employees WHERE department_id IN (SELECT department_id FROM departments); 

In this case, IN retrieves all department_id values from the departments table and checks each row in the employees table against this list.

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If you want to Excel at Power BI and become a data visualization pro, master these essential features:

• DAX Functions – SUMX(), CALCULATE(), FILTER(), ALL()
• Power Query – Clean & transform data efficiently
• Data Modeling – Relationships, star & snowflake schemas
• Measures vs. Calculated Columns – When & how to use them
• Time Intelligence – TOTALYTD(), DATESINPERIOD(), PREVIOUSMONTH()
• Custom Visuals – Go beyond default charts
• Drill-Through & Drill-Down – Interactive insights
• Row-Level Security (RLS) – Control data access
• Bookmarks & Tooltips – Enhance dashboard storytelling
• Performance Optimization – Speed up slow reports

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If you want to Excel as a Data Analyst, master these powerful skills:

• SQL Queries – SELECT, JOINs, GROUP BY, CTEs, Window Functions
• Excel Functions – VLOOKUP, XLOOKUP, PIVOT TABLES, POWER QUERY
• Data Cleaning – Handle missing values, duplicates, and inconsistencies
• Python for Data Analysis – Pandas, NumPy, Matplotlib, Seaborn
• Data Visualization – Create dashboards in Power BI/Tableau
• Statistical Analysis – Hypothesis testing, correlation, regression
• ETL Process – Extract, Transform, Load data efficiently
• Business Acumen – Understand industry-specific KPIs
• A/B Testing – Data-driven decision-making
• Storytelling with Data – Present insights effectively

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