Data Analytics

Day 9: Subqueries and Common Table Expressions (CTEs) 1. Subqueries A subquery is a query nested inside another query. It can be used in SELECT, FROM, or WHERE clauses. Types of Subqueries: 1. Single-row Subquery: Returns one row. 2. Multi-row Subquery: Returns multiple rows. 3. Correlated Subquery: Depends on the outer query. Example 1: Single-row Subquery

-- Find employees earning more than the average salary
SELECT Name, Salary
FROM Employees
WHERE Salary > (SELECT AVG(Salary) FROM Employees);

Example 2: Multi-row Subquery

-- Find employees in specific departments
SELECT Name
FROM Employees
WHERE DepartmentID IN (SELECT DepartmentID FROM Departments WHERE Location = 'NY');

Example 3: Correlated Subquery

-- Find employees with the highest salary in each department
SELECT Name, Salary
FROM Employees E1
WHERE Salary = (
    SELECT MAX(Salary)
    FROM Employees E2
    WHERE E1.DepartmentID = E2.DepartmentID
);

2. Common Table Expressions (CTEs) CTEs provide a way to define temporary result sets that can be reused in the main query. Syntax: WITH CTEName AS ( SELECT Column1, Column2 FROM TableName WHERE Condition ) SELECT * FROM CTEName; Example 1: Simple CTE

-- Get total salary per department
WITH DepartmentSalary AS (
    SELECT DepartmentID, SUM(Salary) AS TotalSalary
    FROM Employees
    GROUP BY DepartmentID
)
SELECT *
FROM DepartmentSalary;

Example 2: Recursive CTE Used for hierarchical data (e.g., organizational structures).

WITH RecursiveCTE AS (
    -- Anchor member
    SELECT EmployeeID, ManagerID, Name
    FROM Employees
    WHERE ManagerID IS NULL
    UNION ALL
    -- Recursive member
    SELECT E.EmployeeID, E.ManagerID, E.Name
    FROM Employees E
    INNER JOIN RecursiveCTE R
    ON E.ManagerID = R.EmployeeID
)
SELECT *
FROM RecursiveCTE;

Action Steps 1. Practice writing subqueries in WHERE, SELECT, and FROM. 2. Use a CTE to simplify complex queries. 3. Create a recursive CTE for hierarchical data if applicable. 🔝 SQL 30 Days Challenge Here you can find SQL Interview Resources👇 https://topmate.io/analyst/864764 Like this post if you want me to continue this SQL series 👍♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

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Day 8: Working with Joins 1. INNER JOIN Returns rows with matching values in both tables. Syntax: SELECT Table1.Column1, Table2.Column2 FROM Table1 INNER JOIN Table2 ON Table1.CommonColumn = Table2.CommonColumn; Example:

SELECT Employees.Name, Departments.DepartmentName
FROM Employees
INNER JOIN Departments
ON Employees.DepartmentID = Departments.DepartmentID;

2. LEFT JOIN (or LEFT OUTER JOIN) Returns all rows from the left table and matching rows from the right table. Non-matching rows in the right table return NULL. Example:

SELECT Employees.Name, Departments.DepartmentName
FROM Employees
LEFT JOIN Departments
ON Employees.DepartmentID = Departments.DepartmentID;

3. RIGHT JOIN (or RIGHT OUTER JOIN) Returns all rows from the right table and matching rows from the left table. Non-matching rows in the left table return NULL. Example:

SELECT Employees.Name, Departments.DepartmentName
FROM Employees
RIGHT JOIN Departments
ON Employees.DepartmentID = Departments.DepartmentID;

4. FULL JOIN (or FULL OUTER JOIN) Returns all rows when there is a match in either table. Rows without matches return NULL. Example:

SELECT Employees.Name, Departments.DepartmentName
FROM Employees
FULL JOIN Departments
ON Employees.DepartmentID = Departments.DepartmentID;

5. SELF JOIN A SELF JOIN is a table joined with itself, useful for hierarchical or relationship data. Syntax: SELECT A.Column1, B.Column2 FROM TableName A INNER JOIN TableName B ON A.CommonColumn = B.CommonColumn; Example:

-- Find employees with the same manager
SELECT A.Name AS Employee, B.Name AS Manager
FROM Employees A
INNER JOIN Employees B
ON A.ManagerID = B.EmployeeID;

6. CROSS JOIN Combines each row from the first table with all rows from the second table, creating a Cartesian product. Syntax: SELECT * FROM Table1 CROSS JOIN Table2; Example:

SELECT Employees.Name, Projects.ProjectName
FROM Employees
CROSS JOIN Projects;

Combining Joins with Filters Use WHERE or ON to refine join results. Example:

-- Employees without departments
SELECT Employees.Name, Departments.DepartmentName
FROM Employees
LEFT JOIN Departments
ON Employees.DepartmentID = Departments.DepartmentID
WHERE Departments.DepartmentName IS NULL;

Action Steps 1. Practice SELF JOIN for hierarchical relationships like managers and employees. 2. Use CROSS JOIN to generate all possible combinations of two tables. 3. Review and compare the results of all join types in your dataset.

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Day 7: Grouping Data with GROUP BY and Filtering with HAVING 1. Using GROUP BY The GROUP BY statement groups rows sharing a common value, often used with aggregate functions. Syntax: SELECT Column1, AggregateFunction(Column2) FROM TableName GROUP BY Column1; Example:

-- Total salary per department
SELECT Department, SUM(Salary) AS TotalSalary
FROM Employees
GROUP BY Department;

2. Filtering Groups with HAVING Use HAVING to filter groups created by GROUP BY. Similar to WHERE, but for aggregated data. Example:

-- Departments with total salary > 100000
SELECT Department, SUM(Salary) AS TotalSalary
FROM Employees
GROUP BY Department
HAVING SUM(Salary) > 100000;

3. Combining WHERE, GROUP BY, and HAVING Use WHERE to filter rows before grouping. Use HAVING to filter groups after aggregation. Example:

-- Total salary of IT department with salary > 40000
SELECT Department, SUM(Salary) AS TotalSalary
FROM Employees
WHERE Salary > 40000
GROUP BY Department
HAVING SUM(Salary) > 100000;

4. Sorting Groups with ORDER BY Sort grouped data using ORDER BY. Example:

-- Sort by total salary (descending)
SELECT Department, SUM(Salary) AS TotalSalary
FROM Employees
GROUP BY Department
ORDER BY TotalSalary DESC;

Action Steps 1. Group data by a column (e.g., department) and use aggregate functions. 2. Filter groups using HAVING. 3. Sort grouped data with ORDER BY. These are very useful SQL concepts, so I would recommend you solve problems related to GROUP BY & HAVING from leetcode or Stratascrach today itself. Start with easy ones and increase difficulty level as you proceed. 🔝 SQL 30 Days Challenge Here you can find SQL Interview Resources👇 https://topmate.io/analyst/864764 Like this post if you want me to continue this SQL series 👍♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

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Day 6: Aggregating Data with Functions (SUM, AVG, MIN, MAX, COUNT) 1. SUM: Calculate the Total The SUM() function adds up all numeric values in a column. Example:

SELECT SUM(Salary) AS TotalSalary
FROM Employees;

2. AVG: Calculate the Average The AVG() function calculates the average value of a numeric column. Example:

SELECT AVG(Salary) AS AverageSalary
FROM Employees;

3. MIN and MAX: Find the Lowest and Highest Values MIN() finds the smallest value. MAX() finds the largest value. Examples:

-- Lowest salary
SELECT MIN(Salary) AS LowestSalary
FROM Employees;

-- Highest salary
SELECT MAX(Salary) AS HighestSalary
FROM Employees;

4. COUNT: Count Rows The COUNT() function counts the number of rows. Examples:

-- Count all rows
SELECT COUNT(*) AS TotalEmployees
FROM Employees;

-- Count employees in a specific department
SELECT COUNT(*) AS TotalITEmployees
FROM Employees
WHERE Department = 'IT';

5. Combining Aggregates You can use multiple aggregate functions in one query. Example:

SELECT 
    COUNT(*) AS TotalEmployees,
    AVG(Salary) AS AverageSalary,
    MAX(Salary) AS HighestSalary
FROM Employees;

Action Steps 1. Find the total, average, minimum, and maximum salaries in your table. 2. Count rows based on specific conditions (e.g., employees in a department). 3. Combine multiple aggregates in a single query. 🔝 SQL 30 Days Challenge Here you can find SQL Interview Resources👇 https://topmate.io/analyst/864764 Like this post if you want me to continue this SQL series 👍♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

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Day 5: Filtering Data with WHERE, LIKE, IN, and BETWEEN 1. Using WHERE for Filtering The WHERE clause filters rows based on specific conditions. Example:

SELECT * FROM Employees
WHERE Department = 'IT';

2. Using LIKE for Pattern Matching Use LIKE with wildcards to match patterns: %: Matches zero or more characters. _: Matches a single character. Examples:

-- Names starting with 'J'
SELECT * FROM Employees
WHERE Name LIKE 'J%';

-- Names ending with 'n'
SELECT * FROM Employees
WHERE Name LIKE '%n';

-- Names with 'a' as the second character
SELECT * FROM Employees
WHERE Name LIKE '_a%';

3. Using IN for Specific Values Use IN to filter rows matching a list of values. Example:

SELECT * FROM Employees
WHERE Department IN ('IT', 'Finance');

4. Using BETWEEN for Ranges Use BETWEEN to filter data within a range (inclusive). Examples:

-- Salaries between 40000 and 60000
SELECT * FROM Employees
WHERE Salary BETWEEN 40000 AND 60000;

-- Hire dates in 2023
SELECT * FROM Employees
WHERE HireDate BETWEEN '2023-01-01' AND '2023-12-31';

Combining Conditions with AND & OR Example:

WHERE Department = 'IT' AND Salary > 50000;

```SELECT * FROM Employees WHERE Department = 'HR' OR Salary < 40000;` Action Steps 1. Retrieve rows using LIKE to match patterns in a column. 2. Filter rows using IN and BETWEEN. 3. Combine conditions with AND and OR. 🔝 SQL 30 Days Challenge Here you can find SQL Interview Resources👇 https://topmate.io/analyst/864764 Like this post if you want me to continue this SQL series 👍♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

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How to Build an Impressive Data Analysis Portfolio As a data analyst, your portfolio is your personal brand. It showcases not only your technical skills but also your ability to solve real-world problems. Having a strong, well-rounded portfolio can set you apart from other candidates and help you land your next job or freelance project. Here's how to build a portfolio that will impress potential employers or clients. 1. Start with a Strong Introduction: Before jumping into your projects, introduce yourself with a brief summary. Include your background, areas of expertise (e.g., Python, R, SQL), and any special achievements or certifications. This is your chance to give context to your portfolio and show your personality. Tip: Make your introduction engaging and concise. Add a professional photo and link to your LinkedIn or personal website. 2. Showcase Real-World Projects: The most powerful way to showcase your skills is through real-world projects. If you don’t have work experience yet, create your own projects using publicly available datasets (e.g., Kaggle, UCI Machine Learning Repository). These projects should highlight the full data analysis process—from data collection and cleaning to analysis and visualization. Examples of project ideas: - Analyzing customer data to identify purchasing trends. - Predicting stock market trends based on historical data. - Analyzing social media sentiment around a brand or event. 3. Focus on Impactful Data Visualizations: Data visualization is a key part of data analysis, and it’s crucial that your portfolio highlights your ability to tell stories with data. Use tools like Tableau, Power BI, or Python (matplotlib, Seaborn) to create compelling visualizations that make complex data easy to understand. Tips for great visuals: - Use color wisely to highlight key insights. - Avoid clutter; focus on clarity. - Create interactive dashboards that allow users to explore the data. 4. Explain Your Methodology: Employers and clients will want to know how you approached each project. For each project in your portfolio, explain the methodology you used, including: - The problem or question you aimed to solve. - The data sources you used. - The tools and techniques you applied (e.g., statistical tests, machine learning models). - The insights or results you discovered. Make sure to document this in a clear, step-by-step manner, ideally with code snippets or screenshots. 5. Include Code and Jupyter Notebooks: If possible, include links to your code or Jupyter Notebooks so potential employers or clients can see your technical expertise firsthand. Platforms like GitHub or GitLab are perfect for hosting your code. Make sure your code is well-commented and easy to follow. Tip: Organize your projects in a structured way on GitHub, using descriptive README files for each project. 6. Feature a Blog or Case Studies: If you enjoy writing, consider adding a blog or case study section to your portfolio. Writing about the data analysis process and the insights you’ve uncovered helps demonstrate your ability to communicate complex ideas in a digestible way. It also allows you to reflect on your projects and show your thought leadership in the field. Blog post ideas: - A breakdown of a data analysis project you’ve completed. - Tips for aspiring data analysts. - Reviews of tools and technologies you use regularly. 7. Continuously Update Your Portfolio: Your portfolio is a living document. As you gain more experience and complete new projects, regularly update it to keep it fresh and relevant. Always add new skills, projects, and certifications to reflect your growth as a data analyst. I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://topmate.io/analyst/861634 Like this post for more content like this 👍♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)

Day 4: Updating and Deleting Data Updating Multiple Columns You can update more than one column at a time. Example:

UPDATE Employees
SET Department = 'Finance', Salary = 60000
WHERE EmployeeID = 2;

Deleting All Rows To delete all rows without removing the table structure, skip the WHERE clause. Example: DELETE FROM Employees; Truncating Data If you need to quickly remove all rows while resetting the auto-increment counters, use TRUNCATE. Example: TRUNCATE TABLE Employees; Action Steps 1. Update a column value (e.g., increase all salaries by 10%).

UPDATE Employees
SET Salary = Salary * 1.1;

2. Delete a specific row based on a condition. 3. Optionally, practice truncating your table (use carefully!). SQL 30 Days Challenge Here you can find SQL Interview Resources👇 https://topmate.io/analyst/864764 Like this post if you want me to continue this SQL series 👍♥️ Share with credits: https://t.me/sqlspecialist Hope it helps :)