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:…
Day 10: Advanced SQL Functions and Window Functions
1. Advanced SQL Functions
These functions enhance data manipulation and analysis.
1. String Functions:
UPPER(), LOWER(): Change case.
CONCAT(): Combine strings.
SUBSTRING(): Extract part of a string.
TRIM(): Remove leading/trailing spaces.
Example:
SELECT CONCAT(FirstName, ' ', LastName) AS FullName
FROM Employees;
2. Date Functions:
NOW(): Current date and time.
DATEADD(): Add intervals to a date.
DATEDIFF(): Difference between dates.
Example:
SELECT DATEDIFF(DAY, HireDate, GETDATE()) AS DaysWorked
FROM Employees;
3. Mathematical Functions:
ROUND(), CEIL(), FLOOR(), ABS(): Perform numerical operations.
Example:
SELECT ROUND(Salary, 2) AS RoundedSalary
FROM Employees;
2. Window Functions
Window functions perform calculations across a set of rows related to the current row, without collapsing rows like aggregate functions.
1. ROW_NUMBER(): Assigns a unique number to each row in a result set.
SELECT Name, Salary, ROW_NUMBER() OVER (ORDER BY Salary DESC) AS RowNum
FROM Employees;
2. RANK(): Assigns a rank to rows, with gaps for ties.
SELECT Name, Salary, RANK() OVER (ORDER BY Salary DESC) AS Rank
FROM Employees;
3. DENSE_RANK(): Similar to RANK() but without gaps.
SELECT Name, Salary, DENSE_RANK() OVER (ORDER BY Salary DESC) AS DenseRank
FROM Employees;
4. NTILE(): Divides rows into a specified number of groups.
SELECT Name, Salary, NTILE(4) OVER (ORDER BY Salary DESC) AS Quartile
FROM Employees;
5. LEAD() and LAG(): Access data from the next or previous row.
SELECT Name, Salary, LEAD(Salary) OVER (ORDER BY Salary) AS NextSalary
FROM Employees;
6. Aggregate with PARTITION BY:
Use PARTITION BY to calculate aggregates within subsets of data.
Example:
SELECT DepartmentID, Name, Salary, SUM(Salary) OVER (PARTITION BY DepartmentID) AS DepartmentTotal
FROM Employees;
Action Steps
1. Practice string, date, and math functions on your dataset.
2. Implement ROW_NUMBER(), RANK(), and PARTITION BY to analyze data.
3. Use LEAD() and LAG() to compare current rows with previous/next rows.
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1. Advanced SQL Functions
These functions enhance data manipulation and analysis.
1. String Functions:
UPPER(), LOWER(): Change case.
CONCAT(): Combine strings.
SUBSTRING(): Extract part of a string.
TRIM(): Remove leading/trailing spaces.
Example:
SELECT CONCAT(FirstName, ' ', LastName) AS FullName
FROM Employees;
2. Date Functions:
NOW(): Current date and time.
DATEADD(): Add intervals to a date.
DATEDIFF(): Difference between dates.
Example:
SELECT DATEDIFF(DAY, HireDate, GETDATE()) AS DaysWorked
FROM Employees;
3. Mathematical Functions:
ROUND(), CEIL(), FLOOR(), ABS(): Perform numerical operations.
Example:
SELECT ROUND(Salary, 2) AS RoundedSalary
FROM Employees;
2. Window Functions
Window functions perform calculations across a set of rows related to the current row, without collapsing rows like aggregate functions.
1. ROW_NUMBER(): Assigns a unique number to each row in a result set.
SELECT Name, Salary, ROW_NUMBER() OVER (ORDER BY Salary DESC) AS RowNum
FROM Employees;
2. RANK(): Assigns a rank to rows, with gaps for ties.
SELECT Name, Salary, RANK() OVER (ORDER BY Salary DESC) AS Rank
FROM Employees;
3. DENSE_RANK(): Similar to RANK() but without gaps.
SELECT Name, Salary, DENSE_RANK() OVER (ORDER BY Salary DESC) AS DenseRank
FROM Employees;
4. NTILE(): Divides rows into a specified number of groups.
SELECT Name, Salary, NTILE(4) OVER (ORDER BY Salary DESC) AS Quartile
FROM Employees;
5. LEAD() and LAG(): Access data from the next or previous row.
SELECT Name, Salary, LEAD(Salary) OVER (ORDER BY Salary) AS NextSalary
FROM Employees;
6. Aggregate with PARTITION BY:
Use PARTITION BY to calculate aggregates within subsets of data.
Example:
SELECT DepartmentID, Name, Salary, SUM(Salary) OVER (PARTITION BY DepartmentID) AS DepartmentTotal
FROM Employees;
Action Steps
1. Practice string, date, and math functions on your dataset.
2. Implement ROW_NUMBER(), RANK(), and PARTITION BY to analyze data.
3. Use LEAD() and LAG() to compare current rows with previous/next rows.
🔝 SQL 30 Days Challenge
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https://365datascience.pxf.io/APy44a
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Data Analytics
Day 10: Advanced SQL Functions and Window Functions 1. Advanced SQL Functions These functions enhance data manipulation and analysis. 1. String Functions: UPPER(), LOWER(): Change case. CONCAT(): Combine strings. SUBSTRING(): Extract part of a string.…
Day 11: Indexes and Performance Optimization
1. What are Indexes?
Indexes improve query performance by allowing the database to find rows more quickly. They act as a data structure that provides a faster way to look up data.
2. Types of Indexes
1. Clustered Index:
Stores data physically in order based on indexed column(s).
Only one per table.
Example: Primary key.
Syntax:
CREATE CLUSTERED INDEX idx_name ON TableName(ColumnName);
2. Non-Clustered Index:
Creates a separate structure for the index while data remains unsorted.
Multiple non-clustered indexes can exist on a table.
Syntax:
CREATE NONCLUSTERED INDEX idx_name ON TableName(ColumnName);
3. Unique Index:
Ensures all values in the indexed column(s) are unique.
Automatically created for PRIMARY KEY and UNIQUE constraints.
Syntax:
CREATE UNIQUE INDEX idx_name ON TableName(ColumnName);
4. Composite Index:
Indexes multiple columns together.
Syntax:
CREATE INDEX idx_name ON TableName(Column1, Column2);
3. Best Practices for Indexing
1. Index columns frequently used in WHERE, JOIN, or ORDER BY.
2. Avoid over-indexing (too many indexes can slow down write operations).
3. Use composite indexes for multi-column searches.
4. Regularly update statistics for accurate query plans.
4. Query Performance Optimization
1. EXPLAIN/Execution Plan:
Use it to analyze query performance and identify bottlenecks.
Syntax:
EXPLAIN SELECT * FROM TableName WHERE Column = 'Value';
2. Avoid SELECT :
Only retrieve required columns to minimize data retrieval.
Example:
SELECT Name, Salary FROM Employees WHERE DepartmentID = 1;
3. Use Joins Efficiently:
Prefer INNER JOIN for better performance if applicable.
4. Optimize WHERE Clauses:
Use indexed columns in WHERE.
Example:
SELECT * FROM Employees WHERE EmployeeID = 101;
5. Avoid Functions in WHERE Clauses:
Functions prevent the use of indexes.
Inefficient:
SELECT * FROM Employees WHERE YEAR(HireDate) = 2023;
Efficient:
SELECT * FROM Employees WHERE HireDate >= '2023-01-01' AND HireDate < '2024-01-01';
6. Use LIMIT/OFFSET:
Reduce the result set size for better performance.
Example:
SELECT * FROM Employees LIMIT 10 OFFSET 0;
5. Dropping Unused Indexes
Too many indexes can slow down write operations. Drop unused ones.
Syntax:
DROP INDEX idx_name ON TableName;
Action Steps
1. Create clustered, non-clustered, and composite indexes on a test table.
2. Use EXPLAIN or execution plans to analyze slow queries.
3. Optimize queries based on the best practices above.
🔝 SQL 30 Days Challenge
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https://365datascience.pxf.io/APy44a
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1. What are Indexes?
Indexes improve query performance by allowing the database to find rows more quickly. They act as a data structure that provides a faster way to look up data.
2. Types of Indexes
1. Clustered Index:
Stores data physically in order based on indexed column(s).
Only one per table.
Example: Primary key.
Syntax:
CREATE CLUSTERED INDEX idx_name ON TableName(ColumnName);
2. Non-Clustered Index:
Creates a separate structure for the index while data remains unsorted.
Multiple non-clustered indexes can exist on a table.
Syntax:
CREATE NONCLUSTERED INDEX idx_name ON TableName(ColumnName);
3. Unique Index:
Ensures all values in the indexed column(s) are unique.
Automatically created for PRIMARY KEY and UNIQUE constraints.
Syntax:
CREATE UNIQUE INDEX idx_name ON TableName(ColumnName);
4. Composite Index:
Indexes multiple columns together.
Syntax:
CREATE INDEX idx_name ON TableName(Column1, Column2);
3. Best Practices for Indexing
1. Index columns frequently used in WHERE, JOIN, or ORDER BY.
2. Avoid over-indexing (too many indexes can slow down write operations).
3. Use composite indexes for multi-column searches.
4. Regularly update statistics for accurate query plans.
4. Query Performance Optimization
1. EXPLAIN/Execution Plan:
Use it to analyze query performance and identify bottlenecks.
Syntax:
EXPLAIN SELECT * FROM TableName WHERE Column = 'Value';
2. Avoid SELECT :
Only retrieve required columns to minimize data retrieval.
Example:
SELECT Name, Salary FROM Employees WHERE DepartmentID = 1;
3. Use Joins Efficiently:
Prefer INNER JOIN for better performance if applicable.
4. Optimize WHERE Clauses:
Use indexed columns in WHERE.
Example:
SELECT * FROM Employees WHERE EmployeeID = 101;
5. Avoid Functions in WHERE Clauses:
Functions prevent the use of indexes.
Inefficient:
SELECT * FROM Employees WHERE YEAR(HireDate) = 2023;
Efficient:
SELECT * FROM Employees WHERE HireDate >= '2023-01-01' AND HireDate < '2024-01-01';
6. Use LIMIT/OFFSET:
Reduce the result set size for better performance.
Example:
SELECT * FROM Employees LIMIT 10 OFFSET 0;
5. Dropping Unused Indexes
Too many indexes can slow down write operations. Drop unused ones.
Syntax:
DROP INDEX idx_name ON TableName;
Action Steps
1. Create clustered, non-clustered, and composite indexes on a test table.
2. Use EXPLAIN or execution plans to analyze slow queries.
3. Optimize queries based on the best practices above.
🔝 SQL 30 Days Challenge
Here you can find SQL Interview Resources👇
https://365datascience.pxf.io/APy44a
Like this post if you want me to continue this SQL series 👍♥️
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Data Analytics
Day 11: Indexes and Performance Optimization 1. What are Indexes? Indexes improve query performance by allowing the database to find rows more quickly. They act as a data structure that provides a faster way to look up data. 2. Types of Indexes 1. Clustered…
Day 12: Transactions and Error Handling
1. What are Transactions?
A transaction is a sequence of SQL operations performed as a single logical unit of work. Transactions ensure data consistency and integrity.
2. ACID Properties of Transactions
1. Atomicity: All operations within the transaction succeed or none do.
2. Consistency: The database remains consistent before and after the transaction.
3. Isolation: Transactions do not interfere with each other.
4. Durability: Once committed, the transaction’s changes are permanent.
3. Transaction Control Statements
1. BEGIN TRANSACTION: Starts a transaction.
BEGIN TRANSACTION;
2. COMMIT: Saves all changes made during the transaction.
COMMIT;
3. ROLLBACK: Undoes all changes made during the transaction.
ROLLBACK;
4. SAVEPOINT: Sets a point within a transaction to roll back to.
SAVEPOINT SavePointName;
5. RELEASE SAVEPOINT: Deletes a savepoint.
RELEASE SAVEPOINT SavePointName;
4. Example of a Transaction
5. Error Handling
1. TRY...CATCH: Handle errors and ensure proper cleanup in case of failure.
Syntax:
2. Example with TRY...CATCH:
3. @@ERROR:
A system function that returns the error code of the last T-SQL statement.
6. Isolation Levels
Control how transactions interact with each other.
1. Read Uncommitted: Allows dirty reads.
2. Read Committed: Prevents dirty reads.
3. Repeatable Read: Prevents non-repeatable reads.
4. Serializable: Prevents dirty, non-repeatable, and phantom reads.
Syntax:
SET TRANSACTION ISOLATION LEVEL SERIALIZABLE;
BEGIN TRANSACTION;
-- SQL operations
COMMIT;
Action Steps
1. Write a transaction with BEGIN TRANSACTION, COMMIT, and ROLLBACK.
2. Implement error handling using TRY...CATCH.
3. Experiment with different isolation levels in test scenarios.
🔝 SQL 30 Days Challenge
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https://365datascience.pxf.io/APy44a
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1. What are Transactions?
A transaction is a sequence of SQL operations performed as a single logical unit of work. Transactions ensure data consistency and integrity.
2. ACID Properties of Transactions
1. Atomicity: All operations within the transaction succeed or none do.
2. Consistency: The database remains consistent before and after the transaction.
3. Isolation: Transactions do not interfere with each other.
4. Durability: Once committed, the transaction’s changes are permanent.
3. Transaction Control Statements
1. BEGIN TRANSACTION: Starts a transaction.
BEGIN TRANSACTION;
2. COMMIT: Saves all changes made during the transaction.
COMMIT;
3. ROLLBACK: Undoes all changes made during the transaction.
ROLLBACK;
4. SAVEPOINT: Sets a point within a transaction to roll back to.
SAVEPOINT SavePointName;
5. RELEASE SAVEPOINT: Deletes a savepoint.
RELEASE SAVEPOINT SavePointName;
4. Example of a Transaction
BEGIN TRANSACTION;
-- Deduct from sender's account
UPDATE Accounts
SET Balance = Balance - 1000
WHERE AccountID = 1;
-- Add to receiver's account
UPDATE Accounts
SET Balance = Balance + 1000
WHERE AccountID = 2;
-- Check for errors
IF @@ERROR <> 0
BEGIN
ROLLBACK;
PRINT 'Transaction Failed';
END
ELSE
BEGIN
COMMIT;
PRINT 'Transaction Successful';
END;
5. Error Handling
1. TRY...CATCH: Handle errors and ensure proper cleanup in case of failure.
Syntax:
BEGIN TRY
-- SQL statements
END TRY
BEGIN CATCH
-- Error handling code
END CATCH
2. Example with TRY...CATCH:
BEGIN TRY
BEGIN TRANSACTION;
-- Insert operation
INSERT INTO Employees (Name, Salary) VALUES ('John', 5000);
-- Error-prone operation
INSERT INTO Employees (Name, Salary) VALUES (NULL, NULL);
COMMIT;
END TRY
BEGIN CATCH
ROLLBACK;
PRINT 'Error occurred: ' + ERROR_MESSAGE();
END CATCH;
3. @@ERROR:
A system function that returns the error code of the last T-SQL statement.
6. Isolation Levels
Control how transactions interact with each other.
1. Read Uncommitted: Allows dirty reads.
2. Read Committed: Prevents dirty reads.
3. Repeatable Read: Prevents non-repeatable reads.
4. Serializable: Prevents dirty, non-repeatable, and phantom reads.
Syntax:
SET TRANSACTION ISOLATION LEVEL SERIALIZABLE;
BEGIN TRANSACTION;
-- SQL operations
COMMIT;
Action Steps
1. Write a transaction with BEGIN TRANSACTION, COMMIT, and ROLLBACK.
2. Implement error handling using TRY...CATCH.
3. Experiment with different isolation levels in test scenarios.
🔝 SQL 30 Days Challenge
Here you can find SQL Interview Resources👇
https://365datascience.pxf.io/APy44a
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Top Tableau Features Every Data Analyst Should Know
Data Connection:
Connect to Multiple Data Sources: Blend data from files, databases, and cloud platforms.
Live vs. Extract: Choose between real-time data updates or working with a snapshot.
Visualizations:
Drag-and-Drop Interface: Quickly create bar charts, line graphs, and heat maps.
Dual-Axis Charts: Compare two measures with separate axes.
Trend Lines: Add statistical trend lines to visuals.
Filters and Parameters:
Interactive Filters: Allow users to filter data dynamically.
Parameters: Let users input values to customize analysis (e.g., thresholds).
Calculated Fields:
Custom Calculations: Create metrics like profit ratios or rolling averages.
Logical Functions: Use IF, CASE, and other functions for custom logic.
Dashboards:
Combine Views: Merge multiple sheets into a single dashboard.
Actions: Add interactivity like filters or URL actions.
Geospatial Analysis:
Map Visualizations: Plot data points on a map using lat-long or names.
Filled Maps: Visualize regions (e.g., countries, states) with color gradients.
Sharing and Publishing:
Tableau Public: Publish visuals for public access.
Tableau Server/Online: Share dashboards securely within an organization.
Best Resources to learn Tableau: https://whatsapp.com/channel/0029VasYW1V5kg6z4EHOHG1t
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Data Connection:
Connect to Multiple Data Sources: Blend data from files, databases, and cloud platforms.
Live vs. Extract: Choose between real-time data updates or working with a snapshot.
Visualizations:
Drag-and-Drop Interface: Quickly create bar charts, line graphs, and heat maps.
Dual-Axis Charts: Compare two measures with separate axes.
Trend Lines: Add statistical trend lines to visuals.
Filters and Parameters:
Interactive Filters: Allow users to filter data dynamically.
Parameters: Let users input values to customize analysis (e.g., thresholds).
Calculated Fields:
Custom Calculations: Create metrics like profit ratios or rolling averages.
Logical Functions: Use IF, CASE, and other functions for custom logic.
Dashboards:
Combine Views: Merge multiple sheets into a single dashboard.
Actions: Add interactivity like filters or URL actions.
Geospatial Analysis:
Map Visualizations: Plot data points on a map using lat-long or names.
Filled Maps: Visualize regions (e.g., countries, states) with color gradients.
Sharing and Publishing:
Tableau Public: Publish visuals for public access.
Tableau Server/Online: Share dashboards securely within an organization.
Best Resources to learn Tableau: https://whatsapp.com/channel/0029VasYW1V5kg6z4EHOHG1t
Like this post if you want me to continue this Tableau series 👍♥️
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👍8❤4
Data Analytics
Day 12: Transactions and Error Handling 1. What are Transactions? A transaction is a sequence of SQL operations performed as a single logical unit of work. Transactions ensure data consistency and integrity. 2. ACID Properties of Transactions 1. Atomicity:…
Day 13: Views, Stored Procedures, and Triggers
1. Views
A view is a virtual table based on a SQL query. It simplifies complex queries and improves data abstraction.
1. Creating a View:
2. Using a View:
SELECT * FROM ViewName;
3. Updating a View:
Views can often be updated if based on a single table and meet certain criteria.
Example:
UPDATE ViewName
SET Column1 = 'NewValue'
WHERE Condition;
4. Dropping a View:
DROP VIEW ViewName;
2. Stored Procedures
A stored procedure is a set of SQL statements stored in the database and executed as a single unit.
1. Creating a Stored Procedure:
2. Executing a Stored Procedure:
EXEC ProcedureName;
3. Stored Procedure with Parameters:
4. Dropping a Stored Procedure:
DROP PROCEDURE ProcedureName;
3. Triggers
Triggers are SQL code automatically executed in response to specific events on a table.
1. Types of Triggers:
AFTER Trigger: Executes after an INSERT, UPDATE, or DELETE operation.
INSTEAD OF Trigger: Replaces the triggering action.
2. Creating an AFTER Trigger:
3. Example: Logging Changes:
4. Dropping a Trigger:
DROP TRIGGER TriggerName;
4. Use Cases
1. Views: Simplify reporting or provide restricted access to data.
2. Stored Procedures: Automate repetitive tasks or enforce business logic.
3. Triggers: Automatically maintain audit trails or enforce rules.
Action Steps
1. Create a view to simplify a complex query.
2. Write a stored procedure to retrieve specific data based on a parameter.
3. Create a trigger to log changes in a table.
🔝 SQL 30 Days Challenge
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https://365datascience.pxf.io/APy44a
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1. Views
A view is a virtual table based on a SQL query. It simplifies complex queries and improves data abstraction.
1. Creating a View:
CREATE VIEW ViewName AS
SELECT Column1, Column2
FROM TableName
WHERE Condition;
2. Using a View:
SELECT * FROM ViewName;
3. Updating a View:
Views can often be updated if based on a single table and meet certain criteria.
Example:
UPDATE ViewName
SET Column1 = 'NewValue'
WHERE Condition;
4. Dropping a View:
DROP VIEW ViewName;
2. Stored Procedures
A stored procedure is a set of SQL statements stored in the database and executed as a single unit.
1. Creating a Stored Procedure:
CREATE PROCEDURE ProcedureName
AS
BEGIN
SELECT * FROM TableName WHERE Condition;
END;
2. Executing a Stored Procedure:
EXEC ProcedureName;
3. Stored Procedure with Parameters:
CREATE PROCEDURE GetEmployeeDetails @EmployeeID INT
AS
BEGIN
SELECT * FROM Employees WHERE EmployeeID = @EmployeeID;
END;
Execution:
EXEC GetEmployeeDetails @EmployeeID = 1;
4. Dropping a Stored Procedure:
DROP PROCEDURE ProcedureName;
3. Triggers
Triggers are SQL code automatically executed in response to specific events on a table.
1. Types of Triggers:
AFTER Trigger: Executes after an INSERT, UPDATE, or DELETE operation.
INSTEAD OF Trigger: Replaces the triggering action.
2. Creating an AFTER Trigger:
CREATE TRIGGER TriggerName
ON TableName
AFTER INSERT, UPDATE, DELETE
AS
BEGIN
PRINT 'Trigger executed';
END;
3. Example: Logging Changes:
CREATE TRIGGER LogChanges
ON Employees
AFTER UPDATE
AS
BEGIN
INSERT INTO AuditLog (EmployeeID, ChangeTime)
SELECT EmployeeID, GETDATE()
FROM Inserted;
END;
4. Dropping a Trigger:
DROP TRIGGER TriggerName;
4. Use Cases
1. Views: Simplify reporting or provide restricted access to data.
2. Stored Procedures: Automate repetitive tasks or enforce business logic.
3. Triggers: Automatically maintain audit trails or enforce rules.
Action Steps
1. Create a view to simplify a complex query.
2. Write a stored procedure to retrieve specific data based on a parameter.
3. Create a trigger to log changes in a table.
🔝 SQL 30 Days Challenge
Here you can find SQL Interview Resources👇
https://365datascience.pxf.io/APy44a
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Cloud-Based Data Analysis Tools
Google BigQuery:
Purpose: Query and analyze large datasets.
Strengths: Scalable, serverless, integrates with Google Cloud.
Amazon Redshift:
Purpose: Data warehousing and analytics.
Strengths: Handles massive datasets with fast query speeds.
Microsoft Azure Synapse Analytics:
Purpose: Integrates big data and data warehousing.
Strengths: Seamless with Power BI and other Azure services.
Snowflake:
Purpose: Cloud data platform for storage and computation.
Strengths: Elastic scalability, easy-to-use SQL interface.
Databricks:
Purpose: Unified analytics for big data and machine learning.
Strengths: Ideal for collaboration and advanced ML workloads.
Tableau Online:
Purpose: Cloud-hosted analytics for sharing visualizations.
Strengths: Real-time dashboards and collaboration.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
t.me/datasimplifier
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Google BigQuery:
Purpose: Query and analyze large datasets.
Strengths: Scalable, serverless, integrates with Google Cloud.
Amazon Redshift:
Purpose: Data warehousing and analytics.
Strengths: Handles massive datasets with fast query speeds.
Microsoft Azure Synapse Analytics:
Purpose: Integrates big data and data warehousing.
Strengths: Seamless with Power BI and other Azure services.
Snowflake:
Purpose: Cloud data platform for storage and computation.
Strengths: Elastic scalability, easy-to-use SQL interface.
Databricks:
Purpose: Unified analytics for big data and machine learning.
Strengths: Ideal for collaboration and advanced ML workloads.
Tableau Online:
Purpose: Cloud-hosted analytics for sharing visualizations.
Strengths: Real-time dashboards and collaboration.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
t.me/datasimplifier
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Data Analytics
Day 13: Views, Stored Procedures, and Triggers 1. Views A view is a virtual table based on a SQL query. It simplifies complex queries and improves data abstraction. 1. Creating a View: CREATE VIEW ViewName AS SELECT Column1, Column2 FROM TableName WHERE…
Day 14: Common Table Expressions (CTEs) and Recursive Queries
1. Common Table Expressions (CTEs)
A Common Table Expression (CTE) is a temporary result set that simplifies complex queries. It exists only during the execution of the query.
2. Syntax of a CTE
3. Example of a CTE
Simple CTE:
4. Recursive CTE
A recursive CTE refers to itself and is commonly used to query hierarchical data like organizational charts or folder structures.
Syntax:
5. Example of a Recursive CTE
Hierarchy of Employees:
6. Key Points to Remember
1. Use CTEs to break down complex queries for better readability.
2. Recursive CTEs must include:
An anchor member (base case).
A recursive member with a termination condition (e.g., ManagerID IS NULL).
3. Recursive queries must include a UNION ALL operator.
7. Benefits of CTEs
1. Improved query readability.
2. Simplifies hierarchical or recursive queries.
3. Can be referenced multiple times within the same query.
Action Steps
1. Write a simple CTE to filter data from a table.
2. Create a recursive CTE to display a hierarchical structure like an organization chart.
3. Test your recursive CTE with a termination condition to avoid infinite loops.
🔝 SQL 30 Days Challenge
Here you can find SQL Interview Resources👇
https://365datascience.pxf.io/APy44a
Like this post if you want me to continue this SQL series 👍♥️
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1. Common Table Expressions (CTEs)
A Common Table Expression (CTE) is a temporary result set that simplifies complex queries. It exists only during the execution of the query.
2. Syntax of a CTE
WITH CTE_Name (Column1, Column2, ...)
AS
(
SELECT Column1, Column2
FROM TableName
WHERE Condition
)
SELECT * FROM CTE_Name;
3. Example of a CTE
Simple CTE:
WITH EmployeeCTE AS
(
SELECT EmployeeID, Name, Salary
FROM Employees
WHERE Salary > 5000
)
SELECT * FROM EmployeeCTE;
4. Recursive CTE
A recursive CTE refers to itself and is commonly used to query hierarchical data like organizational charts or folder structures.
Syntax:
WITH RecursiveCTE (Column1, Column2, ...)
AS
(
-- Anchor member
SELECT Column1, Column2
FROM TableName
WHERE Condition
UNION ALL
-- Recursive member
SELECT Column1, Column2
FROM TableName
INNER JOIN RecursiveCTE
ON TableName.ParentID = RecursiveCTE.ID
)
SELECT * FROM RecursiveCTE;
5. Example of a Recursive CTE
Hierarchy of Employees:
WITH EmployeeHierarchy 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 EmployeeHierarchy eh
ON e.ManagerID = eh.EmployeeID
)
SELECT * FROM EmployeeHierarchy;
6. Key Points to Remember
1. Use CTEs to break down complex queries for better readability.
2. Recursive CTEs must include:
An anchor member (base case).
A recursive member with a termination condition (e.g., ManagerID IS NULL).
3. Recursive queries must include a UNION ALL operator.
7. Benefits of CTEs
1. Improved query readability.
2. Simplifies hierarchical or recursive queries.
3. Can be referenced multiple times within the same query.
Action Steps
1. Write a simple CTE to filter data from a table.
2. Create a recursive CTE to display a hierarchical structure like an organization chart.
3. Test your recursive CTE with a termination condition to avoid infinite loops.
🔝 SQL 30 Days Challenge
Here you can find SQL Interview Resources👇
https://365datascience.pxf.io/APy44a
Like this post if you want me to continue this SQL series 👍♥️
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👍15❤12👏2🎉1
Many people pay too much to learn Python, but my mission is to break down barriers. I have shared complete learning series to learn Python from scratch.
Here are the links to the Python series
Complete Python Topics for Data Analyst: https://news.1rj.ru/str/sqlspecialist/548
Part-1: https://news.1rj.ru/str/sqlspecialist/562
Part-2: https://news.1rj.ru/str/sqlspecialist/564
Part-3: https://news.1rj.ru/str/sqlspecialist/565
Part-4: https://news.1rj.ru/str/sqlspecialist/566
Part-5: https://news.1rj.ru/str/sqlspecialist/568
Part-6: https://news.1rj.ru/str/sqlspecialist/570
Part-7: https://news.1rj.ru/str/sqlspecialist/571
Part-8: https://news.1rj.ru/str/sqlspecialist/572
Part-9: https://news.1rj.ru/str/sqlspecialist/578
Part-10: https://news.1rj.ru/str/sqlspecialist/577
Part-11: https://news.1rj.ru/str/sqlspecialist/578
Part-12:
https://news.1rj.ru/str/sqlspecialist/581
Part-13: https://news.1rj.ru/str/sqlspecialist/583
Part-14: https://news.1rj.ru/str/sqlspecialist/584
Part-15: https://news.1rj.ru/str/sqlspecialist/585
I saw a lot of big influencers copy pasting my content after removing the credits. It's absolutely fine for me as more people are getting free education because of my content.
But I will really appreciate if you share credits for the time and efforts I put in to create such valuable content. I hope you can understand.
Complete SQL Topics for Data Analysts: https://news.1rj.ru/str/sqlspecialist/523
Complete Power BI Topics for Data Analysts: https://news.1rj.ru/str/sqlspecialist/588
Thanks to all who support our channel and share the content with proper credits. You guys are really amazing.
Hope it helps :)
Here are the links to the Python series
Complete Python Topics for Data Analyst: https://news.1rj.ru/str/sqlspecialist/548
Part-1: https://news.1rj.ru/str/sqlspecialist/562
Part-2: https://news.1rj.ru/str/sqlspecialist/564
Part-3: https://news.1rj.ru/str/sqlspecialist/565
Part-4: https://news.1rj.ru/str/sqlspecialist/566
Part-5: https://news.1rj.ru/str/sqlspecialist/568
Part-6: https://news.1rj.ru/str/sqlspecialist/570
Part-7: https://news.1rj.ru/str/sqlspecialist/571
Part-8: https://news.1rj.ru/str/sqlspecialist/572
Part-9: https://news.1rj.ru/str/sqlspecialist/578
Part-10: https://news.1rj.ru/str/sqlspecialist/577
Part-11: https://news.1rj.ru/str/sqlspecialist/578
Part-12:
https://news.1rj.ru/str/sqlspecialist/581
Part-13: https://news.1rj.ru/str/sqlspecialist/583
Part-14: https://news.1rj.ru/str/sqlspecialist/584
Part-15: https://news.1rj.ru/str/sqlspecialist/585
I saw a lot of big influencers copy pasting my content after removing the credits. It's absolutely fine for me as more people are getting free education because of my content.
But I will really appreciate if you share credits for the time and efforts I put in to create such valuable content. I hope you can understand.
Complete SQL Topics for Data Analysts: https://news.1rj.ru/str/sqlspecialist/523
Complete Power BI Topics for Data Analysts: https://news.1rj.ru/str/sqlspecialist/588
Thanks to all who support our channel and share the content with proper credits. You guys are really amazing.
Hope it helps :)
❤20👍19🔥3👎1🥰1🎉1
Data Analytics
Day 14: Common Table Expressions (CTEs) and Recursive Queries 1. Common Table Expressions (CTEs) A Common Table Expression (CTE) is a temporary result set that simplifies complex queries. It exists only during the execution of the query. 2. Syntax of a…
Day 15: Window Functions
1. What are Window Functions?
Window functions perform calculations across a set of rows related to the current row, helping analyze data without grouping.
2. Types of Window Functions
Aggregate Functions: SUM(), AVG(), COUNT(), MIN(), MAX().
Ranking Functions: ROW_NUMBER(), RANK(), DENSE_RANK(), NTILE().
Value Functions: LAG(), LEAD(), FIRST_VALUE(), LAST_VALUE().
3. Syntax
4. Examples
a) Aggregate with PARTITION
Calculate total salary for each department:
In Department 101, if employees earn 5000, 6000, and 4000, the total salary for all rows is 15000.
In Department 102, with only one employee earning 7000, the total salary is 7000.
b) ROW_NUMBER
Assign a unique number to each row based on salary:
If salaries are 7000, 6000, 5000, employees are ranked as 1, 2, and 3 respectively.
c) RANK and DENSE_RANK
Rank employees based on salary:
With salaries 7000, 6000, 6000:
RANK: 1, 2, 2 (skips 3 for ties).
DENSE_RANK: 1, 2, 2 (does not skip numbers for ties).
d) LAG and LEAD
Fetch the previous and next salaries in a sequence:
For salaries 4000, 5000, 6000:
PreviousSalary: NULL, 4000, 5000.
NextSalary: 5000, 6000, NULL.
5. Key Takeaways
PARTITION BY groups data into subsets for calculations.
ORDER BY defines the sequence for calculations.
Use window functions to analyze data efficiently without grouping rows.
Action Steps
- Write a query using SUM() and PARTITION BY to calculate group totals.
- Use ROW_NUMBER to rank rows based on any column.
- Experiment with LAG and LEAD to fetch previous and next row values.
🔝 SQL 30 Days Challenge
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/sqlanalyst
Like this post if you want me to continue this SQL series 👍♥️
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1. What are Window Functions?
Window functions perform calculations across a set of rows related to the current row, helping analyze data without grouping.
2. Types of Window Functions
Aggregate Functions: SUM(), AVG(), COUNT(), MIN(), MAX().
Ranking Functions: ROW_NUMBER(), RANK(), DENSE_RANK(), NTILE().
Value Functions: LAG(), LEAD(), FIRST_VALUE(), LAST_VALUE().
3. Syntax
FunctionName() OVER ( PARTITION BY ColumnName ORDER BY ColumnName ) 4. Examples
a) Aggregate with PARTITION
Calculate total salary for each department:
SELECT EmployeeID, DepartmentID, Salary, SUM(Salary) OVER (PARTITION BY DepartmentID) AS TotalSalary FROM Employees;
In Department 101, if employees earn 5000, 6000, and 4000, the total salary for all rows is 15000.
In Department 102, with only one employee earning 7000, the total salary is 7000.
b) ROW_NUMBER
Assign a unique number to each row based on salary:
SELECT EmployeeID, Name, ROW_NUMBER() OVER (ORDER BY Salary DESC) AS RowNumber FROM Employees;
If salaries are 7000, 6000, 5000, employees are ranked as 1, 2, and 3 respectively.
c) RANK and DENSE_RANK
Rank employees based on salary:
SELECT EmployeeID, Name, Salary, RANK() OVER (ORDER BY Salary DESC) AS Rank, DENSE_RANK() OVER (ORDER BY Salary DESC) AS DenseRank FROM Employees;
With salaries 7000, 6000, 6000:
RANK: 1, 2, 2 (skips 3 for ties).
DENSE_RANK: 1, 2, 2 (does not skip numbers for ties).
d) LAG and LEAD
Fetch the previous and next salaries in a sequence:
SELECT EmployeeID, Name, Salary, LAG(Salary) OVER (ORDER BY Salary) AS PreviousSalary, LEAD(Salary) OVER (ORDER BY Salary) AS NextSalary FROM Employees;
For salaries 4000, 5000, 6000:
PreviousSalary: NULL, 4000, 5000.
NextSalary: 5000, 6000, NULL.
5. Key Takeaways
PARTITION BY groups data into subsets for calculations.
ORDER BY defines the sequence for calculations.
Use window functions to analyze data efficiently without grouping rows.
Action Steps
- Write a query using SUM() and PARTITION BY to calculate group totals.
- Use ROW_NUMBER to rank rows based on any column.
- Experiment with LAG and LEAD to fetch previous and next row values.
🔝 SQL 30 Days Challenge
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/sqlanalyst
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👍15❤9
𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿: You have only 2 minutes to solve this Power BI task.
Retrieve the department name and the highest salary in each department from the 'Employees' table, but only for departments where the highest salary is greater than $70,000.
𝗠𝗲: Challenge accepted!
1️⃣ Add a New Measure: To calculate the highest salary per department, use:
Highest_Salary = CALCULATE(MAX(Employees[Salary]), ALLEXCEPT(Employees, Employees[Department]))
2️⃣ Create a Filtered Table: Next, create a table visual to show only departments with a salary over $70,000. Apply a filter to display departments where:
Highest_Salary > 70000
This solution demonstrates my ability to use DAX measures and filters effectively to meet specific business needs in Power BI.
𝗧𝗶𝗽 𝗳𝗼𝗿 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗝𝗼𝗯 𝗦𝗲𝗲𝗸𝗲𝗿𝘀: Focus on mastering DAX, relationships, and visual-level filters to make your reports more insightful and responsive. It’s about building impactful, user-friendly dashboards, not just complex models!
Interview Resources👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post if you need more 👍❤️
Hope it helps! :)
Retrieve the department name and the highest salary in each department from the 'Employees' table, but only for departments where the highest salary is greater than $70,000.
𝗠𝗲: Challenge accepted!
1️⃣ Add a New Measure: To calculate the highest salary per department, use:
Highest_Salary = CALCULATE(MAX(Employees[Salary]), ALLEXCEPT(Employees, Employees[Department]))
2️⃣ Create a Filtered Table: Next, create a table visual to show only departments with a salary over $70,000. Apply a filter to display departments where:
Highest_Salary > 70000
This solution demonstrates my ability to use DAX measures and filters effectively to meet specific business needs in Power BI.
𝗧𝗶𝗽 𝗳𝗼𝗿 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗝𝗼𝗯 𝗦𝗲𝗲𝗸𝗲𝗿𝘀: Focus on mastering DAX, relationships, and visual-level filters to make your reports more insightful and responsive. It’s about building impactful, user-friendly dashboards, not just complex models!
Interview Resources👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post if you need more 👍❤️
Hope it helps! :)
👍25❤8🔥1
Day 16: Views and Materialized Views
1. What are Views?
A view is a virtual table based on a SELECT query. It does not store data itself but retrieves data from underlying tables when queried.
2. Why Use Views?
1. Simplify complex queries.
2. Enhance security by exposing only specific columns.
3. Maintain consistency across multiple queries.
4. Make queries reusable.
3. Syntax for Creating a View
Example:
Create a view to show employees with a salary greater than 5000:
To query the view:
4. Updating Data via Views
If the view is based on a single table and includes all the necessary primary keys, you can update data through the view:
5. Dropping a View
To remove a view:
DROP VIEW ViewName;
6. Materialized Views
A materialized view stores query results physically, unlike regular views. It is refreshed periodically to reflect changes in the underlying data.
7. Why Use Materialized Views?
1. Improve query performance for complex calculations.
2. Reduce load on the database for frequently used queries.
8. Syntax for Creating a Materialized View
9. Refreshing Materialized Views
Materialized views can be refreshed manually or automatically:
10. Key Differences Between Views and Materialized Views
Views: Always fetch data in real-time from the underlying tables.
Materialized Views: Store data physically and need refreshing to update.
Action Steps
1. Create a view for frequently used queries in your database.
2. Try updating data through a view (if allowed).
3. Experiment with creating and refreshing materialized views.
🔝 SQL 30 Days Challenge
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https://news.1rj.ru/str/sqlanalyst
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1. What are Views?
A view is a virtual table based on a SELECT query. It does not store data itself but retrieves data from underlying tables when queried.
2. Why Use Views?
1. Simplify complex queries.
2. Enhance security by exposing only specific columns.
3. Maintain consistency across multiple queries.
4. Make queries reusable.
3. Syntax for Creating a View
CREATE VIEW ViewName AS
SELECT Column1, Column2
FROM TableName
WHERE Condition;
Example:
Create a view to show employees with a salary greater than 5000:
CREATE VIEW HighSalaryEmployees AS
SELECT EmployeeID, Name, Salary
FROM Employees
WHERE Salary > 5000;
To query the view:
SELECT * FROM HighSalaryEmployees;
4. Updating Data via Views
If the view is based on a single table and includes all the necessary primary keys, you can update data through the view:
UPDATE HighSalaryEmployees
SET Salary = 7000
WHERE EmployeeID = 1;
5. Dropping a View
To remove a view:
DROP VIEW ViewName;
6. Materialized Views
A materialized view stores query results physically, unlike regular views. It is refreshed periodically to reflect changes in the underlying data.
7. Why Use Materialized Views?
1. Improve query performance for complex calculations.
2. Reduce load on the database for frequently used queries.
8. Syntax for Creating a Materialized View
CREATE MATERIALIZED VIEW MaterializedViewName
AS
SELECT Column1, Column2
FROM TableName
WHERE Condition;
9. Refreshing Materialized Views
Materialized views can be refreshed manually or automatically:
-- Manually refresh
REFRESH MATERIALIZED VIEW MaterializedViewName;
10. Key Differences Between Views and Materialized Views
Views: Always fetch data in real-time from the underlying tables.
Materialized Views: Store data physically and need refreshing to update.
Action Steps
1. Create a view for frequently used queries in your database.
2. Try updating data through a view (if allowed).
3. Experiment with creating and refreshing materialized views.
🔝 SQL 30 Days Challenge
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/sqlanalyst
Like this post if you want me to continue this SQL series 👍♥️
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👍16❤10
5 Essential Portfolio Projects for data analysts 😄👇
1. Exploratory Data Analysis (EDA) on a Real Dataset: Choose a dataset related to your interests, perform thorough EDA, visualize trends, and draw insights. This showcases your ability to understand data and derive meaningful conclusions.
Free websites to find datasets: https://news.1rj.ru/str/DataPortfolio/8
2. Predictive Modeling Project: Build a predictive model, such as a linear regression or classification model. Use a dataset to train and test your model, and evaluate its performance. Highlight your skills in machine learning and statistical analysis.
3. Data Cleaning and Transformation: Take a messy dataset and demonstrate your skills in cleaning and transforming data. Showcase your ability to handle missing values, outliers, and prepare data for analysis.
4. Dashboard Creation: Utilize tools like Tableau or Power BI to create an interactive dashboard. This project demonstrates your ability to present data insights in a visually appealing and user-friendly manner.
5. Time Series Analysis: Work with time-series data to forecast future trends. This could involve stock prices, weather data, or any other time-dependent dataset. Showcase your understanding of time-series concepts and forecasting techniques.
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1. Exploratory Data Analysis (EDA) on a Real Dataset: Choose a dataset related to your interests, perform thorough EDA, visualize trends, and draw insights. This showcases your ability to understand data and derive meaningful conclusions.
Free websites to find datasets: https://news.1rj.ru/str/DataPortfolio/8
2. Predictive Modeling Project: Build a predictive model, such as a linear regression or classification model. Use a dataset to train and test your model, and evaluate its performance. Highlight your skills in machine learning and statistical analysis.
3. Data Cleaning and Transformation: Take a messy dataset and demonstrate your skills in cleaning and transforming data. Showcase your ability to handle missing values, outliers, and prepare data for analysis.
4. Dashboard Creation: Utilize tools like Tableau or Power BI to create an interactive dashboard. This project demonstrates your ability to present data insights in a visually appealing and user-friendly manner.
5. Time Series Analysis: Work with time-series data to forecast future trends. This could involve stock prices, weather data, or any other time-dependent dataset. Showcase your understanding of time-series concepts and forecasting techniques.
Share with credits: https://news.1rj.ru/str/sqlspecialist
Like it if you need more posts like this 😄❤️
Hope it helps :)
👍21❤15
Day 17: Indexes
1. What are Indexes?
Indexes are database objects that speed up the retrieval of data from tables. They function like a book’s index, allowing quick access to specific rows.
2. Types of Indexes
Clustered Index:
Alters the physical order of table data.
Each table can have only one clustered index.
Example: Index on a primary key.
Non-Clustered Index:
Does not change the physical order of data but creates a separate structure for quick lookups.
A table can have multiple non-clustered indexes.
Unique Index:
Ensures that values in a column or group of columns are unique.
Composite Index:
An index on multiple columns for queries involving those columns.
3. Why Use Indexes?
Improve query performance for large datasets.
Speed up searches, joins, and filtering.
Enforce uniqueness with unique indexes.
4. Syntax for Creating Indexes
a) Clustered Index
Automatically created when a primary key is defined:
Example:
b) Non-Clustered Index
Example:
c) Unique Index
Example:
5. Viewing Indexes
To list all indexes on a table:
6. Dropping Indexes
To remove an index:
Example:
7. Limitations of Indexes
Slows down INSERT, UPDATE, and DELETE operations due to maintenance.
Requires additional storage.
Too many indexes can degrade performance.
8. Best Practices:
Use indexes for frequently queried columns.
Avoid indexing small tables or columns with low selectivity.
Regularly monitor and optimize index usage.
Action Steps:
Create clustered and non-clustered indexes for common queries in your database.
Check the performance difference using indexed vs non-indexed columns.
Drop unused or redundant indexes.
🔝 SQL 30 Days Challenge
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/sqlanalyst
Like this post if you want me to continue this SQL series 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
1. What are Indexes?
Indexes are database objects that speed up the retrieval of data from tables. They function like a book’s index, allowing quick access to specific rows.
2. Types of Indexes
Clustered Index:
Alters the physical order of table data.
Each table can have only one clustered index.
Example: Index on a primary key.
Non-Clustered Index:
Does not change the physical order of data but creates a separate structure for quick lookups.
A table can have multiple non-clustered indexes.
Unique Index:
Ensures that values in a column or group of columns are unique.
Composite Index:
An index on multiple columns for queries involving those columns.
3. Why Use Indexes?
Improve query performance for large datasets.
Speed up searches, joins, and filtering.
Enforce uniqueness with unique indexes.
4. Syntax for Creating Indexes
a) Clustered Index
Automatically created when a primary key is defined:
CREATE CLUSTERED INDEX IndexName ON TableName (ColumnName); Example:
CREATE CLUSTERED INDEX IDX_EmployeeID ON Employees (EmployeeID);
b) Non-Clustered Index
CREATE NONCLUSTERED INDEX IndexName ON TableName (ColumnName); Example:
CREATE NONCLUSTERED INDEX IDX_Salary ON Employees (Salary);
c) Unique Index
CREATE UNIQUE INDEX IndexName ON TableName (ColumnName); Example:
CREATE UNIQUE INDEX IDX_UniqueEmail ON Employees (Email);
5. Viewing Indexes
To list all indexes on a table:
EXEC sp_helpindex 'TableName'; 6. Dropping Indexes
To remove an index:
DROP INDEX IndexName ON TableName; Example:
DROP INDEX IDX_Salary ON Employees;
7. Limitations of Indexes
Slows down INSERT, UPDATE, and DELETE operations due to maintenance.
Requires additional storage.
Too many indexes can degrade performance.
8. Best Practices:
Use indexes for frequently queried columns.
Avoid indexing small tables or columns with low selectivity.
Regularly monitor and optimize index usage.
Action Steps:
Create clustered and non-clustered indexes for common queries in your database.
Check the performance difference using indexed vs non-indexed columns.
Drop unused or redundant indexes.
🔝 SQL 30 Days Challenge
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/sqlanalyst
Like this post if you want me to continue this SQL series 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
👍21❤7
If you're new to data analytics, start with SQL and Excel. Focus on one at a time, master it, and then move on to the next. Don’t juggle multiple things at once; finish what you start before taking up something new.
SQL Topics:
SELECT statements
WHERE clause
GROUP BY and HAVING
JOINS (INNER, LEFT, RIGHT, FULL)
Aggregation functions (SUM, COUNT, AVG, etc.)
Common Table Expressions (CTEs)
Subqueries
Excel Topics:
Basic formulas (SUM, IF, VLOOKUP, etc.)
Data cleaning techniques
Pivot tables
Conditional formatting
Charts and graphs
Data validation
Advanced features like Power Query and Macros
SQL Learning Series: https://news.1rj.ru/str/sqlspecialist/567
Excel Learning Series: https://news.1rj.ru/str/sqlspecialist/664
Hope it helps :)
SQL Topics:
SELECT statements
WHERE clause
GROUP BY and HAVING
JOINS (INNER, LEFT, RIGHT, FULL)
Aggregation functions (SUM, COUNT, AVG, etc.)
Common Table Expressions (CTEs)
Subqueries
Excel Topics:
Basic formulas (SUM, IF, VLOOKUP, etc.)
Data cleaning techniques
Pivot tables
Conditional formatting
Charts and graphs
Data validation
Advanced features like Power Query and Macros
SQL Learning Series: https://news.1rj.ru/str/sqlspecialist/567
Excel Learning Series: https://news.1rj.ru/str/sqlspecialist/664
Hope it helps :)
👍17❤10🔥2👏1
Data Analytics
Day 17: Indexes 1. What are Indexes? Indexes are database objects that speed up the retrieval of data from tables. They function like a book’s index, allowing quick access to specific rows. 2. Types of Indexes Clustered Index: Alters the physical order…
Day 18: Transactions and ACID Properties
1. What are Transactions?
A transaction is a sequence of operations performed as a single unit of work. It ensures data consistency, even in cases of failure.
2. Key Characteristics of Transactions:
Atomicity: Ensures all operations within the transaction are completed or none at all.
Consistency: Guarantees the database remains in a valid state before and after the transaction.
Isolation: Transactions are independent and do not interfere with each other.
Durability: Once a transaction is committed, the changes are permanent.
3. Syntax for Transactions:
Start a Transaction
Commit a Transaction
Saves the changes made during the transaction:
Rollback a Transaction
Reverts the changes made during the transaction:
4. Example
Without Transactions
If an error occurs, only part of the data may be saved, causing inconsistencies:
With Transactions
Ensures both operations are completed or none:
If either UPDATE fails, the entire transaction is rolled back.
5. Savepoints
Savepoints allow partial rollback within a transaction:
6. Isolation Levels:
Control how transactions interact with each other:
Read Uncommitted: Allows dirty reads (data not yet committed).
Read Committed: Prevents dirty reads (default in most databases).
Repeatable Read: Prevents dirty reads and ensures no changes to data during the transaction.
Serializable: Ensures complete isolation but may reduce performance.
Set the isolation level:
7. Best Practices:
Keep transactions short to reduce locking and improve performance.
Always use COMMIT or ROLLBACK explicitly.
Test for errors within transactions to handle rollbacks.
Use appropriate isolation levels based on requirements.
Action Steps:
Write a transaction to transfer money between two accounts.
Experiment with savepoints for partial rollbacks.
Explore the effect of different isolation levels on concurrent transactions.
🔝 SQL 30 Days Challenge
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/sqlanalyst
Like this post if you want me to continue this SQL series 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
1. What are Transactions?
A transaction is a sequence of operations performed as a single unit of work. It ensures data consistency, even in cases of failure.
2. Key Characteristics of Transactions:
Atomicity: Ensures all operations within the transaction are completed or none at all.
Consistency: Guarantees the database remains in a valid state before and after the transaction.
Isolation: Transactions are independent and do not interfere with each other.
Durability: Once a transaction is committed, the changes are permanent.
3. Syntax for Transactions:
Start a Transaction
BEGIN TRANSACTION; Commit a Transaction
Saves the changes made during the transaction:
COMMIT; Rollback a Transaction
Reverts the changes made during the transaction:
ROLLBACK; 4. Example
Without Transactions
If an error occurs, only part of the data may be saved, causing inconsistencies:
UPDATE Accounts SET Balance = Balance - 500 WHERE AccountID = 1; UPDATE Accounts SET Balance = Balance + 500 WHERE AccountID = 2;
With Transactions
Ensures both operations are completed or none:
BEGIN TRANSACTION; UPDATE Accounts SET Balance = Balance - 500 WHERE AccountID = 1; UPDATE Accounts SET Balance = Balance + 500 WHERE AccountID = 2; IF @@ERROR <> 0 ROLLBACK; ELSE COMMIT;
If either UPDATE fails, the entire transaction is rolled back.
5. Savepoints
Savepoints allow partial rollback within a transaction:
BEGIN TRANSACTION; UPDATE Accounts SET Balance = Balance - 500 WHERE AccountID = 1; SAVE TRANSACTION SavePoint1; UPDATE Accounts SET Balance = Balance + 500 WHERE AccountID = 2; ROLLBACK TRANSACTION SavePoint1; -- Reverts the second update only COMMIT;
6. Isolation Levels:
Control how transactions interact with each other:
Read Uncommitted: Allows dirty reads (data not yet committed).
Read Committed: Prevents dirty reads (default in most databases).
Repeatable Read: Prevents dirty reads and ensures no changes to data during the transaction.
Serializable: Ensures complete isolation but may reduce performance.
Set the isolation level:
SET TRANSACTION ISOLATION LEVEL SERIALIZABLE; BEGIN TRANSACTION;
7. Best Practices:
Keep transactions short to reduce locking and improve performance.
Always use COMMIT or ROLLBACK explicitly.
Test for errors within transactions to handle rollbacks.
Use appropriate isolation levels based on requirements.
Action Steps:
Write a transaction to transfer money between two accounts.
Experiment with savepoints for partial rollbacks.
Explore the effect of different isolation levels on concurrent transactions.
🔝 SQL 30 Days Challenge
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👍13❤10🥰1🎉1
As a data analyst, your primary goal isn’t just to create dashboards, write SQL queries, build pivot tables, generate reports, or clean data. While these are important technical tasks, they are merely tools in your toolkit.
Your real focus should be on solving business problems and providing actionable insights. This means understanding the context of the problem, identifying the right data to analyze, and interpreting the results in a way that adds value to the business. Use these technical skills strategically to answer key questions, identify opportunities, and help drive informed decision-making.
Always remember, the ultimate purpose of your work is to contribute to business growth and efficiency by solving problems, not just completing tasks.
Here you can find entire data analytics roadmap with free resources: https://news.1rj.ru/str/free4unow_backup/902
You can join this channel to find latest data analytics job opportunities: https://news.1rj.ru/str/jobs_SQL
Hope it helps :)
Your real focus should be on solving business problems and providing actionable insights. This means understanding the context of the problem, identifying the right data to analyze, and interpreting the results in a way that adds value to the business. Use these technical skills strategically to answer key questions, identify opportunities, and help drive informed decision-making.
Always remember, the ultimate purpose of your work is to contribute to business growth and efficiency by solving problems, not just completing tasks.
Here you can find entire data analytics roadmap with free resources: https://news.1rj.ru/str/free4unow_backup/902
You can join this channel to find latest data analytics job opportunities: https://news.1rj.ru/str/jobs_SQL
Hope it helps :)
👍11❤10👏2
Data Analytics
Day 18: Transactions and ACID Properties 1. What are Transactions? A transaction is a sequence of operations performed as a single unit of work. It ensures data consistency, even in cases of failure. 2. Key Characteristics of Transactions: Atomicity: Ensures…
Day 19: Stored Procedures
1. What is a Stored Procedure?
A stored procedure is a precompiled set of SQL statements that can be executed with a single call. It helps improve performance, maintainability, and security.
2. Why Use Stored Procedures?
✅ Reduce redundant code.
✅ Improve query performance.
✅ Enhance security by controlling access to direct queries.
✅ Allow parameterized queries for dynamic execution.
3. Syntax for Creating a Stored Procedure
Example:
A procedure to fetch all employees:
4. Stored Procedures with Parameters
Stored procedures can take input and output parameters.
Example:
Procedure to fetch employees based on department ID:
5. Stored Procedure with Output Parameters
Used to return values from a procedure.
Example:
A procedure to count employees in a department:
Call the procedure and get the output value:
6. Modifying and Dropping a Stored Procedure
Modify an existing procedure:
7. Best Practices for Stored Procedures
Use meaningful names for easy identification.
Avoid SELECT ; instead, specify required columns.
Use parameters instead of hardcoded values.
Handle errors using TRY...CATCH.
Action Steps:
Create a stored procedure to insert a new employee into the Employees table.
Write a procedure with an input parameter for filtering records.
Experiment with an output parameter to return calculated values.
🔝 SQL 30 Days Challenge
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https://news.1rj.ru/str/sqlanalyst
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1. What is a Stored Procedure?
A stored procedure is a precompiled set of SQL statements that can be executed with a single call. It helps improve performance, maintainability, and security.
2. Why Use Stored Procedures?
✅ Reduce redundant code.
✅ Improve query performance.
✅ Enhance security by controlling access to direct queries.
✅ Allow parameterized queries for dynamic execution.
3. Syntax for Creating a Stored Procedure
CREATE PROCEDURE ProcedureName AS BEGIN -- SQL statements END;
Example:
A procedure to fetch all employees:
CREATE PROCEDURE GetAllEmployees AS BEGIN SELECT * FROM Employees; END;
Execute the procedure:
EXEC GetAllEmployees;
4. Stored Procedures with Parameters
Stored procedures can take input and output parameters.
Example:
Procedure to fetch employees based on department ID:
CREATE PROCEDURE GetEmployeesByDept @DeptID INT AS BEGIN SELECT * FROM Employees WHERE DepartmentID = @DeptID; END;
Execute with a parameter:
EXEC GetEmployeesByDept @DeptID = 2;
5. Stored Procedure with Output Parameters
Used to return values from a procedure.
Example:
A procedure to count employees in a department:
CREATE PROCEDURE GetEmployeeCountByDept @DeptID INT, @EmpCount INT OUTPUT AS BEGIN SELECT @EmpCount = COUNT(*) FROM Employees WHERE DepartmentID = @DeptID; END;
Call the procedure and get the output value:
DECLARE @Count INT; EXEC GetEmployeeCountByDept @DeptID = 2, @EmpCount = @Count OUTPUT; PRINT @Count;
6. Modifying and Dropping a Stored Procedure
Modify an existing procedure:
ALTER PROCEDURE ProcedureName AS BEGIN
-- Updated SQL statements END;
Drop a stored procedure:
DROP PROCEDURE ProcedureName;
7. Best Practices for Stored Procedures
Use meaningful names for easy identification.
Avoid SELECT ; instead, specify required columns.
Use parameters instead of hardcoded values.
Handle errors using TRY...CATCH.
Action Steps:
Create a stored procedure to insert a new employee into the Employees table.
Write a procedure with an input parameter for filtering records.
Experiment with an output parameter to return calculated values.
🔝 SQL 30 Days Challenge
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/sqlanalyst
Like this post if you want me to continue this SQL series 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
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👍15❤10
Certificates have their own value in proving your skills, but completing a course just for the sake of a certificate won’t help you at all.
What truly matters is how well you understand and apply what you’ve learned.
Whatever course you take, focus on learning, practicing, and mastering the skill rather than just collecting certificates. The real proof of your expertise is in solving real-world problems, not in the number of certificates you have.
That's why I always recommend building data analytics project. Projects help you apply your knowledge, work with real datasets, and tackle challenges similar to what you’d face in a real job.
They also showcase your problem-solving skills, creativity, and ability to draw meaningful insights—things no certificate alone can prove.
Here, you can find free resources to build your own data portfolio
👇👇
https://news.1rj.ru/str/DataPortfolio
Like if you agree ❤️
Hope it helps :)
What truly matters is how well you understand and apply what you’ve learned.
Whatever course you take, focus on learning, practicing, and mastering the skill rather than just collecting certificates. The real proof of your expertise is in solving real-world problems, not in the number of certificates you have.
That's why I always recommend building data analytics project. Projects help you apply your knowledge, work with real datasets, and tackle challenges similar to what you’d face in a real job.
They also showcase your problem-solving skills, creativity, and ability to draw meaningful insights—things no certificate alone can prove.
Here, you can find free resources to build your own data portfolio
👇👇
https://news.1rj.ru/str/DataPortfolio
Like if you agree ❤️
Hope it helps :)
❤16👍10👏2
Data Analytics
Day 19: Stored Procedures 1. What is a Stored Procedure? A stored procedure is a precompiled set of SQL statements that can be executed with a single call. It helps improve performance, maintainability, and security. 2. Why Use Stored Procedures? ✅ Reduce…
Day 20: Triggers
1. What is a Trigger?
A trigger is a special type of stored procedure that automatically executes in response to specific events on a table, such as INSERT, UPDATE, or DELETE.
2. Types of Triggers:
AFTER Trigger (a.k.a. FOR Trigger)
– Executes after the triggering event.
INSTEAD OF Trigger – Replaces the default action of an INSERT, UPDATE, or DELETE.
3. AFTER Trigger Example:
Triggers after a row is inserted into the Employees table.
After execution, the message "A new employee record has been inserted!" appears.
4. INSTEAD OF Trigger Example
Prevents deleting employees from the Employees table but logs the request.
5. Viewing & Dropping Triggers
List triggers on a table:
6. Best Practices for Triggers:
✅ Keep triggers lightweight to avoid performance issues.
✅ Use triggers only when necessary (consider stored procedures for flexibility).
✅ Avoid recursive triggers (where a trigger fires another trigger).
✅ Log actions to track unwanted modifications.
Action Steps:
Create an AFTER INSERT trigger to log new entries into an audit table.
Create an INSTEAD OF UPDATE trigger to prevent salary updates above a certain limit.
Experiment with retrieving deleted records using the deleted table inside a trigger.
🔝 SQL 30 Days Challenge
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/sqlanalyst
Like this post if you want me to continue this SQL series 👍♥️
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1. What is a Trigger?
A trigger is a special type of stored procedure that automatically executes in response to specific events on a table, such as INSERT, UPDATE, or DELETE.
2. Types of Triggers:
AFTER Trigger (a.k.a. FOR Trigger)
– Executes after the triggering event.
INSTEAD OF Trigger – Replaces the default action of an INSERT, UPDATE, or DELETE.
3. AFTER Trigger Example:
Triggers after a row is inserted into the Employees table.
CREATE TRIGGER trg_AfterInsert ON Employees AFTER INSERT AS BEGIN PRINT 'A new employee record has been inserted!'; END;
Test the trigger:
INSERT INTO Employees (EmployeeID, Name, Department) VALUES (101, 'John Doe', 'IT');
After execution, the message "A new employee record has been inserted!" appears.
4. INSTEAD OF Trigger Example
Prevents deleting employees from the Employees table but logs the request.
CREATE TRIGGER trg_InsteadOfDelete ON Employees INSTEAD OF DELETE AS BEGIN PRINT 'Delete operation blocked. Logging attempt...'; INSERT INTO DeleteLogs (EmployeeID, DeleteTime) SELECT EmployeeID, GETDATE() FROM deleted; END;
Test the trigger:
DELETE FROM Employees WHERE EmployeeID = 101;
Instead of deleting, it logs the deletion attempt.
5. Viewing & Dropping Triggers
List triggers on a table:
SELECT name FROM sys.triggers WHERE parent_id = OBJECT_ID('Employees');
Drop a trigger:
DROP TRIGGER trg_AfterInsert; 6. Best Practices for Triggers:
✅ Keep triggers lightweight to avoid performance issues.
✅ Use triggers only when necessary (consider stored procedures for flexibility).
✅ Avoid recursive triggers (where a trigger fires another trigger).
✅ Log actions to track unwanted modifications.
Action Steps:
Create an AFTER INSERT trigger to log new entries into an audit table.
Create an INSTEAD OF UPDATE trigger to prevent salary updates above a certain limit.
Experiment with retrieving deleted records using the deleted table inside a trigger.
🔝 SQL 30 Days Challenge
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/sqlanalyst
Like this post if you want me to continue this SQL series 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤15👍6
Python Learning Plan in 2025
|-- Week 1: Introduction to Python
| |-- Python Basics
| | |-- What is Python?
| | |-- Installing Python
| | |-- Introduction to IDEs (Jupyter, VS Code)
| |-- Setting up Python Environment
| | |-- Anaconda Setup
| | |-- Virtual Environments
| | |-- Basic Syntax and Data Types
| |-- First Python Program
| | |-- Writing and Running Python Scripts
| | |-- Basic Input/Output
| | |-- Simple Calculations
|
|-- Week 2: Core Python Concepts
| |-- Control Structures
| | |-- Conditional Statements (if, elif, else)
| | |-- Loops (for, while)
| | |-- Comprehensions
| |-- Functions
| | |-- Defining Functions
| | |-- Function Arguments and Return Values
| | |-- Lambda Functions
| |-- Modules and Packages
| | |-- Importing Modules
| | |-- Standard Library Overview
| | |-- Creating and Using Packages
|
|-- Week 3: Advanced Python Concepts
| |-- Data Structures
| | |-- Lists, Tuples, and Sets
| | |-- Dictionaries
| | |-- Collections Module
| |-- File Handling
| | |-- Reading and Writing Files
| | |-- Working with CSV and JSON
| | |-- Context Managers
| |-- Error Handling
| | |-- Exceptions
| | |-- Try, Except, Finally
| | |-- Custom Exceptions
|
|-- Week 4: Object-Oriented Programming
| |-- OOP Basics
| | |-- Classes and Objects
| | |-- Attributes and Methods
| | |-- Inheritance
| |-- Advanced OOP
| | |-- Polymorphism
| | |-- Encapsulation
| | |-- Magic Methods and Operator Overloading
| |-- Design Patterns
| | |-- Singleton
| | |-- Factory
| | |-- Observer
|
|-- Week 5: Python for Data Analysis
| |-- NumPy
| | |-- Arrays and Vectorization
| | |-- Indexing and Slicing
| | |-- Mathematical Operations
| |-- Pandas
| | |-- DataFrames and Series
| | |-- Data Cleaning and Manipulation
| | |-- Merging and Joining Data
| |-- Matplotlib and Seaborn
| | |-- Basic Plotting
| | |-- Advanced Visualizations
| | |-- Customizing Plots
|
|-- Week 6-8: Specialized Python Libraries
| |-- Web Development
| | |-- Flask Basics
| | |-- Django Basics
| |-- Data Science and Machine Learning
| | |-- Scikit-Learn
| | |-- TensorFlow and Keras
| |-- Automation and Scripting
| | |-- Automating Tasks with Python
| | |-- Web Scraping with BeautifulSoup and Scrapy
| |-- APIs and RESTful Services
| | |-- Working with REST APIs
| | |-- Building APIs with Flask/Django
|
|-- Week 9-11: Real-world Applications and Projects
| |-- Capstone Project
| | |-- Project Planning
| | |-- Data Collection and Preparation
| | |-- Building and Optimizing Models
| | |-- Creating and Publishing Reports
| |-- Case Studies
| | |-- Business Use Cases
| | |-- Industry-specific Solutions
| |-- Integration with Other Tools
| | |-- Python and SQL
| | |-- Python and Excel
| | |-- Python and Power BI
|
|-- Week 12: Post-Project Learning
| |-- Python for Automation
| | |-- Automating Daily Tasks
| | |-- Scripting with Python
| |-- Advanced Python Topics
| | |-- Asyncio and Concurrency
| | |-- Advanced Data Structures
| |-- Continuing Education
| | |-- Advanced Python Techniques
| | |-- Community and Forums
| | |-- Keeping Up with Updates
|
|-- Resources and Community
| |-- Online Courses (Coursera, edX, Udemy)
| |-- Books (Automate the Boring Stuff, Python Crash Course)
| |-- Python Blogs and Podcasts
| |-- GitHub Repositories
| |-- Python Communities (Reddit, Stack Overflow)
Here you can find essential Python Interview Resources👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more resources like this 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
|-- Week 1: Introduction to Python
| |-- Python Basics
| | |-- What is Python?
| | |-- Installing Python
| | |-- Introduction to IDEs (Jupyter, VS Code)
| |-- Setting up Python Environment
| | |-- Anaconda Setup
| | |-- Virtual Environments
| | |-- Basic Syntax and Data Types
| |-- First Python Program
| | |-- Writing and Running Python Scripts
| | |-- Basic Input/Output
| | |-- Simple Calculations
|
|-- Week 2: Core Python Concepts
| |-- Control Structures
| | |-- Conditional Statements (if, elif, else)
| | |-- Loops (for, while)
| | |-- Comprehensions
| |-- Functions
| | |-- Defining Functions
| | |-- Function Arguments and Return Values
| | |-- Lambda Functions
| |-- Modules and Packages
| | |-- Importing Modules
| | |-- Standard Library Overview
| | |-- Creating and Using Packages
|
|-- Week 3: Advanced Python Concepts
| |-- Data Structures
| | |-- Lists, Tuples, and Sets
| | |-- Dictionaries
| | |-- Collections Module
| |-- File Handling
| | |-- Reading and Writing Files
| | |-- Working with CSV and JSON
| | |-- Context Managers
| |-- Error Handling
| | |-- Exceptions
| | |-- Try, Except, Finally
| | |-- Custom Exceptions
|
|-- Week 4: Object-Oriented Programming
| |-- OOP Basics
| | |-- Classes and Objects
| | |-- Attributes and Methods
| | |-- Inheritance
| |-- Advanced OOP
| | |-- Polymorphism
| | |-- Encapsulation
| | |-- Magic Methods and Operator Overloading
| |-- Design Patterns
| | |-- Singleton
| | |-- Factory
| | |-- Observer
|
|-- Week 5: Python for Data Analysis
| |-- NumPy
| | |-- Arrays and Vectorization
| | |-- Indexing and Slicing
| | |-- Mathematical Operations
| |-- Pandas
| | |-- DataFrames and Series
| | |-- Data Cleaning and Manipulation
| | |-- Merging and Joining Data
| |-- Matplotlib and Seaborn
| | |-- Basic Plotting
| | |-- Advanced Visualizations
| | |-- Customizing Plots
|
|-- Week 6-8: Specialized Python Libraries
| |-- Web Development
| | |-- Flask Basics
| | |-- Django Basics
| |-- Data Science and Machine Learning
| | |-- Scikit-Learn
| | |-- TensorFlow and Keras
| |-- Automation and Scripting
| | |-- Automating Tasks with Python
| | |-- Web Scraping with BeautifulSoup and Scrapy
| |-- APIs and RESTful Services
| | |-- Working with REST APIs
| | |-- Building APIs with Flask/Django
|
|-- Week 9-11: Real-world Applications and Projects
| |-- Capstone Project
| | |-- Project Planning
| | |-- Data Collection and Preparation
| | |-- Building and Optimizing Models
| | |-- Creating and Publishing Reports
| |-- Case Studies
| | |-- Business Use Cases
| | |-- Industry-specific Solutions
| |-- Integration with Other Tools
| | |-- Python and SQL
| | |-- Python and Excel
| | |-- Python and Power BI
|
|-- Week 12: Post-Project Learning
| |-- Python for Automation
| | |-- Automating Daily Tasks
| | |-- Scripting with Python
| |-- Advanced Python Topics
| | |-- Asyncio and Concurrency
| | |-- Advanced Data Structures
| |-- Continuing Education
| | |-- Advanced Python Techniques
| | |-- Community and Forums
| | |-- Keeping Up with Updates
|
|-- Resources and Community
| |-- Online Courses (Coursera, edX, Udemy)
| |-- Books (Automate the Boring Stuff, Python Crash Course)
| |-- Python Blogs and Podcasts
| |-- GitHub Repositories
| |-- Python Communities (Reddit, Stack Overflow)
Here you can find essential Python Interview Resources👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like this post for more resources like this 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
👍34❤13
Data Analytics
Day 20: Triggers 1. What is a Trigger? A trigger is a special type of stored procedure that automatically executes in response to specific events on a table, such as INSERT, UPDATE, or DELETE. 2. Types of Triggers: AFTER Trigger (a.k.a. FOR Trigger) …
Day 21: Review Week 3 Topics & Complex SQL Challenges
📌 Topics to Review from Week 3
Window Functions – (ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG).
Stored Procedures – Creating, executing, and using parameters.
Triggers – AFTER and INSTEAD OF triggers.
Views – Creating, modifying, and using indexed views.
Transactions & ACID Properties – Ensuring data consistency.
📝 Complex SQL Challenges
1️⃣ Challenge: Find the Second Highest Salary (Without Using LIMIT or TOP)
You have an Employees table. Write a query to find the second highest salary.
2️⃣ Challenge: Get Consecutive Login Streaks
Given a Logins table with UserID and LoginDate, find users who logged in for three consecutive days.
3️⃣ Challenge: Rank Employees by Salary Within Each Department
✅ Action Plan for Today
Review Week 3 Topics – Revisit notes, practice stored procedures, and triggers.
Solve These Complex Challenges – Try modifying them for different cases.
Ask Yourself:
What happens if two employees have the same second-highest salary?
How would you handle ties in ranking employees?
Can you optimize these queries for better performance?
🔝 SQL 30 Days Challenge
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/sqlanalyst
Like this post if you want me to continue this SQL series 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
📌 Topics to Review from Week 3
Window Functions – (ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG).
Stored Procedures – Creating, executing, and using parameters.
Triggers – AFTER and INSTEAD OF triggers.
Views – Creating, modifying, and using indexed views.
Transactions & ACID Properties – Ensuring data consistency.
📝 Complex SQL Challenges
1️⃣ Challenge: Find the Second Highest Salary (Without Using LIMIT or TOP)
You have an Employees table. Write a query to find the second highest salary.
SELECT MAX(Salary) AS SecondHighestSalary FROM Employees WHERE Salary < (SELECT MAX(Salary) FROM Employees);
2️⃣ Challenge: Get Consecutive Login Streaks
Given a Logins table with UserID and LoginDate, find users who logged in for three consecutive days.
SELECT DISTINCT L1.UserID FROM Logins L1 JOIN Logins L2 ON L1.UserID = L2.UserID AND L1.LoginDate = L2.LoginDate - 1 JOIN Logins L3 ON L1.UserID = L3.UserID AND L1.LoginDate = L3.LoginDate - 2;
3️⃣ Challenge: Rank Employees by Salary Within Each Department
SELECT EmployeeID, Name, Department, Salary, RANK() OVER (PARTITION BY Department ORDER BY Salary DESC) AS SalaryRank FROM Employees;
✅ Action Plan for Today
Review Week 3 Topics – Revisit notes, practice stored procedures, and triggers.
Solve These Complex Challenges – Try modifying them for different cases.
Ask Yourself:
What happens if two employees have the same second-highest salary?
How would you handle ties in ranking employees?
Can you optimize these queries for better performance?
🔝 SQL 30 Days Challenge
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/sqlanalyst
Like this post if you want me to continue this SQL series 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
👍21👏1