Data Analytics
I enjoyed connecting with you all. Thanks everyone for the kind words, it really motivates me to post more content in the future ❤️
Special thanks to RJ for appreciating the efforts. Here are some resources which may help you with storytelling 👇👇
👍9❤2
Don't know why but somehow telegram stopped showing our channel in searches, I would really appreciate if you guys can share our channel link with your friends and loved ones who want to enter into data analytics domain 👇
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Thanks again ❤️
https://news.1rj.ru/str/sqlspecialist
Thanks again ❤️
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You can find data analyst job & internship opportunities on this WhatsApp channel 👇👇
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How to Become a Data Analyst from Scratch! 🚀
Whether you're starting fresh or upskilling, here's your roadmap:
➜ Master Excel and SQL - solve SQL problems from leetcode & hackerank
➜ Get the hang of either Power BI or Tableau - do some hands-on projects
➜ learn what the heck ATS is and how to get around it
➜ learn to be ready for any interview question
➜ Build projects for a data portfolio
➜ And you don't need to do it all at once!
➜ Fail and learn to pick yourself up whenever required
Whether it's acing interviews or building an impressive portfolio, give yourself the space to learn, fail, and grow. Good things take time ✅
You can find the detailed article here
Like if it helps ❤️
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://news.1rj.ru/str/DataSimplifier
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
Whether you're starting fresh or upskilling, here's your roadmap:
➜ Master Excel and SQL - solve SQL problems from leetcode & hackerank
➜ Get the hang of either Power BI or Tableau - do some hands-on projects
➜ learn what the heck ATS is and how to get around it
➜ learn to be ready for any interview question
➜ Build projects for a data portfolio
➜ And you don't need to do it all at once!
➜ Fail and learn to pick yourself up whenever required
Whether it's acing interviews or building an impressive portfolio, give yourself the space to learn, fail, and grow. Good things take time ✅
You can find the detailed article here
Like if it helps ❤️
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://news.1rj.ru/str/DataSimplifier
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
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Data Analytics
What SQL topic do you find the most challenging?
Let's go through the next topic today
How to use Subqueries Effectively
Subqueries are incredibly useful when you need to perform a query within a query. However, they can sometimes be challenging to use efficiently. Here’s how to master subqueries for cleaner and more powerful SQL queries:
Types of Subqueries:
Scalar Subqueries: These return a single value and are often used in SELECT or WHERE clauses.
Row Subqueries: These return one row and are used with IN or EXISTS.
Table Subqueries: These return multiple rows and columns and can be used in the FROM clause as a derived table.
Use Cases: Subqueries are great for breaking complex logic into smaller, more manageable pieces. Common use cases include filtering records based on aggregate results or comparing data between two tables without using a JOIN.
Performance Considerations: While subqueries are powerful, they can sometimes be slower than JOINs, especially when nested multiple times. Consider using JOINs or Common Table Expressions (CTEs) as alternatives for performance optimization.
Avoid Correlated Subqueries: Correlated subqueries reference columns from the outer query, which means the subquery runs repeatedly for each row in the outer query. This can be inefficient for large datasets. Use them only when necessary, and always check performance.
Example:
In this example, the subquery retrieves customer IDs that placed orders after a specific date. The outer query uses this subquery to filter the list of customers.
Alternative with JOIN:
While subqueries are useful, a JOIN can sometimes be more efficient. The query above could be rewritten as a JOIN:
Choose Wisely: Always consider whether a subquery or a JOIN makes more sense for the specific problem. JOINs are typically faster for larger datasets, but subqueries can be more readable in some cases.
When working with subqueries, always test their performance, especially if they are nested within other queries or return large result sets. Consider using indexing to improve speed where possible.
Writing Complex Joins
Optimise Complex SQL Queries
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
How to use Subqueries Effectively
Subqueries are incredibly useful when you need to perform a query within a query. However, they can sometimes be challenging to use efficiently. Here’s how to master subqueries for cleaner and more powerful SQL queries:
Types of Subqueries:
Scalar Subqueries: These return a single value and are often used in SELECT or WHERE clauses.
Row Subqueries: These return one row and are used with IN or EXISTS.
Table Subqueries: These return multiple rows and columns and can be used in the FROM clause as a derived table.
Use Cases: Subqueries are great for breaking complex logic into smaller, more manageable pieces. Common use cases include filtering records based on aggregate results or comparing data between two tables without using a JOIN.
Performance Considerations: While subqueries are powerful, they can sometimes be slower than JOINs, especially when nested multiple times. Consider using JOINs or Common Table Expressions (CTEs) as alternatives for performance optimization.
Avoid Correlated Subqueries: Correlated subqueries reference columns from the outer query, which means the subquery runs repeatedly for each row in the outer query. This can be inefficient for large datasets. Use them only when necessary, and always check performance.
Example:
SELECT customer_id, customer_name
FROM customers
WHERE customer_id IN (
SELECT customer_id
FROM orders
WHERE order_date > '2023-01-01'
);
In this example, the subquery retrieves customer IDs that placed orders after a specific date. The outer query uses this subquery to filter the list of customers.
Alternative with JOIN:
While subqueries are useful, a JOIN can sometimes be more efficient. The query above could be rewritten as a JOIN:
SELECT DISTINCT customers.customer_id, customers.customer_name
FROM customers
JOIN orders ON customers.customer_id = orders.customer_id
WHERE orders.order_date > '2023-01-01';
Choose Wisely: Always consider whether a subquery or a JOIN makes more sense for the specific problem. JOINs are typically faster for larger datasets, but subqueries can be more readable in some cases.
When working with subqueries, always test their performance, especially if they are nested within other queries or return large result sets. Consider using indexing to improve speed where possible.
Writing Complex Joins
Optimise Complex SQL Queries
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
👍25❤7👏2
Data Analytics
Let's go through the next topic today How to use Subqueries Effectively Subqueries are incredibly useful when you need to perform a query within a query. However, they can sometimes be challenging to use efficiently. Here’s how to master subqueries for…
Today, let's go through next challenging SQL topic:
Working with Window Functions
Window functions are a powerful SQL tool for performing calculations across a set of table rows related to the current row. Unlike aggregate functions, which collapse rows into a single value, window functions keep individual rows while allowing you to calculate running totals, rankings, and more. Here’s how you can use them effectively:
Syntax Overview: Window functions use the OVER() clause, which defines how the rows are partitioned and ordered. A typical window function looks like this:
Key Use Cases:
Rankings and Row Numbers: Use functions like RANK(), ROW_NUMBER(), and DENSE_RANK() to rank data while preserving individual rows.
Running Totals: Use SUM() with a window to compute cumulative totals over a partition of rows.
Moving Averages: Use AVG() with a window to calculate averages over a specific range of rows (e.g., for trend analysis).
Lag and Lead: These functions allow you to access data from previous or subsequent rows without using self-joins.
PARTITION BY vs. ORDER BY:
PARTITION BY works like a GROUP BY clause, dividing the data into segments before applying the window function.
ORDER BY specifies how the rows within each partition are ordered for the window function calculation.
Common Window Functions:
ROW_NUMBER(): Assigns a unique number to each row in the result set.
RANK(): Assigns a rank to each row with gaps between tied ranks.
DENSE_RANK(): Similar to RANK(), but without gaps between ranks.
SUM(), AVG(): Can be used to calculate running totals or averages.
Example: Cumulative Total
In this query, we calculate a cumulative total of salaries as we move down the list of employees ordered by employee_id. The SUM() function calculates the running total without collapsing rows.
Example: Ranking Employees by Salary
Here, the RANK() function assigns a rank to each employee based on their salary, with the highest-paid employee getting a rank of 1.
Window functions are highly flexible and can replace more complex queries involving JOINs and GROUP BY. When working with large datasets, make sure to test performance, as window functions can be computationally intensive.
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
Working with Window Functions
Window functions are a powerful SQL tool for performing calculations across a set of table rows related to the current row. Unlike aggregate functions, which collapse rows into a single value, window functions keep individual rows while allowing you to calculate running totals, rankings, and more. Here’s how you can use them effectively:
Syntax Overview: Window functions use the OVER() clause, which defines how the rows are partitioned and ordered. A typical window function looks like this:
SELECT column_name,
window_function() OVER (PARTITION BY column_name ORDER BY column_name) AS alias
FROM table_name;
Key Use Cases:
Rankings and Row Numbers: Use functions like RANK(), ROW_NUMBER(), and DENSE_RANK() to rank data while preserving individual rows.
Running Totals: Use SUM() with a window to compute cumulative totals over a partition of rows.
Moving Averages: Use AVG() with a window to calculate averages over a specific range of rows (e.g., for trend analysis).
Lag and Lead: These functions allow you to access data from previous or subsequent rows without using self-joins.
PARTITION BY vs. ORDER BY:
PARTITION BY works like a GROUP BY clause, dividing the data into segments before applying the window function.
ORDER BY specifies how the rows within each partition are ordered for the window function calculation.
Common Window Functions:
ROW_NUMBER(): Assigns a unique number to each row in the result set.
RANK(): Assigns a rank to each row with gaps between tied ranks.
DENSE_RANK(): Similar to RANK(), but without gaps between ranks.
SUM(), AVG(): Can be used to calculate running totals or averages.
Example: Cumulative Total
SELECT
employee_id,
salary,
SUM(salary) OVER (ORDER BY employee_id) AS cumulative_salary
FROM employees;
In this query, we calculate a cumulative total of salaries as we move down the list of employees ordered by employee_id. The SUM() function calculates the running total without collapsing rows.
Example: Ranking Employees by Salary
SELECT
employee_id,
salary,
RANK() OVER (ORDER BY salary DESC) AS salary_rank
FROM employees;
Here, the RANK() function assigns a rank to each employee based on their salary, with the highest-paid employee getting a rank of 1.
Window functions are highly flexible and can replace more complex queries involving JOINs and GROUP BY. When working with large datasets, make sure to test performance, as window functions can be computationally intensive.
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
👍19❤6
Data Analytics
What SQL topic do you find the most challenging?
Today, let's explore next Advanced SQL Topic
Writing Stored Procedures and Functions
Stored procedures and functions are essential in SQL when you want to automate repetitive tasks, enhance security, and improve performance by reducing client-server interactions. Here’s how to use them effectively:
Stored Procedures: A stored procedure is a set of SQL statements that you can execute repeatedly. You can pass parameters to a stored procedure, which makes it versatile for tasks like updating records or generating reports.
Use Cases:
Automating tasks like daily data imports or backups.
Performing complex data transformations.
Enforcing business rules with reusable logic.
Syntax:
CREATE PROCEDURE procedure_name (parameters)
BEGIN
-- SQL statements
END;
Example:
This procedure updates an employee's salary based on their ID.
Functions: Functions are similar to stored procedures but are used to return a value. They’re typically used for computations and can be used in queries like regular expressions.
Use Cases:
Returning computed values, such as calculating total sales or tax.
Custom transformations or data validations.
Syntax:
Example:
In this example, the function returns 10% of an employee's salary as their bonus.
Key Differences Between Procedures and Functions:
Return Values: Procedures do not have to return a value, whereas functions must return a value.
Usage in Queries: Functions can be called from within a SELECT statement, while stored procedures cannot.
Transaction Management: Stored procedures can manage transactions (BEGIN, COMMIT, ROLLBACK), whereas functions cannot.
Performance Benefits:
Reduced Network Traffic: Since the logic is stored on the server, stored procedures reduce the need for multiple round-trips between the client and server.
Execution Plans: Stored procedures benefit from precompiled execution plans, which can improve performance on frequently executed queries.
Example: Using a Function in a Query
In this query, the custom function GetEmployeeBonus() is used to calculate a bonus for each employee based on their salary.
Use stored procedures and functions when you need reusable, secure, and efficient ways to handle complex logic and repetitive tasks in your database.
Writing Complex Joins
Optimise Complex SQL Queries
How to use Subqueries in SQL
Working with window functions
Like for more ❤️
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
Writing Stored Procedures and Functions
Stored procedures and functions are essential in SQL when you want to automate repetitive tasks, enhance security, and improve performance by reducing client-server interactions. Here’s how to use them effectively:
Stored Procedures: A stored procedure is a set of SQL statements that you can execute repeatedly. You can pass parameters to a stored procedure, which makes it versatile for tasks like updating records or generating reports.
Use Cases:
Automating tasks like daily data imports or backups.
Performing complex data transformations.
Enforcing business rules with reusable logic.
Syntax:
CREATE PROCEDURE procedure_name (parameters)
BEGIN
-- SQL statements
END;
Example:
CREATE PROCEDURE UpdateEmployeeSalary (IN employee_id INT, IN new_salary DECIMAL(10, 2))
BEGIN
UPDATE employees
SET salary = new_salary
WHERE id = employee_id;
END;
This procedure updates an employee's salary based on their ID.
Functions: Functions are similar to stored procedures but are used to return a value. They’re typically used for computations and can be used in queries like regular expressions.
Use Cases:
Returning computed values, such as calculating total sales or tax.
Custom transformations or data validations.
Syntax:
CREATE FUNCTION function_name (parameters)
RETURNS return_type
BEGIN
-- SQL statements
RETURN value;
END;
Example:
CREATE FUNCTION GetEmployeeBonus (salary DECIMAL(10, 2))
RETURNS DECIMAL(10, 2)
BEGIN
RETURN salary * 0.10;
END;
In this example, the function returns 10% of an employee's salary as their bonus.
Key Differences Between Procedures and Functions:
Return Values: Procedures do not have to return a value, whereas functions must return a value.
Usage in Queries: Functions can be called from within a SELECT statement, while stored procedures cannot.
Transaction Management: Stored procedures can manage transactions (BEGIN, COMMIT, ROLLBACK), whereas functions cannot.
Performance Benefits:
Reduced Network Traffic: Since the logic is stored on the server, stored procedures reduce the need for multiple round-trips between the client and server.
Execution Plans: Stored procedures benefit from precompiled execution plans, which can improve performance on frequently executed queries.
Example: Using a Function in a Query
SELECT
employee_id,
salary,
GetEmployeeBonus(salary) AS bonus
FROM employees;
In this query, the custom function GetEmployeeBonus() is used to calculate a bonus for each employee based on their salary.
Use stored procedures and functions when you need reusable, secure, and efficient ways to handle complex logic and repetitive tasks in your database.
Writing Complex Joins
Optimise Complex SQL Queries
How to use Subqueries in SQL
Working with window functions
Like for more ❤️
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
👍21❤8🥰4
Hi guys,
Many people charge too much to teach Excel, Power BI, SQL, Python & Tableau but my mission is to break down barriers. I have shared complete learning series to start your data analytics journey from scratch.
For those of you who are new to this channel, here are some quick links to navigate this channel easily.
Data Analyst Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/752
Python Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/749
Power BI Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/745
SQL Learning Plan 👇
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SQL Learning Series 👇
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Excel Learning Series 👇
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Power BI Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/768
Python Learning Series 👇
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Tableau Essential Topics 👇
https://news.1rj.ru/str/sqlspecialist/667
Best Data Analytics Resources 👇
https://heylink.me/DataAnalytics
You can find more resources on Medium & Linkedin
Like for more ❤️
Thanks to all who support our channel and share it with friends & loved ones. You guys are really amazing.
Hope it helps :)
Many people charge too much to teach Excel, Power BI, SQL, Python & Tableau but my mission is to break down barriers. I have shared complete learning series to start your data analytics journey from scratch.
For those of you who are new to this channel, here are some quick links to navigate this channel easily.
Data Analyst Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/752
Python Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/749
Power BI Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/745
SQL Learning Plan 👇
https://news.1rj.ru/str/sqlspecialist/738
SQL Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/567
Excel Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/664
Power BI Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/768
Python Learning Series 👇
https://news.1rj.ru/str/sqlspecialist/615
Tableau Essential Topics 👇
https://news.1rj.ru/str/sqlspecialist/667
Best Data Analytics Resources 👇
https://heylink.me/DataAnalytics
You can find more resources on Medium & Linkedin
Like for more ❤️
Thanks to all who support our channel and share it with friends & loved ones. You guys are really amazing.
Hope it helps :)
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This Telegram channel is a hidden gem for anyone seeking job opportunities in data analytics
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I usually don’t go out of my way to recommend channels, but this one is truly worth it. Whether you're on the hunt for data analyst jobs or need interview tips, this channel has everything you need.
Hope it helps :)
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I usually don’t go out of my way to recommend channels, but this one is truly worth it. Whether you're on the hunt for data analyst jobs or need interview tips, this channel has everything you need.
Hope it helps :)
❤19👍13
Commit and master 𝗦𝗤𝗟 in just 𝟯𝟬 𝗗𝗮𝘆𝘀!
I've outlined a simple, actionable plan for you to follow…
𝗪𝗲𝗲𝗸 𝟭: 𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗦𝗤𝗟
➛ Day 1-2: Introduction to SQL, setting up your environment (MySQL/PostgreSQL/SQL Server).
➛ Day 3-4: Understanding databases, tables, and basic SQL syntax.
➛ Day 5-7: Working with SELECT, WHERE, and filtering data.
𝗪𝗲𝗲𝗸 𝟮: 𝗖𝗼𝗿𝗲 𝗤𝘂𝗲𝗿𝗶𝗲𝘀
➛ Day 8-10: Using JOINs – INNER, LEFT, RIGHT, FULL.
➛ Day 11-13: GROUP BY, HAVING, and aggregate functions (SUM, COUNT, AVG).
➛ Day 14: Practice session – write complex queries.
𝗪𝗲𝗲𝗸 𝟯: 𝗠𝗼𝗱𝗶𝗳𝘆𝗶𝗻𝗴 𝗗𝗮𝘁𝗮
➛ Day 15-17: INSERT, UPDATE, DELETE – altering your data.
➛ Day 18-20: Subqueries, nested queries, and derived tables.
➛ Day 21: Practice session – work on a mini-project.
𝗪𝗲𝗲𝗸 𝟰: 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗤𝗟 𝗧𝗼𝗽𝗶𝗰𝘀 𝗮𝗻𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝘁
➛ Day 22-24: Window functions, RANK, DENSE_RANK, ROW_NUMBER.
➛ Day 25-27: Creating and managing indexes, views, and stored procedures.
➛ Day 28-30: Capstone project – work with real-world data to design and query a database.
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Like this post if you need more 👍❤️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
I've outlined a simple, actionable plan for you to follow…
𝗪𝗲𝗲𝗸 𝟭: 𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗦𝗤𝗟
➛ Day 1-2: Introduction to SQL, setting up your environment (MySQL/PostgreSQL/SQL Server).
➛ Day 3-4: Understanding databases, tables, and basic SQL syntax.
➛ Day 5-7: Working with SELECT, WHERE, and filtering data.
𝗪𝗲𝗲𝗸 𝟮: 𝗖𝗼𝗿𝗲 𝗤𝘂𝗲𝗿𝗶𝗲𝘀
➛ Day 8-10: Using JOINs – INNER, LEFT, RIGHT, FULL.
➛ Day 11-13: GROUP BY, HAVING, and aggregate functions (SUM, COUNT, AVG).
➛ Day 14: Practice session – write complex queries.
𝗪𝗲𝗲𝗸 𝟯: 𝗠𝗼𝗱𝗶𝗳𝘆𝗶𝗻𝗴 𝗗𝗮𝘁𝗮
➛ Day 15-17: INSERT, UPDATE, DELETE – altering your data.
➛ Day 18-20: Subqueries, nested queries, and derived tables.
➛ Day 21: Practice session – work on a mini-project.
𝗪𝗲𝗲𝗸 𝟰: 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗤𝗟 𝗧𝗼𝗽𝗶𝗰𝘀 𝗮𝗻𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝘁
➛ Day 22-24: Window functions, RANK, DENSE_RANK, ROW_NUMBER.
➛ Day 25-27: Creating and managing indexes, views, and stored procedures.
➛ Day 28-30: Capstone project – work with real-world data to design and query a database.
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Like this post if you need more 👍❤️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
👍40❤22
Master 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 in just 𝟯𝟬 𝗗𝗮𝘆𝘀 and boost your data skills!
Here's a clear, step-by-step plan for you…
𝗪𝗲𝗲𝗸 𝟭: 𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜
➛ Day 1-2: Introduction to Power BI, installation, and understanding the interface.
➛ Day 3-4: Connecting to data sources and importing data.
➛ Day 5-7: Data cleaning and transforming using Power Query Editor.
𝗪𝗲𝗲𝗸 𝟮: 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴
➛ Day 8-10: Creating relationships between tables.
➛ Day 11-13: DAX basics – Calculated columns, measures, and key functions like SUM, COUNT.
➛ Day 14: Practice building a simple data model.
𝗪𝗲𝗲𝗸 𝟯: 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻
➛ Day 15-17: Building visualizations – bar charts, pie charts, and line graphs.
➛ Day 18-20: Using slicers, filters, and drill-through to create interactive reports.
➛ Day 21: Design a dashboard – bringing everything together.
𝗪𝗲𝗲𝗸 𝟰: 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗧𝗼𝗽𝗶𝗰𝘀 𝗮𝗻𝗱 𝗖𝗮𝗽𝘀𝘁𝗼𝗻𝗲
➛ Day 22-24: Advanced DAX – Time intelligence, IF statements, and nested functions.
➛ Day 25-27: Publishing to Power BI Service, sharing, and setting up scheduled refresh.
➛ Day 28-30: Capstone project – Build a full Power BI report from real data, complete with interactive visuals and insights.
You can refer these Power BI Interview Resources to learn more: https://news.1rj.ru/str/DataSimplifier
Like this post if you want me to continue this Power BI series 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
Here's a clear, step-by-step plan for you…
𝗪𝗲𝗲𝗸 𝟭: 𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜
➛ Day 1-2: Introduction to Power BI, installation, and understanding the interface.
➛ Day 3-4: Connecting to data sources and importing data.
➛ Day 5-7: Data cleaning and transforming using Power Query Editor.
𝗪𝗲𝗲𝗸 𝟮: 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴
➛ Day 8-10: Creating relationships between tables.
➛ Day 11-13: DAX basics – Calculated columns, measures, and key functions like SUM, COUNT.
➛ Day 14: Practice building a simple data model.
𝗪𝗲𝗲𝗸 𝟯: 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻
➛ Day 15-17: Building visualizations – bar charts, pie charts, and line graphs.
➛ Day 18-20: Using slicers, filters, and drill-through to create interactive reports.
➛ Day 21: Design a dashboard – bringing everything together.
𝗪𝗲𝗲𝗸 𝟰: 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗧𝗼𝗽𝗶𝗰𝘀 𝗮𝗻𝗱 𝗖𝗮𝗽𝘀𝘁𝗼𝗻𝗲
➛ Day 22-24: Advanced DAX – Time intelligence, IF statements, and nested functions.
➛ Day 25-27: Publishing to Power BI Service, sharing, and setting up scheduled refresh.
➛ Day 28-30: Capstone project – Build a full Power BI report from real data, complete with interactive visuals and insights.
You can refer these Power BI Interview Resources to learn more: https://news.1rj.ru/str/DataSimplifier
Like this post if you want me to continue this Power BI series 👍♥️
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SQL Checklist for Data Analysts 🚀
🌱 Getting Started with SQL
👉 Install SQL database software (MySQL, PostgreSQL, or SQL Server)
👉 Set up your database environment and connect to your data
🔍 Load & Explore Data
👉 Understand tables, rows, and columns
👉 Use SELECT to retrieve data and LIMIT to get a sample view
👉 Explore schema and table structure with DESCRIBE or SHOW COLUMNS
🧹 Data Filtering Essentials
👉 Filter data using WHERE clauses
👉 Use comparison operators (=, >, <) and logical operators (AND, OR)
👉 Handle NULL values with IS NULL and IS NOT NULL
🔄 Transforming Data
👉 Sort data with ORDER BY
👉 Create calculated columns with AS and use arithmetic operators (+, -, *, /)
👉 Use CASE WHEN for conditional expressions
📊 Aggregation & Grouping
👉 Summarize data with aggregation functions: SUM, COUNT, AVG, MIN, MAX
👉 Group data with GROUP BY and filter groups with HAVING
🔗 Mastering Joins
👉 Combine tables with JOIN (INNER, LEFT, RIGHT, FULL OUTER)
👉 Understand primary and foreign keys to create meaningful joins
👉 Use SELF JOIN for analyzing data within the same table
📅 Date & Time Data
👉 Convert dates and extract parts (year, month, day) with EXTRACT
👉 Perform time-based analysis using DATEDIFF and date functions
📈 Quick Exploratory Analysis
👉 Calculate statistics to understand data distributions
👉 Use GROUP BY with aggregation for category-based analysis
📉 Basic Data Visualizations (Optional)
👉 Integrate SQL with visualization tools (Power BI, Tableau)
👉 Create charts directly in SQL with certain extensions (like MySQL's built-in charts)
💪 Advanced Query Handling
👉 Master subqueries and nested queries
👉 Use WITH (Common Table Expressions) for complex queries
👉 Window functions for running totals, moving averages, and rankings (ROW_NUMBER, RANK, LAG, LEAD)
🚀 Optimize for Performance
👉 Index critical columns for faster querying
👉 Analyze query plans and use optimizations
👉 Limit result sets and avoid excessive joins for efficiency
📂 Practice Projects
👉 Use real datasets to perform SQL analysis
👉 Create a portfolio with case studies and projects
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Like this post if you need more 👍❤️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
🌱 Getting Started with SQL
👉 Install SQL database software (MySQL, PostgreSQL, or SQL Server)
👉 Set up your database environment and connect to your data
🔍 Load & Explore Data
👉 Understand tables, rows, and columns
👉 Use SELECT to retrieve data and LIMIT to get a sample view
👉 Explore schema and table structure with DESCRIBE or SHOW COLUMNS
🧹 Data Filtering Essentials
👉 Filter data using WHERE clauses
👉 Use comparison operators (=, >, <) and logical operators (AND, OR)
👉 Handle NULL values with IS NULL and IS NOT NULL
🔄 Transforming Data
👉 Sort data with ORDER BY
👉 Create calculated columns with AS and use arithmetic operators (+, -, *, /)
👉 Use CASE WHEN for conditional expressions
📊 Aggregation & Grouping
👉 Summarize data with aggregation functions: SUM, COUNT, AVG, MIN, MAX
👉 Group data with GROUP BY and filter groups with HAVING
🔗 Mastering Joins
👉 Combine tables with JOIN (INNER, LEFT, RIGHT, FULL OUTER)
👉 Understand primary and foreign keys to create meaningful joins
👉 Use SELF JOIN for analyzing data within the same table
📅 Date & Time Data
👉 Convert dates and extract parts (year, month, day) with EXTRACT
👉 Perform time-based analysis using DATEDIFF and date functions
📈 Quick Exploratory Analysis
👉 Calculate statistics to understand data distributions
👉 Use GROUP BY with aggregation for category-based analysis
📉 Basic Data Visualizations (Optional)
👉 Integrate SQL with visualization tools (Power BI, Tableau)
👉 Create charts directly in SQL with certain extensions (like MySQL's built-in charts)
💪 Advanced Query Handling
👉 Master subqueries and nested queries
👉 Use WITH (Common Table Expressions) for complex queries
👉 Window functions for running totals, moving averages, and rankings (ROW_NUMBER, RANK, LAG, LEAD)
🚀 Optimize for Performance
👉 Index critical columns for faster querying
👉 Analyze query plans and use optimizations
👉 Limit result sets and avoid excessive joins for efficiency
📂 Practice Projects
👉 Use real datasets to perform SQL analysis
👉 Create a portfolio with case studies and projects
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Like this post if you need more 👍❤️
Share with credits: https://news.1rj.ru/str/sqlspecialist
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👍35❤12🥰4
10 Advanced SQL Concepts For Data Analysts
1. Window Functions for Advanced Analytics:
Calculate running totals, ranks, and moving averages without subqueries.
2. Conditional Aggregation with CASE WHEN:
Segment data within a single query, saving time and creating versatile summaries.
3. CTEs for Modular Queries:
Make complex queries more readable and reusable with CTEs.
4. Optimize with EXISTS vs. IN:
Use EXISTS for better performance in larger datasets.
5. Self Joins for Row Comparisons:
Compare rows within the same table, helpful for changes over time.
6. UNION vs. UNION ALL:
Combine results from multiple queries; UNION ALL is faster as it doesn’t remove duplicates.
7. Handle NULLs with COALESCE:
Replace NULLs with defaults to avoid calculation issues.
8. Pivot Data with CASE Statements:
Transform rows into columns for clearer insights.
9. Extract Data with STRING Functions:
Useful for semi-structured data; extract domains, product codes, etc.
10. Indexing for Faster Queries:
Indexes speed up data retrieval, especially on frequently queried columns.
Mastering these SQL tricks will optimize your queries, simplify logic, and enable complex analyses.
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Like this post if you need more 👍❤️
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1. Window Functions for Advanced Analytics:
Calculate running totals, ranks, and moving averages without subqueries.
SELECT date, sales, SUM(sales) OVER (ORDER BY date) AS running_total FROM sales_data;
2. Conditional Aggregation with CASE WHEN:
Segment data within a single query, saving time and creating versatile summaries.
SELECT COUNT(CASE WHEN status = 'Completed' THEN 1 END) AS completed_orders FROM orders;
3. CTEs for Modular Queries:
Make complex queries more readable and reusable with CTEs.
WITH filtered_sales AS (SELECT * FROM sales_data WHERE region = 'North')
SELECT product, SUM(sales) FROM filtered_sales GROUP BY product;
4. Optimize with EXISTS vs. IN:
Use EXISTS for better performance in larger datasets.
SELECT * FROM customers c WHERE EXISTS (SELECT 1 FROM orders o WHERE o.customer_id = c.id);
5. Self Joins for Row Comparisons:
Compare rows within the same table, helpful for changes over time.
SELECT a.date, (a.sales - b.sales) AS sales_diff FROM sales_data a JOIN sales_data b ON a.date = b.date + INTERVAL '1' MONTH;
6. UNION vs. UNION ALL:
Combine results from multiple queries; UNION ALL is faster as it doesn’t remove duplicates.
7. Handle NULLs with COALESCE:
Replace NULLs with defaults to avoid calculation issues.
SELECT product, COALESCE(sales, 0) AS sales FROM product_sales;
8. Pivot Data with CASE Statements:
Transform rows into columns for clearer insights.
9. Extract Data with STRING Functions:
Useful for semi-structured data; extract domains, product codes, etc.
SELECT SUBSTRING(email, CHARINDEX('@', email) + 1, LEN(email)) AS domain FROM users;10. Indexing for Faster Queries:
Indexes speed up data retrieval, especially on frequently queried columns.
Mastering these SQL tricks will optimize your queries, simplify logic, and enable complex analyses.
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Like this post if you need more 👍❤️
Share with credits: https://news.1rj.ru/str/sqlspecialist
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👍28❤7🥰1😁1👌1
Master 𝗘𝘅𝗰𝗲𝗹 in just 𝟯𝟬 𝗗𝗮𝘆𝘀 with this simple plan!
Here's your complete Excel roadmap
𝗪𝗲𝗲𝗸 𝟭: 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗘𝘅𝗰𝗲𝗹 𝗕𝗮𝘀𝗶𝗰𝘀
➛ Day 1-2: Introduction to Excel, interface, and basic navigation.
➛ Day 3-4: Working with cells, rows, columns, and basic formatting.
➛ Day 5-7: Basic formulas and functions – SUM, AVERAGE, MIN, MAX.
𝗪𝗲𝗲𝗸 𝟮: 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗙𝗼𝗿𝗺𝘂𝗹𝗮𝘀
➛ Day 8-10: Advanced formulas – IF, VLOOKUP, and INDEX-MATCH.
➛ Day 11-13: Data sorting, filtering, and conditional formatting.
➛ Day 14: Practice session – Work on organizing and analyzing a small dataset.
𝗪𝗲𝗲𝗸 𝟯: 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗧𝗼𝗼𝗹𝘀
➛ Day 15-17: Pivot tables and charts – summarizing and visualizing data.
➛ Day 18-20: Working with data validation, drop-down lists, and named ranges.
➛ Day 21: Practice building a pivot table from scratch.
𝗪𝗲𝗲𝗸 𝟰: 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝗮𝗻𝗱 𝗖𝗮𝗽𝘀𝘁𝗼𝗻𝗲
➛ Day 22-24: Macros – Automating tasks with recorded macros.
➛ Day 25-27: Power Query and Power Pivot – for advanced data analysis.
➛ Day 28-30: Capstone project – Analyze a large dataset using all your Excel skills and create a comprehensive report.
Like if it helps ❤️
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://news.1rj.ru/str/DataSimplifier
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Hope it helps :)
Here's your complete Excel roadmap
𝗪𝗲𝗲𝗸 𝟭: 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗘𝘅𝗰𝗲𝗹 𝗕𝗮𝘀𝗶𝗰𝘀
➛ Day 1-2: Introduction to Excel, interface, and basic navigation.
➛ Day 3-4: Working with cells, rows, columns, and basic formatting.
➛ Day 5-7: Basic formulas and functions – SUM, AVERAGE, MIN, MAX.
𝗪𝗲𝗲𝗸 𝟮: 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗙𝗼𝗿𝗺𝘂𝗹𝗮𝘀
➛ Day 8-10: Advanced formulas – IF, VLOOKUP, and INDEX-MATCH.
➛ Day 11-13: Data sorting, filtering, and conditional formatting.
➛ Day 14: Practice session – Work on organizing and analyzing a small dataset.
𝗪𝗲𝗲𝗸 𝟯: 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗧𝗼𝗼𝗹𝘀
➛ Day 15-17: Pivot tables and charts – summarizing and visualizing data.
➛ Day 18-20: Working with data validation, drop-down lists, and named ranges.
➛ Day 21: Practice building a pivot table from scratch.
𝗪𝗲𝗲𝗸 𝟰: 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝗮𝗻𝗱 𝗖𝗮𝗽𝘀𝘁𝗼𝗻𝗲
➛ Day 22-24: Macros – Automating tasks with recorded macros.
➛ Day 25-27: Power Query and Power Pivot – for advanced data analysis.
➛ Day 28-30: Capstone project – Analyze a large dataset using all your Excel skills and create a comprehensive report.
Like if it helps ❤️
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://news.1rj.ru/str/DataSimplifier
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👍25❤12👌2🥰1👏1🎉1
Essential Power BI Interview Questions for Data Analysts:
🔹 Basic Power BI Concepts:
Define Power BI and its core components.
Differentiate between Power BI Desktop, Service, and Mobile.
🔹 Data Connectivity and Transformation:
Explain Power Query and its purpose in Power BI.
Describe common data sources that Power BI can connect to.
🔹 Data Modeling:
What is data modeling in Power BI, and why is it important?
Explain relationships in Power BI. How do one-to-many and many-to-many relationships work?
🔹 DAX (Data Analysis Expressions):
Define DAX and its importance in Power BI.
Write a DAX formula to calculate year-over-year growth.
Differentiate between calculated columns and measures.
🔹 Visualization:
Describe the types of visualizations available in Power BI.
How would you use slicers and filters to enhance user interaction?
🔹 Reports and Dashboards:
What is the difference between a Power BI report and a dashboard?
Explain the process of creating a dashboard in Power BI.
🔹 Publishing and Sharing:
How can you publish a Power BI report to the Power BI Service?
What are the options for sharing a report with others?
🔹 Row-Level Security (RLS):
Define Row-Level Security in Power BI and explain how to implement it.
🔹 Power BI Performance Optimization:
What techniques would you use to optimize a slow Power BI report?
Explain the role of aggregations and data reduction strategies.
🔹 Power BI Gateways:
Describe an on-premises data gateway and its purpose in Power BI.
How would you manage data refreshes with a gateway?
🔹 Advanced Power BI:
Explain incremental data refresh and how to set it up.
Discuss Power BI’s AI and Machine Learning capabilities.
🔹 Deployment Pipelines and Version Control:
How would you use deployment pipelines for development, testing, and production?
Explain version control best practices in Power BI.
I have curated the best interview resources to crack Power BI Interviews 👇👇
https://news.1rj.ru/str/DataSimplifier
You can find detailed answers here
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🔹 Basic Power BI Concepts:
Define Power BI and its core components.
Differentiate between Power BI Desktop, Service, and Mobile.
🔹 Data Connectivity and Transformation:
Explain Power Query and its purpose in Power BI.
Describe common data sources that Power BI can connect to.
🔹 Data Modeling:
What is data modeling in Power BI, and why is it important?
Explain relationships in Power BI. How do one-to-many and many-to-many relationships work?
🔹 DAX (Data Analysis Expressions):
Define DAX and its importance in Power BI.
Write a DAX formula to calculate year-over-year growth.
Differentiate between calculated columns and measures.
🔹 Visualization:
Describe the types of visualizations available in Power BI.
How would you use slicers and filters to enhance user interaction?
🔹 Reports and Dashboards:
What is the difference between a Power BI report and a dashboard?
Explain the process of creating a dashboard in Power BI.
🔹 Publishing and Sharing:
How can you publish a Power BI report to the Power BI Service?
What are the options for sharing a report with others?
🔹 Row-Level Security (RLS):
Define Row-Level Security in Power BI and explain how to implement it.
🔹 Power BI Performance Optimization:
What techniques would you use to optimize a slow Power BI report?
Explain the role of aggregations and data reduction strategies.
🔹 Power BI Gateways:
Describe an on-premises data gateway and its purpose in Power BI.
How would you manage data refreshes with a gateway?
🔹 Advanced Power BI:
Explain incremental data refresh and how to set it up.
Discuss Power BI’s AI and Machine Learning capabilities.
🔹 Deployment Pipelines and Version Control:
How would you use deployment pipelines for development, testing, and production?
Explain version control best practices in Power BI.
I have curated the best interview resources to crack Power BI Interviews 👇👇
https://news.1rj.ru/str/DataSimplifier
You can find detailed answers here
Share with credits: https://news.1rj.ru/str/sqlspecialist
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👍31❤9🥰1
10 Advanced Excel Concepts for Data Analysts
1. VLOOKUP & XLOOKUP for Fast Data Retrieval:
Quickly find data from different sheets with VLOOKUP or XLOOKUP for flexible lookups and defaults when no match is found.
2. Pivot Tables for Summarizing Data:
Quickly summarize, explore, and analyze large datasets with drag-and-drop ease.
3. Conditional Formatting for Key Insights:
Highlight trends and outliers automatically with conditional formatting, like Color Scales for instant data visualization.
4. Data Validation for Consistent Entries:
Use dropdowns and set criteria to avoid entry errors and maintain data consistency.
5. IFERROR for Clean Formulas:
Replace errors with default values like "N/A" for cleaner, more professional sheets.
6. INDEX-MATCH for Advanced Lookups:
INDEX-MATCH is more flexible than VLOOKUP, allowing lookups in any direction and handling large datasets effectively.
7. TEXT Functions for Data Cleaning:
Use LEFT, RIGHT, and TEXT functions to clean up inconsistent data formats or extract specific data elements.
8. Sparklines for Mini Data Visuals:
Insert mini line or bar charts directly in cells to show trends at a glance without taking up space.
9. Array Formulas (UNIQUE, FILTER, SORT):
Create dynamic lists and automatically update data with array formulas, perfect for unique values or filtered results.
10. Power Query for Efficient Data Transformation:
Use Power Query to clean and reshape data from multiple sources effortlessly, making data prep faster.
Read this blog for more details
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://news.1rj.ru/str/DataSimplifier
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1. VLOOKUP & XLOOKUP for Fast Data Retrieval:
Quickly find data from different sheets with VLOOKUP or XLOOKUP for flexible lookups and defaults when no match is found.
2. Pivot Tables for Summarizing Data:
Quickly summarize, explore, and analyze large datasets with drag-and-drop ease.
3. Conditional Formatting for Key Insights:
Highlight trends and outliers automatically with conditional formatting, like Color Scales for instant data visualization.
4. Data Validation for Consistent Entries:
Use dropdowns and set criteria to avoid entry errors and maintain data consistency.
5. IFERROR for Clean Formulas:
Replace errors with default values like "N/A" for cleaner, more professional sheets.
6. INDEX-MATCH for Advanced Lookups:
INDEX-MATCH is more flexible than VLOOKUP, allowing lookups in any direction and handling large datasets effectively.
7. TEXT Functions for Data Cleaning:
Use LEFT, RIGHT, and TEXT functions to clean up inconsistent data formats or extract specific data elements.
8. Sparklines for Mini Data Visuals:
Insert mini line or bar charts directly in cells to show trends at a glance without taking up space.
9. Array Formulas (UNIQUE, FILTER, SORT):
Create dynamic lists and automatically update data with array formulas, perfect for unique values or filtered results.
10. Power Query for Efficient Data Transformation:
Use Power Query to clean and reshape data from multiple sources effortlessly, making data prep faster.
Read this blog for more details
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://news.1rj.ru/str/DataSimplifier
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Share with credits: https://news.1rj.ru/str/sqlspecialist
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👍11❤6🥰1👏1🎉1
𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿: You have only 2 minutes to solve this SQL query.
Retrieve the employee names and their manager names from the employees table, where both the employee and manager work in the same department.
𝗠𝗲: Challenge accepted!
I used a self-join to connect the employees table with itself, matching employees with their managers based on manager_id and employee_id. The ON condition specifies the relationship, and WHERE ensures both employee and manager are in the same department. This query demonstrates how self-joins allow us to link a table to itself to extract meaningful relationships between its rows.
𝗧𝗶𝗽 𝗳𝗼𝗿 𝗦𝗤𝗟 𝗝𝗼𝗯 𝗦𝗲𝗲𝗸𝗲𝗿𝘀:
Understanding joins is crucial—INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN, and SELF JOIN each have unique applications.
Master these to confidently navigate complex datasets and queries.
I've compiled essential SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Like this post if you need more 👍❤️
Hope it helps :)
Retrieve the employee names and their manager names from the employees table, where both the employee and manager work in the same department.
𝗠𝗲: Challenge accepted!
SELECT e.employee_name, m.employee_name AS manager_name
FROM employees e
JOIN employees m ON e.manager_id = m.employee_id
WHERE e.department = m.department;`
I used a self-join to connect the employees table with itself, matching employees with their managers based on manager_id and employee_id. The ON condition specifies the relationship, and WHERE ensures both employee and manager are in the same department. This query demonstrates how self-joins allow us to link a table to itself to extract meaningful relationships between its rows.
𝗧𝗶𝗽 𝗳𝗼𝗿 𝗦𝗤𝗟 𝗝𝗼𝗯 𝗦𝗲𝗲𝗸𝗲𝗿𝘀:
Understanding joins is crucial—INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN, and SELF JOIN each have unique applications.
Master these to confidently navigate complex datasets and queries.
I've compiled essential SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Like this post if you need more 👍❤️
Hope it helps :)
👍41❤11👏5🥰1🎉1
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!
I have curated essential Power BI Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Like this post if you need more 👍❤️
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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!
I have curated essential Power BI Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Like this post if you need more 👍❤️
Hope it helps! :)
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𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿: You have only 2 minutes to solve this problem with Tableau.
Retrieve the department name and the highest salary in each department from the 'Employees' dataset, but only for departments where the highest salary is greater than $70,000.
𝗠𝗲: Challenge accepted!
1️⃣ Create a New Sheet: Start by dragging Department to the Rows shelf and Salary to the Columns shelf.
2️⃣ Calculate Highest Salary per Department:
Right-click on Salary in the Columns shelf, select Measure, and choose Maximum to show the highest salary for each department.
3️⃣ Apply Filter for Salary > $70,000:
Drag Salary to the Filters shelf, select Maximum as the aggregation type, and set the condition to > 70000.
𝗧𝗶𝗽 𝗳𝗼𝗿 𝗧𝗮𝗯𝗹𝗲𝗮𝘂:
Focus on mastering calculated fields, aggregation functions, and filters. Building interactive, user-friendly dashboards is key in Tableau!
I have curated essential Tableau Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Like this post if you need more 👍❤️
Hope it helps! :)
Retrieve the department name and the highest salary in each department from the 'Employees' dataset, but only for departments where the highest salary is greater than $70,000.
𝗠𝗲: Challenge accepted!
1️⃣ Create a New Sheet: Start by dragging Department to the Rows shelf and Salary to the Columns shelf.
2️⃣ Calculate Highest Salary per Department:
Right-click on Salary in the Columns shelf, select Measure, and choose Maximum to show the highest salary for each department.
3️⃣ Apply Filter for Salary > $70,000:
Drag Salary to the Filters shelf, select Maximum as the aggregation type, and set the condition to > 70000.
𝗧𝗶𝗽 𝗳𝗼𝗿 𝗧𝗮𝗯𝗹𝗲𝗮𝘂:
Focus on mastering calculated fields, aggregation functions, and filters. Building interactive, user-friendly dashboards is key in Tableau!
I have curated essential Tableau Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Like this post if you need more 👍❤️
Hope it helps! :)
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