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0- Python
1- Data Science
2- Machine Learning
3- Data Visualization
4- Artificial Intelligence
5- Data Analysis
6- Statistics
7- Deep Learning
8- programming Languages
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Understanding CTEs in SQL
A Common Table Expression (CTE) is a temporary result set that you can refer to within a SELECT, INSERT, UPDATE, or DELETE statement. It provides better readability and can be thought of as defining a temporary view for just one query.
A Common Table Expression (CTE) is a temporary result set that you can refer to within a SELECT, INSERT, UPDATE, or DELETE statement. It provides better readability and can be thought of as defining a temporary view for just one query.
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Checklist to become data analyst
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Data Analytics using SQL & Excel
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Hey 👋
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◾How to get it:
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3. Click the 'I Want This' Button
4. Enter your email and get it delivered!
I'd appreciate it if you could give it a 5 star when you download it.
Join for more: https://t.me/sqlspecialist
Thanks 😊
Here you can access Resources for SQL & Excel❤️🔥👇
https://dataanalysts.gumroad.com/l/Sql?a=363448787
◾How to get it:
1. Click on the link
2. Enter the amount you like [Can be 0 as well :) ]
3. Click the 'I Want This' Button
4. Enter your email and get it delivered!
I'd appreciate it if you could give it a 5 star when you download it.
Join for more: https://t.me/sqlspecialist
Thanks 😊
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Forwarded from Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources
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800+ SQL Interview questions and answers 👇👇
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📊Here's a breakdown of SQL interview questions covering various topics:
🔺Basic SQL Concepts:
-Differentiate between SQL and NoSQL databases.
-List common data types in SQL.
🔺Querying:
-Retrieve all records from a table named "Customers."
-Contrast SELECT and SELECT DISTINCT.
-Explain the purpose of the WHERE clause.
🔺Joins:
-Describe types of joins (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN).
-Retrieve data from two tables using INNER JOIN.
🔺Aggregate Functions:
-Define aggregate functions and name a few.
-Calculate average, sum, and count of a column in SQL.
🔺Grouping and Filtering:
-Explain the GROUP BY clause and its use.
-Filter SQL query results using the HAVING clause.
🔺Subqueries:
-Define a subquery and provide an example.
🔺Indexes and Optimization:
-Discuss the importance of indexes in a database.
&Optimize a slow-running SQL query.
🔺Normalization and Data Integrity:
-Define database normalization and its significance.
-Enforce data integrity in a SQL database.
🔺Transactions:
-Define a SQL transaction and its purpose.
-Explain ACID properties in database transactions.
🔺Views and Stored Procedures:
-Define a database view and its use.
-Distinguish a stored procedure from a regular SQL query.
🔺Advanced SQL:
-Write a recursive SQL query and explain its use.
-Explain window functions in SQL.
✅👀These questions offer a comprehensive assessment of SQL knowledge, ranging from basics to advanced concepts.
❤️Like if you'd like answers in the next post! 👍
👉Be the first one to know the latest Job openings 👇
https://t.me/jobs_SQL
🔺Basic SQL Concepts:
-Differentiate between SQL and NoSQL databases.
-List common data types in SQL.
🔺Querying:
-Retrieve all records from a table named "Customers."
-Contrast SELECT and SELECT DISTINCT.
-Explain the purpose of the WHERE clause.
🔺Joins:
-Describe types of joins (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN).
-Retrieve data from two tables using INNER JOIN.
🔺Aggregate Functions:
-Define aggregate functions and name a few.
-Calculate average, sum, and count of a column in SQL.
🔺Grouping and Filtering:
-Explain the GROUP BY clause and its use.
-Filter SQL query results using the HAVING clause.
🔺Subqueries:
-Define a subquery and provide an example.
🔺Indexes and Optimization:
-Discuss the importance of indexes in a database.
&Optimize a slow-running SQL query.
🔺Normalization and Data Integrity:
-Define database normalization and its significance.
-Enforce data integrity in a SQL database.
🔺Transactions:
-Define a SQL transaction and its purpose.
-Explain ACID properties in database transactions.
🔺Views and Stored Procedures:
-Define a database view and its use.
-Distinguish a stored procedure from a regular SQL query.
🔺Advanced SQL:
-Write a recursive SQL query and explain its use.
-Explain window functions in SQL.
✅👀These questions offer a comprehensive assessment of SQL knowledge, ranging from basics to advanced concepts.
❤️Like if you'd like answers in the next post! 👍
👉Be the first one to know the latest Job openings 👇
https://t.me/jobs_SQL
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Answers for this👀
🔺Basic SQL Concepts:
SQL vs NoSQL: SQL is relational, structured, and uses a predefined schema. NoSQL is non-relational, flexible, and schema-less.
Common Data Types: Examples include INT, VARCHAR, DATE, and BOOLEAN.
🔺Querying:
Retrieve all records from "Customers": SELECT * FROM Customers;
SELECT vs SELECT DISTINCT: SELECT retrieves all rows, while SELECT DISTINCT returns only unique values.
WHERE clause: Filters data based on specified conditions.
🔺Joins:
Types of Joins: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN.
INNER JOIN example: SELECT * FROM Table1 INNER JOIN Table2 ON Table1.ID = Table2.ID;
🔺Aggregate Functions:
Aggregate Functions: Examples include COUNT, AVG, SUM.
Calculate average, sum, count: SELECT AVG(column), SUM(column), COUNT(column) FROM Table;
🔺Grouping and Filtering:
GROUP BY clause: Groups results based on specified columns.
HAVING clause: Filters grouped results.
🔺Subqueries:
Subquery: A query within another query. Example: SELECT column FROM Table WHERE column = (SELECT MAX(column) FROM Table);
🔺Indexes and Optimization:
Importance of Indexes: Improve query performance by speeding up data retrieval.
Optimize slow query: Add indexes, optimize queries, and consider database design.
🔺Normalization and Data Integrity:
Normalization: Organizing data to reduce redundancy and dependency.
Data Integrity: Enforce rules to maintain accuracy and consistency.
🔺Transactions:
SQL Transaction: A sequence of one or more SQL statements treated as a single unit.
ACID properties: Atomicity, Consistency, Isolation, Durability.
🔺Views and Stored Procedures:
Database View: Virtual table based on the result of a SELECT query.
Stored Procedure: Precompiled SQL code stored in the database for reuse.
🔺Advanced SQL:
Recursive SQL query: Used for hierarchical data.
Window Functions: Perform calculations across a set of rows related to the current row.
React❤️👉 to this if you like the post
👉Be the first one to know the latest Job openings
https://t.me/jobs_SQL
🔺Basic SQL Concepts:
SQL vs NoSQL: SQL is relational, structured, and uses a predefined schema. NoSQL is non-relational, flexible, and schema-less.
Common Data Types: Examples include INT, VARCHAR, DATE, and BOOLEAN.
🔺Querying:
Retrieve all records from "Customers": SELECT * FROM Customers;
SELECT vs SELECT DISTINCT: SELECT retrieves all rows, while SELECT DISTINCT returns only unique values.
WHERE clause: Filters data based on specified conditions.
🔺Joins:
Types of Joins: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN.
INNER JOIN example: SELECT * FROM Table1 INNER JOIN Table2 ON Table1.ID = Table2.ID;
🔺Aggregate Functions:
Aggregate Functions: Examples include COUNT, AVG, SUM.
Calculate average, sum, count: SELECT AVG(column), SUM(column), COUNT(column) FROM Table;
🔺Grouping and Filtering:
GROUP BY clause: Groups results based on specified columns.
HAVING clause: Filters grouped results.
🔺Subqueries:
Subquery: A query within another query. Example: SELECT column FROM Table WHERE column = (SELECT MAX(column) FROM Table);
🔺Indexes and Optimization:
Importance of Indexes: Improve query performance by speeding up data retrieval.
Optimize slow query: Add indexes, optimize queries, and consider database design.
🔺Normalization and Data Integrity:
Normalization: Organizing data to reduce redundancy and dependency.
Data Integrity: Enforce rules to maintain accuracy and consistency.
🔺Transactions:
SQL Transaction: A sequence of one or more SQL statements treated as a single unit.
ACID properties: Atomicity, Consistency, Isolation, Durability.
🔺Views and Stored Procedures:
Database View: Virtual table based on the result of a SELECT query.
Stored Procedure: Precompiled SQL code stored in the database for reuse.
🔺Advanced SQL:
Recursive SQL query: Used for hierarchical data.
Window Functions: Perform calculations across a set of rows related to the current row.
React❤️👉 to this if you like the post
👉Be the first one to know the latest Job openings
https://t.me/jobs_SQL
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Comment "SQL" to get the link to practice SQL skills 👇
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TOP CONCEPTS FOR INTERVIEW PREPARATION!!
🚀TOP 10 SQL Concepts for Job Interview
1. Aggregate Functions (SUM/AVG)
2. Group By and Order By
3. JOINs (Inner/Left/Right)
4. Union and Union All
5. Date and Time processing
6. String processing
7. Window Functions (Partition by)
8. Subquery
9. View and Index
10. Common Table Expression (CTE)
🚀TOP 10 Statistics Concepts for Job Interview
1. Sampling
2. Experiments (A/B tests)
3. Descriptive Statistics
4. p-value
5. Probability Distributions
6. t-test
7. ANOVA
8. Correlation
9. Linear Regression
10. Logistics Regression
🚀TOP 10 Python Concepts for Job Interview
1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming
Like ❤️ the post if it was helpful to you!!!
🚀TOP 10 SQL Concepts for Job Interview
1. Aggregate Functions (SUM/AVG)
2. Group By and Order By
3. JOINs (Inner/Left/Right)
4. Union and Union All
5. Date and Time processing
6. String processing
7. Window Functions (Partition by)
8. Subquery
9. View and Index
10. Common Table Expression (CTE)
🚀TOP 10 Statistics Concepts for Job Interview
1. Sampling
2. Experiments (A/B tests)
3. Descriptive Statistics
4. p-value
5. Probability Distributions
6. t-test
7. ANOVA
8. Correlation
9. Linear Regression
10. Logistics Regression
🚀TOP 10 Python Concepts for Job Interview
1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming
Like ❤️ the post if it was helpful to you!!!
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