Data Analytics
Thanks for the amazing response on last poll. Because of the huge request, I have decided to post important data analyst questions in the channel on daily basis 😊 Data Analyst Interview Part-1 1. What is the difference between a primary key and a foreign…
Data analyst interview Part-2
6. What is the difference between a WHERE and HAVING clause in SQL?
Answer:
WHERE is used to filter records before aggregation (used with SELECT, UPDATE, DELETE).
HAVING is used to filter records after aggregation (used with GROUP BY).
Example:
7. What are the different types of filters in Power BI?
Answer:
Power BI provides different types of filters for refining data:
Visual-level filters – Apply to a single visual.
Page-level filters – Apply to all visuals on a report page.
Report-level filters – Apply to the entire report.
Drillthrough filters – Allow users to focus on specific data in another page.
Top N filters – Show only the top N records based on a measure.
Example: In Power BI Desktop, you can apply filters using the Filters Pane.
8. How do you remove duplicate values in Excel?
Answer:
You can remove duplicates in Excel using:
Remove Duplicates feature:
Select your data.
Go to Data → Remove Duplicates and choose columns to check for duplicates.
Using a formula (COUNTIF method):
Using Power Query:
Load data into Power Query Editor → Remove Duplicates option.
9. What is the difference between a Bar Chart and a Histogram?
Answer:
Bar Chart represents categorical data with discrete bars.
Histogram represents continuous data and shows frequency distribution.
Example:
A Bar Chart can show sales by product category.
A Histogram can show age distribution in a population dataset.
10. How do you handle missing values in Python using Pandas?
Answer:
You can handle missing values using:
Drop missing values:
Fill missing values:
Fill with column mean/median/mode:
Interpolate missing values:
I have curated best 80+ top-notch Data Analytics Resources 👇👇
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6. What is the difference between a WHERE and HAVING clause in SQL?
Answer:
WHERE is used to filter records before aggregation (used with SELECT, UPDATE, DELETE).
HAVING is used to filter records after aggregation (used with GROUP BY).
Example:
-- WHERE filters before aggregation SELECT * FROM Sales WHERE Region = 'North'; -- HAVING filters after aggregation SELECT Region, SUM(Sales) AS TotalSales FROM Sales GROUP BY Region HAVING SUM(Sales) > 50000;
7. What are the different types of filters in Power BI?
Answer:
Power BI provides different types of filters for refining data:
Visual-level filters – Apply to a single visual.
Page-level filters – Apply to all visuals on a report page.
Report-level filters – Apply to the entire report.
Drillthrough filters – Allow users to focus on specific data in another page.
Top N filters – Show only the top N records based on a measure.
Example: In Power BI Desktop, you can apply filters using the Filters Pane.
8. How do you remove duplicate values in Excel?
Answer:
You can remove duplicates in Excel using:
Remove Duplicates feature:
Select your data.
Go to Data → Remove Duplicates and choose columns to check for duplicates.
Using a formula (COUNTIF method):
=IF(COUNTIF(A:A, A2)>1, "Duplicate", "Unique") Using Power Query:
Load data into Power Query Editor → Remove Duplicates option.
9. What is the difference between a Bar Chart and a Histogram?
Answer:
Bar Chart represents categorical data with discrete bars.
Histogram represents continuous data and shows frequency distribution.
Example:
A Bar Chart can show sales by product category.
A Histogram can show age distribution in a population dataset.
10. How do you handle missing values in Python using Pandas?
Answer:
You can handle missing values using:
Drop missing values:
df.dropna() # Removes rows with missing values Fill missing values:
df.fillna(0) # Replaces NaN with 0 Fill with column mean/median/mode:
df['Column'].fillna(df['Column'].mean(), inplace=True) Interpolate missing values:
df.interpolate(method='linear')I have curated best 80+ top-notch Data Analytics Resources 👇👇
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👍20❤5
Data Analytics
Day 22: Database Design & Normalization 1. What is Database Design? Database design is the process of structuring data efficiently to avoid redundancy, improve consistency, and optimize performance. 2. What is Normalization? Normalization is a technique…
Day 23: Constraints in SQL
SQL constraints are rules applied to columns in a table to ensure data integrity and accuracy.
1. Types of SQL Constraints
✅ PRIMARY KEY
Ensures each row is unique and not NULL.
A table can have only one primary key.
✅ FOREIGN KEY
Enforces a relationship between tables.
Ensures data exists in the referenced table.
✅ UNIQUE
Ensures all values in a column are distinct.
Unlike PRIMARY KEY, a table can have multiple UNIQUE constraints.
✅ CHECK
Sets a condition that each row must satisfy.
Example: Age must be greater than 18.
✅ DEFAULT
Assigns a default value if no value is provided.
✅ NOT NULL
Ensures a column cannot have NULL values.
2. Why Are Constraints Important?
✔ Prevent invalid data entry (e.g., age cannot be negative).
✔ Ensure referential integrity (foreign keys link valid records).
✔ Improve query performance by enforcing structure.
3. Common Use Cases
📌 PRIMARY KEY → Used for unique identification of records.
📌 FOREIGN KEY → Used for linking tables in a database.
📌 CHECK → Used for validation (e.g., salary must be positive).
📌 UNIQUE → Used to avoid duplicates in specific columns.
📌 NOT NULL → Used when a column must always have a value.
✅ Action Plan for Today:
1️⃣ Understand when to use each constraint.
2️⃣ Try adding constraints to an existing table definition.
3️⃣ Practice writing SQL queries using PRIMARY KEY, FOREIGN KEY, and CHECK constraints.
🔝 SQL 30 Days Challenge
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/sqlanalyst
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SQL constraints are rules applied to columns in a table to ensure data integrity and accuracy.
1. Types of SQL Constraints
✅ PRIMARY KEY
Ensures each row is unique and not NULL.
A table can have only one primary key.
✅ FOREIGN KEY
Enforces a relationship between tables.
Ensures data exists in the referenced table.
✅ UNIQUE
Ensures all values in a column are distinct.
Unlike PRIMARY KEY, a table can have multiple UNIQUE constraints.
✅ CHECK
Sets a condition that each row must satisfy.
Example: Age must be greater than 18.
✅ DEFAULT
Assigns a default value if no value is provided.
✅ NOT NULL
Ensures a column cannot have NULL values.
2. Why Are Constraints Important?
✔ Prevent invalid data entry (e.g., age cannot be negative).
✔ Ensure referential integrity (foreign keys link valid records).
✔ Improve query performance by enforcing structure.
3. Common Use Cases
📌 PRIMARY KEY → Used for unique identification of records.
📌 FOREIGN KEY → Used for linking tables in a database.
📌 CHECK → Used for validation (e.g., salary must be positive).
📌 UNIQUE → Used to avoid duplicates in specific columns.
📌 NOT NULL → Used when a column must always have a value.
✅ Action Plan for Today:
1️⃣ Understand when to use each constraint.
2️⃣ Try adding constraints to an existing table definition.
3️⃣ Practice writing SQL queries using PRIMARY KEY, FOREIGN KEY, and CHECK constraints.
🔝 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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Hope it helps :)
👍15❤3🥰2🎉1👌1
Data Analytics
Day 23: Constraints in SQL SQL constraints are rules applied to columns in a table to ensure data integrity and accuracy. 1. Types of SQL Constraints ✅ PRIMARY KEY Ensures each row is unique and not NULL. A table can have only one primary key. ✅ FOREIGN…
Day 24: Creating and Managing Indexes in SQL
1. What is an Index in SQL?
An index is a database object that improves the speed of data retrieval from a table. It's like an index in a book—it helps you find information quickly without scanning the entire table.
2. Types of Indexes
✅ Primary Index (Clustered Index):
Automatically created on the PRIMARY KEY.
Physically organizes data in a sorted order.
A table can have only one clustered index.
✅ Secondary Index (Non-Clustered Index):
Created manually using the CREATE INDEX command.
Stores a pointer to the actual data (does not change physical order).
A table can have multiple non-clustered indexes.
✅ Unique Index:
Ensures that all values in a column are distinct (same as UNIQUE constraint).
✅ Full-Text Index:
Used for fast text searches in large text-based columns.
✅ Composite Index:
Created on multiple columns to optimize queries using those columns together.
3. How to Create and Drop Indexes
📌 Create an Index
📌 Create a Composite Index
📌 Drop an Index
4. When to Use Indexes?
✔ Use indexes on frequently searched columns.
✔ Use indexes on JOIN and WHERE clause columns.
✔ Avoid indexing small tables (full table scans are faster).
✔ Avoid too many indexes (they slow down INSERT, UPDATE, DELETE).
✅ Action Plan for Today:
1️⃣ Identify queries in your database that could benefit from an index.
2️⃣ Create a non-clustered index on a table and check the performance.
3️⃣ Drop unnecessary indexes and observe the difference.
🔝 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 an Index in SQL?
An index is a database object that improves the speed of data retrieval from a table. It's like an index in a book—it helps you find information quickly without scanning the entire table.
2. Types of Indexes
✅ Primary Index (Clustered Index):
Automatically created on the PRIMARY KEY.
Physically organizes data in a sorted order.
A table can have only one clustered index.
✅ Secondary Index (Non-Clustered Index):
Created manually using the CREATE INDEX command.
Stores a pointer to the actual data (does not change physical order).
A table can have multiple non-clustered indexes.
✅ Unique Index:
Ensures that all values in a column are distinct (same as UNIQUE constraint).
✅ Full-Text Index:
Used for fast text searches in large text-based columns.
✅ Composite Index:
Created on multiple columns to optimize queries using those columns together.
3. How to Create and Drop Indexes
📌 Create an Index
CREATE INDEX idx_customer_name ON customers (customer_name); 📌 Create a Composite Index
CREATE INDEX idx_order ON orders (customer_id, order_date); 📌 Drop an Index
DROP INDEX idx_customer_name; 4. When to Use Indexes?
✔ Use indexes on frequently searched columns.
✔ Use indexes on JOIN and WHERE clause columns.
✔ Avoid indexing small tables (full table scans are faster).
✔ Avoid too many indexes (they slow down INSERT, UPDATE, DELETE).
✅ Action Plan for Today:
1️⃣ Identify queries in your database that could benefit from an index.
2️⃣ Create a non-clustered index on a table and check the performance.
3️⃣ Drop unnecessary indexes and observe the difference.
🔝 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 :)
❤13👍6👏3🥰1
Data Analytics
Day 24: Creating and Managing Indexes in SQL 1. What is an Index in SQL? An index is a database object that improves the speed of data retrieval from a table. It's like an index in a book—it helps you find information quickly without scanning the entire…
Day 25: Backup and Restore Strategies in SQL (Good to know concept)
1. Why Are Backups Important?
Backups protect your database from accidental deletions, hardware failures, or cyberattacks. A good backup strategy ensures minimal downtime and data recovery when needed.
2. Types of Backups:
✅ Full Backup
Backs up the entire database (all tables, indexes, and transactions).
Used for disaster recovery.
Example:
✅ Differential Backup
Backs up only changes made since the last full backup.
Faster than a full backup.
Example:
✅ Transaction Log Backup
Backs up the transaction logs, allowing point-in-time recovery.
Example:
3. How to Restore a Database
📌 Restore a Full Backup
📌 Restore with Differential Backup
4. Best Practices for Database Backups
✔ Schedule backups regularly (daily or weekly).
✔ Store backups in multiple locations (local + cloud).
✔ Automate backups to avoid manual errors.
✔ Test your backups by restoring to a test database.
✅ Action Plan for Today:
1️⃣ Identify the best backup strategy for your database.
2️⃣ Set up an automated full backup.
3️⃣ Try restoring a backup to check if it works correctly.
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. Why Are Backups Important?
Backups protect your database from accidental deletions, hardware failures, or cyberattacks. A good backup strategy ensures minimal downtime and data recovery when needed.
2. Types of Backups:
✅ Full Backup
Backs up the entire database (all tables, indexes, and transactions).
Used for disaster recovery.
Example:
BACKUP DATABASE mydb TO DISK = 'C:\backups\mydb_full.bak';
✅ Differential Backup
Backs up only changes made since the last full backup.
Faster than a full backup.
Example:
BACKUP DATABASE mydb TO DISK = 'C:\backups\mydb_diff.bak' WITH DIFFERENTIAL;
✅ Transaction Log Backup
Backs up the transaction logs, allowing point-in-time recovery.
Example:
BACKUP LOG mydb TO DISK = 'C:\backups\mydb_log.trn'; 3. How to Restore a Database
📌 Restore a Full Backup
RESTORE DATABASE mydb FROM DISK = 'C:\backups\mydb_full.bak';
📌 Restore with Differential Backup
RESTORE DATABASE mydb FROM DISK = 'C:\backups\mydb_full.bak' WITH NORECOVERY; RESTORE DATABASE mydb FROM DISK = 'C:\backups\mydb_diff.bak' WITH RECOVERY;
4. Best Practices for Database Backups
✔ Schedule backups regularly (daily or weekly).
✔ Store backups in multiple locations (local + cloud).
✔ Automate backups to avoid manual errors.
✔ Test your backups by restoring to a test database.
✅ Action Plan for Today:
1️⃣ Identify the best backup strategy for your database.
2️⃣ Set up an automated full backup.
3️⃣ Try restoring a backup to check if it works correctly.
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
Data analytics offers excellent job prospects in 2025, with numerous opportunities across various industries.
Job Market Overview
Data analyst jobs are experiencing rapid growth, with an expected expansion in multiple sectors.
- High Demand Roles:
- Data Scientist
- Business Intelligence Analyst
- Financial Analyst
- Marketing Analyst
- Healthcare Data Analyst
Skills Required
Top skills for success in data analytics include:
- Technical Skills:
- Python and R programming
- SQL database management
- Data manipulation and cleaning
- Statistical analysis
- Power BI or Tableau
- Machine learning basics
Salary Expectations
Average salaries vary by role:
- Data Scientist: ~$122,738 per year
- Data Analyst: Around INR 6L per annum
- Entry-level Data Analyst: ~$83,011 annually[2]
Job Search Strategies
- Utilize job portals like LinkedIn, Indeed & telegram
- Attend industry conferences and webinars
- Network with professionals
- Check company career pages
- Consider recruitment agencies specializing in tech roles
I have curated best 80+ top-notch Data Analytics Resources 👇👇
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Job Market Overview
Data analyst jobs are experiencing rapid growth, with an expected expansion in multiple sectors.
- High Demand Roles:
- Data Scientist
- Business Intelligence Analyst
- Financial Analyst
- Marketing Analyst
- Healthcare Data Analyst
Skills Required
Top skills for success in data analytics include:
- Technical Skills:
- Python and R programming
- SQL database management
- Data manipulation and cleaning
- Statistical analysis
- Power BI or Tableau
- Machine learning basics
Salary Expectations
Average salaries vary by role:
- Data Scientist: ~$122,738 per year
- Data Analyst: Around INR 6L per annum
- Entry-level Data Analyst: ~$83,011 annually[2]
Job Search Strategies
- Utilize job portals like LinkedIn, Indeed & telegram
- Attend industry conferences and webinars
- Network with professionals
- Check company career pages
- Consider recruitment agencies specializing in tech roles
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://news.1rj.ru/str/DataSimplifier
Like this post for if you want me to continue the interview series 👍♥️
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👍13
Data Analytics
Data analyst interview Part-2 6. What is the difference between a WHERE and HAVING clause in SQL? Answer: WHERE is used to filter records before aggregation (used with SELECT, UPDATE, DELETE). HAVING is used to filter records after aggregation (used with…
Data Analyst Interview Series Part-3
11. What is the difference between UNION and UNION ALL in SQL?
Answer:
UNION combines results from two queries and removes duplicates.
UNION ALL combines results but keeps duplicates for better performance.
Example:
12. What are common DAX functions in Power BI?
Answer:
DAX (Data Analysis Expressions) is used in Power BI for calculations.
Common DAX functions include:
SUM() – Adds up values in a column.
AVERAGE() – Finds the mean value.
COUNT() – Counts the number of rows.
CALCULATE() – Modifies a measure based on conditions.
FILTER() – Returns a subset of data.
Example:
13. How do you use VLOOKUP in Excel?
Answer:
VLOOKUP searches for a value in the first column of a range and returns a value from another column.
Syntax:
Example:
To find the price of a product in a table:
14. What is the difference between a Heatmap and a Scatter Plot?
Answer:
Heatmap: Uses color intensity to represent values across a matrix. Used for correlation analysis.
Scatter Plot: Shows relationships between two continuous variables using dots. Used for trend analysis.
Example:
A Heatmap can show sales performance by region and product category.
A Scatter Plot can show sales vs. profit for different stores.
15. How do you read a CSV file into Pandas in Python?
Answer:
You can read a CSV file using pandas.read_csv():
To handle missing values:
To select specific columns:
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11. What is the difference between UNION and UNION ALL in SQL?
Answer:
UNION combines results from two queries and removes duplicates.
UNION ALL combines results but keeps duplicates for better performance.
Example:
SELECT CustomerID FROM Orders_A
UNION
SELECT CustomerID FROM Orders_B; -- Removes duplicates
SELECT CustomerID FROM Orders_A
UNION ALL
SELECT CustomerID FROM Orders_B; -- Keeps duplicates
12. What are common DAX functions in Power BI?
Answer:
DAX (Data Analysis Expressions) is used in Power BI for calculations.
Common DAX functions include:
SUM() – Adds up values in a column.
AVERAGE() – Finds the mean value.
COUNT() – Counts the number of rows.
CALCULATE() – Modifies a measure based on conditions.
FILTER() – Returns a subset of data.
Example:
TotalSales = SUM(Sales[Amount]) FilteredSales = CALCULATE(SUM(Sales[Amount]), Sales[Region] = "North")
13. How do you use VLOOKUP in Excel?
Answer:
VLOOKUP searches for a value in the first column of a range and returns a value from another column.
Syntax:
=VLOOKUP(lookup_value, table_array, col_index_num, [range_lookup]) Example:
To find the price of a product in a table:
=VLOOKUP("ProductA", A2:C10, 2, FALSE) 14. What is the difference between a Heatmap and a Scatter Plot?
Answer:
Heatmap: Uses color intensity to represent values across a matrix. Used for correlation analysis.
Scatter Plot: Shows relationships between two continuous variables using dots. Used for trend analysis.
Example:
A Heatmap can show sales performance by region and product category.
A Scatter Plot can show sales vs. profit for different stores.
15. How do you read a CSV file into Pandas in Python?
Answer:
You can read a CSV file using pandas.read_csv():
import pandas as pd df = pd.read_csv("data.csv") print(df.head()) # Displays first 5 rows To handle missing values:
df = pd.read_csv("data.csv", na_values=["NA", "Missing"]) To select specific columns:
df = pd.read_csv("data.csv", usecols=["Name", "Sales"])I have curated best 80+ top-notch Data Analytics Resources 👇👇
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👍8❤7
Top companies currently hiring data analysts
Based on the current job market in 2025, here are the top companies hiring data analysts:
## Top Tech Companies
- Meta: Investing heavily in AI with significant GPU investments
- Amazon: Offers diverse data analyst roles with complex responsibilities
- Google (Alphabet): Leverages massive data ecosystems
- JP Morgan Chase & Co.: Strong focus on data-driven banking transformation
## Specialized Data Analytics Firms
- Tiger Analytics: Specializes in AI/ML solutions
- SG Analytics: Provides data-driven insights
- Monte Carlo Data: Focuses on data observability
- CB Insights: Excels in market intelligence
## Emerging Opportunities
Companies like Samsara, ScienceSoft, and Forage are also actively recruiting data analysts, offering competitive salaries ranging from $85,000 to $207,000 annually.
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Based on the current job market in 2025, here are the top companies hiring data analysts:
## Top Tech Companies
- Meta: Investing heavily in AI with significant GPU investments
- Amazon: Offers diverse data analyst roles with complex responsibilities
- Google (Alphabet): Leverages massive data ecosystems
- JP Morgan Chase & Co.: Strong focus on data-driven banking transformation
## Specialized Data Analytics Firms
- Tiger Analytics: Specializes in AI/ML solutions
- SG Analytics: Provides data-driven insights
- Monte Carlo Data: Focuses on data observability
- CB Insights: Excels in market intelligence
## Emerging Opportunities
Companies like Samsara, ScienceSoft, and Forage are also actively recruiting data analysts, offering competitive salaries ranging from $85,000 to $207,000 annually.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://news.1rj.ru/str/DataSimplifier
Like this post for if you want me to continue the interview series 👍♥️
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👍19❤9
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 👇
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 :)
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 :)
👍25❤12🔥4
Data Analytics
Day 25: Backup and Restore Strategies in SQL (Good to know concept) 1. Why Are Backups Important? Backups protect your database from accidental deletions, hardware failures, or cyberattacks. A good backup strategy ensures minimal downtime and data recovery…
Day 26: Pivoting and Unpivoting Data in SQL
1. What is Pivoting in SQL?
Pivoting converts row-based data into columns to create a structured report. It's commonly used in reporting and summarization.
2. How to Pivot Data?
Example: You have a sales table with columns Month, Product, and Sales.
If you want to convert product names into columns and show total sales per month, use:
3. What is Unpivoting in SQL?
Unpivoting converts columns back into rows, which is useful for normalizing data.
Example: If you have sales data stored in separate columns (Shoes, Shirts), but you need a column named Product instead, use:
4. When to Use Pivot and Unpivot?
Pivot when you need structured reports with categories as columns.
Unpivot when working with dynamic columns and data normalization.
✅ Action Plan for Today:
1️⃣ Write a PIVOT query for summarizing data.
2️⃣ Use UNPIVOT to transform columns into rows.
3️⃣ Experiment with SUM, COUNT, and AVG while pivoting data.
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1. What is Pivoting in SQL?
Pivoting converts row-based data into columns to create a structured report. It's commonly used in reporting and summarization.
2. How to Pivot Data?
Example: You have a sales table with columns Month, Product, and Sales.
If you want to convert product names into columns and show total sales per month, use:
SELECT Month, SUM(CASE WHEN Product = 'Shoes' THEN Sales ELSE 0 END) AS Shoes, SUM(CASE WHEN Product = 'Shirts' THEN Sales ELSE 0 END) AS Shirts FROM Sales GROUP BY Month;
3. What is Unpivoting in SQL?
Unpivoting converts columns back into rows, which is useful for normalizing data.
Example: If you have sales data stored in separate columns (Shoes, Shirts), but you need a column named Product instead, use:
SELECT Month, Product, Sales FROM SalesTable UNPIVOT (Sales FOR Product IN (Shoes, Shirts)) AS unpvt;
4. When to Use Pivot and Unpivot?
Pivot when you need structured reports with categories as columns.
Unpivot when working with dynamic columns and data normalization.
✅ Action Plan for Today:
1️⃣ Write a PIVOT query for summarizing data.
2️⃣ Use UNPIVOT to transform columns into rows.
3️⃣ Experiment with SUM, COUNT, and AVG while pivoting data.
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👍18❤13🥰1
Data Analytics
Day 26: Pivoting and Unpivoting Data in SQL 1. What is Pivoting in SQL? Pivoting converts row-based data into columns to create a structured report. It's commonly used in reporting and summarization. 2. How to Pivot Data? Example: You have a sales table…
Day 27: Writing Stored Procedures and Functions in SQL
1. What Are Stored Procedures?
A Stored Procedure is a reusable block of SQL code that executes multiple SQL statements in a single call. It improves performance, security, and maintainability.
2. Creating a Stored Procedure
📌 Basic Syntax
📌 Executing the Procedure
📌 Stored Procedure with Parameters
📌 Executing with a Parameter
3. What Are SQL Functions?
Functions return a single value and are used inside queries. Unlike stored procedures, functions cannot modify the database.
📌 Creating a Function
📌 Calling the Function
4. Differences: Stored Procedures vs Functions
✔ Stored Procedures: Perform multiple actions, support transactions, and can modify data.
✔ Functions: Return a value, are used inside queries, and cannot change database state.
✅ Action Plan for Today:
1️⃣ Create a Stored Procedure that retrieves filtered data.
2️⃣ Write a Function that calculates an aggregate value.
3️⃣ Compare performance differences between functions and stored procedures.
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1. What Are Stored Procedures?
A Stored Procedure is a reusable block of SQL code that executes multiple SQL statements in a single call. It improves performance, security, and maintainability.
2. Creating a Stored Procedure
📌 Basic Syntax
CREATE PROCEDURE GetEmployeeDetails AS BEGIN SELECT * FROM Employees; END;
📌 Executing the Procedure
EXEC GetEmployeeDetails;
📌 Stored Procedure with Parameters
CREATE PROCEDURE GetEmployeeByID (@EmpID INT) AS BEGIN SELECT * FROM Employees WHERE EmployeeID = @EmpID; END;
📌 Executing with a Parameter
EXEC GetEmployeeByID 101;
3. What Are SQL Functions?
Functions return a single value and are used inside queries. Unlike stored procedures, functions cannot modify the database.
📌 Creating a Function
CREATE FUNCTION GetTotalSalary() RETURNS INT AS BEGIN DECLARE @TotalSalary INT; SELECT @TotalSalary = SUM(Salary) FROM Employees; RETURN @TotalSalary; END;
📌 Calling the Function
SELECT dbo.GetTotalSalary();
4. Differences: Stored Procedures vs Functions
✔ Stored Procedures: Perform multiple actions, support transactions, and can modify data.
✔ Functions: Return a value, are used inside queries, and cannot change database state.
✅ Action Plan for Today:
1️⃣ Create a Stored Procedure that retrieves filtered data.
2️⃣ Write a Function that calculates an aggregate value.
3️⃣ Compare performance differences between functions and stored procedures.
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/sqlanalyst
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Data Analytics
Data Analyst Interview Series Part-3 11. What is the difference between UNION and UNION ALL in SQL? Answer: UNION combines results from two queries and removes duplicates. UNION ALL combines results but keeps duplicates for better performance. Example:…
Data Analyst Interview Part-4
16. What is the difference between OLTP and OLAP?
Answer:
OLTP (Online Transaction Processing): Handles real-time, transactional data (e.g., banking systems, e-commerce). Focuses on fast inserts, updates, and deletes.
OLAP (Online Analytical Processing): Used for complex queries, reporting, and business intelligence (e.g., data warehouses, dashboards). Optimized for data retrieval.
Example:
An OLTP system records a customer's purchase in an online store.
An OLAP system analyzes total sales trends for different products over time.
17. What is the difference between RANK(), DENSE_RANK(), and ROW_NUMBER() in SQL?
Answer:
These functions assign ranks to rows in a result set:
RANK(): Assigns a rank but skips numbers if there are duplicates.
DENSE_RANK(): Assigns a rank without skipping numbers.
ROW_NUMBER(): Assigns a unique row number to each row, even if values are the same.
Example:
18. What are Measures and Dimensions in Tableau?
Answer:
Measures: Numeric values that can be aggregated (e.g., Sales, Profit, Quantity).
Dimensions: Categorical fields that define data granularity (e.g., Product, Region, Date).
Example:
"Sales" is a Measure (sum of sales).
"Customer Name" is a Dimension (used to group data).
19. How do you remove outliers from a dataset in Python?
Answer:
Outliers can be removed using statistical methods:
Using IQR (Interquartile Range) Method:
Using Z-Score Method:
20. What is the difference between INNER JOIN and LEFT JOIN?
Answer:
INNER JOIN returns only matching records from both tables.
LEFT JOIN returns all records from the left table and matching records from the right table (fills NULL if no match).
Example:
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16. What is the difference between OLTP and OLAP?
Answer:
OLTP (Online Transaction Processing): Handles real-time, transactional data (e.g., banking systems, e-commerce). Focuses on fast inserts, updates, and deletes.
OLAP (Online Analytical Processing): Used for complex queries, reporting, and business intelligence (e.g., data warehouses, dashboards). Optimized for data retrieval.
Example:
An OLTP system records a customer's purchase in an online store.
An OLAP system analyzes total sales trends for different products over time.
17. What is the difference between RANK(), DENSE_RANK(), and ROW_NUMBER() in SQL?
Answer:
These functions assign ranks to rows in a result set:
RANK(): Assigns a rank but skips numbers if there are duplicates.
DENSE_RANK(): Assigns a rank without skipping numbers.
ROW_NUMBER(): Assigns a unique row number to each row, even if values are the same.
Example:
SELECT Employee, Salary, RANK() OVER (ORDER BY Salary DESC) AS Rank, DENSE_RANK() OVER (ORDER BY Salary DESC) AS DenseRank, ROW_NUMBER() OVER (ORDER BY Salary DESC) AS RowNum FROM Employees;
18. What are Measures and Dimensions in Tableau?
Answer:
Measures: Numeric values that can be aggregated (e.g., Sales, Profit, Quantity).
Dimensions: Categorical fields that define data granularity (e.g., Product, Region, Date).
Example:
"Sales" is a Measure (sum of sales).
"Customer Name" is a Dimension (used to group data).
19. How do you remove outliers from a dataset in Python?
Answer:
Outliers can be removed using statistical methods:
Using IQR (Interquartile Range) Method:
import pandas as pd import numpy as np Q1 = df["Sales"].quantile(0.25) Q3 = df["Sales"].quantile(0.75) IQR = Q3 - Q1 df_cleaned = df[(df["Sales"] >= Q1 - 1.5*IQR) & (df["Sales"] <= Q3 + 1.5*IQR)]
Using Z-Score Method:
from scipy import stats df_cleaned = df[(np.abs(stats.zscore(df["Sales"])) < 3)]
20. What is the difference between INNER JOIN and LEFT JOIN?
Answer:
INNER JOIN returns only matching records from both tables.
LEFT JOIN returns all records from the left table and matching records from the right table (fills NULL if no match).
Example:
-- INNER JOIN: Returns only matching Customers with Orders SELECT Customers.Name, Orders.OrderID FROM Customers INNER JOIN Orders ON Customers.CustomerID = Orders.CustomerID; -- LEFT JOIN: Returns all Customers, even if they have no Orders SELECT Customers.Name, Orders.OrderID FROM Customers LEFT JOIN Orders ON Customers.CustomerID = Orders.CustomerID;
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Data Analytics
Day 27: Writing Stored Procedures and Functions in SQL 1. What Are Stored Procedures? A Stored Procedure is a reusable block of SQL code that executes multiple SQL statements in a single call. It improves performance, security, and maintainability. 2. Creating…
Day 28: Integrating SQL with Other Tools (Python, Power BI, Tableau) & SQL in Big Data
1. Using SQL with Python
Python is widely used to interact with databases via libraries like sqlite3, SQLAlchemy, and pandas.
📌 Example: Connecting to a Database in Python
2. Using SQL with Power BI
Power BI allows direct SQL connections for data visualization.
📌 Steps to Connect SQL to Power BI:
1️⃣ Open Power BI Desktop.
2️⃣ Click on Get Data → Select SQL Server.
3️⃣ Enter Server Name & Database Name.
4️⃣ Choose DirectQuery or Import Mode.
5️⃣ Load and create visualizations using SQL queries.
3. Using SQL with Tableau
Tableau connects with SQL databases to create interactive dashboards.
📌 Steps to Connect SQL to Tableau:
1️⃣ Open Tableau → Click Connect to Data.
2️⃣ Choose Microsoft SQL Server, MySQL, or PostgreSQL.
3️⃣ Enter Database Credentials.
4️⃣ Use SQL queries to fetch data and build charts & graphs.
4. SQL in Big Data (Introduction to NoSQL)
SQL is not always suitable for big data processing. NoSQL databases like MongoDB, Cassandra, and Hadoop are used for scalable, unstructured data.
📌 SQL vs NoSQL:
✔ SQL: Structured data, strict schema, ACID compliance (e.g., MySQL, PostgreSQL).
✔ NoSQL: Flexible schema, distributed storage, better for big data (e.g., MongoDB, Cassandra).
✅ Action Plan for Today:
1️⃣ Try running a SQL query in Python.
2️⃣ Connect a SQL database to Power BI/Tableau.
3️⃣ Research the difference between SQL and NoSQL for big data.
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/sqlanalyst
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1. Using SQL with Python
Python is widely used to interact with databases via libraries like sqlite3, SQLAlchemy, and pandas.
📌 Example: Connecting to a Database in Python
import sqlite3 # Connect to the database conn = sqlite3.connect('my_database.db') cursor = conn.cursor() # Execute a query cursor.execute("SELECT * FROM Employees") rows = cursor.fetchall() # Print results for row in rows: print(row) conn.close()2. Using SQL with Power BI
Power BI allows direct SQL connections for data visualization.
📌 Steps to Connect SQL to Power BI:
1️⃣ Open Power BI Desktop.
2️⃣ Click on Get Data → Select SQL Server.
3️⃣ Enter Server Name & Database Name.
4️⃣ Choose DirectQuery or Import Mode.
5️⃣ Load and create visualizations using SQL queries.
3. Using SQL with Tableau
Tableau connects with SQL databases to create interactive dashboards.
📌 Steps to Connect SQL to Tableau:
1️⃣ Open Tableau → Click Connect to Data.
2️⃣ Choose Microsoft SQL Server, MySQL, or PostgreSQL.
3️⃣ Enter Database Credentials.
4️⃣ Use SQL queries to fetch data and build charts & graphs.
4. SQL in Big Data (Introduction to NoSQL)
SQL is not always suitable for big data processing. NoSQL databases like MongoDB, Cassandra, and Hadoop are used for scalable, unstructured data.
📌 SQL vs NoSQL:
✔ SQL: Structured data, strict schema, ACID compliance (e.g., MySQL, PostgreSQL).
✔ NoSQL: Flexible schema, distributed storage, better for big data (e.g., MongoDB, Cassandra).
✅ Action Plan for Today:
1️⃣ Try running a SQL query in Python.
2️⃣ Connect a SQL database to Power BI/Tableau.
3️⃣ Research the difference between SQL and NoSQL for big data.
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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Data analyst interview questions Part-5:
21. What are Window Functions in SQL?
Window functions perform calculations across a set of rows related to the current row without collapsing the dataset like GROUP BY.
Common window functions:
RANK() – Assigns a rank with gaps for ties.
DENSE_RANK() – Assigns a rank without gaps.
ROW_NUMBER() – Assigns a unique row number.
LEAD() / LAG() – Access next/previous row values.
Example:
22. How do you create a calculated column in Power BI?
A calculated column is a new column created using DAX formulas.
Example:
Steps in Power BI:
Open Power BI Desktop.
Go to the Modeling tab → Click New Column.
Enter the DAX formula and press Enter.
23. How do you find duplicate values in Excel?
Methods to identify duplicates:
Conditional Formatting:
Select data → Click Home > Conditional Formatting > Highlight Duplicates.
Using COUNTIF formula:
Using Power Query:
Load data into Power Query → Use Group By to count duplicates.
24. What is the difference between a Line Chart and an Area Chart?
Line Chart: Shows trends over time using a continuous line.
Area Chart: Similar to a Line Chart but fills the area below the line with color, emphasizing volume.
Example:
A Line Chart shows monthly stock prices over time.
An Area Chart shows cumulative sales trends over time.
25. How do you merge two DataFrames in Pandas?
You can use merge() for SQL-like joins:
Common merge types:
how="inner" → Returns only matching rows.
how="left" → Keeps all rows from the left DataFrame.
how="right" → Keeps all rows from the right DataFrame.
how="outer" → Returns all rows, filling missing values with NaN.
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21. What are Window Functions in SQL?
Window functions perform calculations across a set of rows related to the current row without collapsing the dataset like GROUP BY.
Common window functions:
RANK() – Assigns a rank with gaps for ties.
DENSE_RANK() – Assigns a rank without gaps.
ROW_NUMBER() – Assigns a unique row number.
LEAD() / LAG() – Access next/previous row values.
Example:
SELECT Employee, Department, Salary, RANK() OVER (PARTITION BY Department ORDER BY Salary DESC) AS Rank FROM Employees;
22. How do you create a calculated column in Power BI?
A calculated column is a new column created using DAX formulas.
Example:
Profit Margin = Sales[Profit] / Sales[Revenue] Steps in Power BI:
Open Power BI Desktop.
Go to the Modeling tab → Click New Column.
Enter the DAX formula and press Enter.
23. How do you find duplicate values in Excel?
Methods to identify duplicates:
Conditional Formatting:
Select data → Click Home > Conditional Formatting > Highlight Duplicates.
Using COUNTIF formula:
=IF(COUNTIF(A:A, A2) > 1, "Duplicate", "Unique") Using Power Query:
Load data into Power Query → Use Group By to count duplicates.
24. What is the difference between a Line Chart and an Area Chart?
Line Chart: Shows trends over time using a continuous line.
Area Chart: Similar to a Line Chart but fills the area below the line with color, emphasizing volume.
Example:
A Line Chart shows monthly stock prices over time.
An Area Chart shows cumulative sales trends over time.
25. How do you merge two DataFrames in Pandas?
You can use merge() for SQL-like joins:
import pandas as pd df1 = pd.DataFrame({"ID": [1, 2, 3], "Name": ["Alice", "Bob", "Charlie"]}) df2 = pd.DataFrame({"ID": [1, 2, 4], "Salary": [50000, 60000, 70000]}) # INNER JOIN df_inner = df1.merge(df2, on="ID", how="inner") # LEFT JOIN df_left = df1.merge(df2, on="ID", how="left") print(df_inner) Common merge types:
how="inner" → Returns only matching rows.
how="left" → Keeps all rows from the left DataFrame.
how="right" → Keeps all rows from the right DataFrame.
how="outer" → Returns all rows, filling missing values with NaN.
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👍21❤5
Data Analytics
Day 28: Integrating SQL with Other Tools (Python, Power BI, Tableau) & SQL in Big Data 1. Using SQL with Python Python is widely used to interact with databases via libraries like sqlite3, SQLAlchemy, and pandas. 📌 Example: Connecting to a Database in Python…
Day 29: Query Performance Tuning – Optimize SQL Queries
1. Why Optimize SQL Queries?
Efficient queries reduce execution time, improve database performance, and minimize resource usage.
2. Key Techniques for Query Optimization
📌 1. Use Indexes to Speed Up Searches
Indexes improve query performance by reducing the number of scanned rows.
✅ Example: Creating an Index
✅ Check Existing Indexes
📌 2. Avoid SELECT * (Specify Columns Instead)
Fetching all columns increases memory usage and slows down queries.
❌ Bad Query:
✅ Optimized Query:
📌 3. Use WHERE Instead of HAVING for Filtering
WHERE filters before grouping, while HAVING filters after aggregation, making WHERE more efficient.
❌ Bad Query:
✅ Optimized Query:
📌 4. Use EXISTS Instead of IN for Large Datasets
EXISTS stops searching after the first match, whereas IN scans the entire list.
❌ Bad Query:
✅ Optimized Query:
📌 5. Optimize JOINS by Selecting Required Columns
Avoid unnecessary columns and filters in JOIN queries.
❌ Bad Query:
✅ Optimized Query:
✅ Action Plan for Today:
1️⃣ Create an index for a frequently searched column.
2️⃣ Rewrite a query to avoid SELECT *.
3️⃣ Experiment with EXISTS vs IN for filtering data.
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/sqlanalyst
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1. Why Optimize SQL Queries?
Efficient queries reduce execution time, improve database performance, and minimize resource usage.
2. Key Techniques for Query Optimization
📌 1. Use Indexes to Speed Up Searches
Indexes improve query performance by reducing the number of scanned rows.
✅ Example: Creating an Index
CREATE INDEX idx_employee_name ON Employees(Name);
✅ Check Existing Indexes
SELECT * FROM sys.indexes WHERE object_id = OBJECT_ID('Employees'); 📌 2. Avoid SELECT * (Specify Columns Instead)
Fetching all columns increases memory usage and slows down queries.
❌ Bad Query:
SELECT * FROM Employees; ✅ Optimized Query:
SELECT Name, Salary FROM Employees; 📌 3. Use WHERE Instead of HAVING for Filtering
WHERE filters before grouping, while HAVING filters after aggregation, making WHERE more efficient.
❌ Bad Query:
SELECT Department, COUNT(*) FROM Employees GROUP BY Department HAVING COUNT(*) > 5;
✅ Optimized Query:
SELECT Department, COUNT(*) FROM Employees WHERE Department IS NOT NULL GROUP BY Department;
📌 4. Use EXISTS Instead of IN for Large Datasets
EXISTS stops searching after the first match, whereas IN scans the entire list.
❌ Bad Query:
SELECT * FROM Employees WHERE DepartmentID IN (SELECT DepartmentID FROM Departments);
✅ Optimized Query:
SELECT * FROM Employees WHERE EXISTS (SELECT 1 FROM Departments WHERE Departments.DepartmentID = Employees.DepartmentID);
📌 5. Optimize JOINS by Selecting Required Columns
Avoid unnecessary columns and filters in JOIN queries.
❌ Bad Query:
SELECT * FROM Employees e JOIN Departments d ON e.DepartmentID = d.DepartmentID;
✅ Optimized Query:
SELECT e.Name, d.DepartmentName FROM Employees e JOIN Departments d ON e.DepartmentID = d.DepartmentID;
✅ Action Plan for Today:
1️⃣ Create an index for a frequently searched column.
2️⃣ Rewrite a query to avoid SELECT *.
3️⃣ Experiment with EXISTS vs IN for filtering data.
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/sqlanalyst
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👍16❤6🎉2
There’s one thing in common that Data Analysts did to land their first job
They never gave up
When things get tough and burnout starts to creep
- Take a small break (but get back into it)
- Don’t use the same applying strategies (switch it up)
- Understand you’re playing the long game
Don’t waste months of learning just to give up at the finish line
I have curated free Data Analytics Resources 👇👇
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Hope this helps you 😊
They never gave up
When things get tough and burnout starts to creep
- Take a small break (but get back into it)
- Don’t use the same applying strategies (switch it up)
- Understand you’re playing the long game
Don’t waste months of learning just to give up at the finish line
I have curated free Data Analytics Resources 👇👇
https://news.1rj.ru/str/DataSimplifier
Hope this helps you 😊
❤6👍4
You don't need to know everything about every data tool. Focus on what will help land you your job.
For Excel:
- IFS (all variations)
- XLOOKUP
- IMPORTRANGE (in GSheets)
- Pivot Tables
- Dynamic functions like TODAY()
For SQL:
- Sum
- Group By
- Window Functions
- CTEs
- Joins
For Tableau:
- Calculated Columns
- Sets
- Groups
- Formatting
For Power BI:
- Power Query for data transformation
- DAX (Data Analysis Expressions) for creating custom calculations
- Relationships between tables
- Creating interactive and dynamic dashboards
- Utilizing slicers and filters effectively
You can now find Data Analytics Resources on WhatsApp 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you 😊
For Excel:
- IFS (all variations)
- XLOOKUP
- IMPORTRANGE (in GSheets)
- Pivot Tables
- Dynamic functions like TODAY()
For SQL:
- Sum
- Group By
- Window Functions
- CTEs
- Joins
For Tableau:
- Calculated Columns
- Sets
- Groups
- Formatting
For Power BI:
- Power Query for data transformation
- DAX (Data Analysis Expressions) for creating custom calculations
- Relationships between tables
- Creating interactive and dynamic dashboards
- Utilizing slicers and filters effectively
You can now find Data Analytics Resources on WhatsApp 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you 😊
❤15👍7👏4🔥2🥰2
If you have time to learn...!
You have time to clean...!
Start from Scratch that !!!!
You have time to become a Data Analyst...!!
➜ learn Excel
➜ learn SQL
➜ learn either Power BI or Tableau
➜ learn what the heck ATS is and how to get around it
➜ learn to be ready for any interview question
➜ to build projects for a portfolio
➜ to put invest the time for your future
➜ to fail and pick yourself back up
And you don't need to do it all at once!
You can now find Data Analytics Resources on WhatsApp as well
👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you 😊
You have time to clean...!
Start from Scratch that !!!!
You have time to become a Data Analyst...!!
➜ learn Excel
➜ learn SQL
➜ learn either Power BI or Tableau
➜ learn what the heck ATS is and how to get around it
➜ learn to be ready for any interview question
➜ to build projects for a portfolio
➜ to put invest the time for your future
➜ to fail and pick yourself back up
And you don't need to do it all at once!
You can now find Data Analytics Resources on WhatsApp as well
👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you 😊
❤11👍9
Data Analytics
Day 29: Query Performance Tuning – Optimize SQL Queries 1. Why Optimize SQL Queries? Efficient queries reduce execution time, improve database performance, and minimize resource usage. 2. Key Techniques for Query Optimization 📌 1. Use Indexes to Speed…
Day 30: Final Review & SQL Projects
1. Recap of Key Topics
✔ Week 1 (SQL Basics): SELECT, WHERE, ORDER BY, Aggregations, GROUP BY
✔ Week 2 (Intermediate SQL): JOINS, Subqueries, String & Date Functions, UNION
✔ Week 3 (Advanced SQL): CTEs, Window Functions, Transactions, Indexing
✔ Week 4 (Database Management): Constraints, Performance Tuning, Stored Procedures
2. SQL Mini Projects (Hands-On Practice)
📌 Project 1: Employee Database Analysis
👉 Skills Used: Joins, Aggregations, Window Functions
🔹 Find the top 5 highest-paid employees in each department.
🔹 Calculate the average salary per department using GROUP BY.
✅ Example Query:
📌 Project 2: E-Commerce Sales Insights
👉 Skills Used: Joins, Date Functions, Subqueries
🔹 Find the total revenue generated in the last 6 months.
🔹 Identify the top-selling products.
✅ Example Query:
📌 Project 3: Customer Retention Analysis
👉 Skills Used: CTEs, Window Functions, Recursive Queries
🔹 Identify customers who made repeat purchases.
🔹 Find the time gap between first and last purchase.
✅ Example Query:
3. What’s Next?
🚀 Continue Improving: Solve problems on LeetCode, StrataScratch, SQLZoo & 365datascience
📈 Build Projects: Create a portfolio with real-world datasets
📚 Learn Advanced Topics: Explore Data Warehousing, BigQuery, NoSQL
🎉 Congratulations on Completing the 30-Day SQL Challenge! 🎉
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Hope it helps :)
1. Recap of Key Topics
✔ Week 1 (SQL Basics): SELECT, WHERE, ORDER BY, Aggregations, GROUP BY
✔ Week 2 (Intermediate SQL): JOINS, Subqueries, String & Date Functions, UNION
✔ Week 3 (Advanced SQL): CTEs, Window Functions, Transactions, Indexing
✔ Week 4 (Database Management): Constraints, Performance Tuning, Stored Procedures
2. SQL Mini Projects (Hands-On Practice)
📌 Project 1: Employee Database Analysis
👉 Skills Used: Joins, Aggregations, Window Functions
🔹 Find the top 5 highest-paid employees in each department.
🔹 Calculate the average salary per department using GROUP BY.
✅ Example Query:
SELECT DepartmentID, Name, Salary,
RANK() OVER(PARTITION BY DepartmentID ORDER BY Salary DESC) AS SalaryRank
FROM Employees;
📌 Project 2: E-Commerce Sales Insights
👉 Skills Used: Joins, Date Functions, Subqueries
🔹 Find the total revenue generated in the last 6 months.
🔹 Identify the top-selling products.
✅ Example Query:
SELECT ProductID, SUM(TotalAmount) AS TotalSales
FROM Orders
WHERE OrderDate >= DATEADD(MONTH, -6, GETDATE())
GROUP BY ProductID
ORDER BY TotalSales DESC;
📌 Project 3: Customer Retention Analysis
👉 Skills Used: CTEs, Window Functions, Recursive Queries
🔹 Identify customers who made repeat purchases.
🔹 Find the time gap between first and last purchase.
✅ Example Query:
WITH CustomerOrders AS (
SELECT CustomerID, OrderDate,
RANK() OVER (PARTITION BY CustomerID ORDER BY OrderDate ASC) AS FirstOrder
FROM Orders
)
SELECT CustomerID, MIN(OrderDate) AS FirstPurchase, MAX(OrderDate) AS LastPurchase
FROM CustomerOrders
GROUP BY CustomerID;
3. What’s Next?
🚀 Continue Improving: Solve problems on LeetCode, StrataScratch, SQLZoo & 365datascience
📈 Build Projects: Create a portfolio with real-world datasets
📚 Learn Advanced Topics: Explore Data Warehousing, BigQuery, NoSQL
🎉 Congratulations on Completing the 30-Day SQL Challenge! 🎉
If you found this useful, like this post and share it with your friends!
Here you can find SQL Interview Resources👇
https://news.1rj.ru/str/sqlanalyst
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤16👍12
Data Analyst Roadmap:
- Tier 1: Learn Excel & SQL
- Tier 2: Data Cleaning & Exploratory Data Analysis (EDA)
- Tier 3: Data Visualization & Business Intelligence (BI) Tools
- Tier 4: Statistical Analysis & Machine Learning Basics
Then build projects that include:
- Data Collection
- Data Cleaning
- Data Analysis
- Data Visualization
And if you want to make your portfolio stand out more:
- Solve real business problems
- Provide clear, impactful insights
- Create a presentation
- Record a video presentation
- Target specific industries
- Reach out to companies
Hope this helps you 😊
- Tier 1: Learn Excel & SQL
- Tier 2: Data Cleaning & Exploratory Data Analysis (EDA)
- Tier 3: Data Visualization & Business Intelligence (BI) Tools
- Tier 4: Statistical Analysis & Machine Learning Basics
Then build projects that include:
- Data Collection
- Data Cleaning
- Data Analysis
- Data Visualization
And if you want to make your portfolio stand out more:
- Solve real business problems
- Provide clear, impactful insights
- Create a presentation
- Record a video presentation
- Target specific industries
- Reach out to companies
Hope this helps you 😊
👍11❤8
Data Analytics
Data analyst interview questions Part-5: 21. What are Window Functions in SQL? Window functions perform calculations across a set of rows related to the current row without collapsing the dataset like GROUP BY. Common window functions: RANK() – Assigns…
Data Analyst Interview Series Part-6
What is the difference between COUNT(), COUNT(*), and COUNT(DISTINCT) in SQL?
COUNT(column_name): Counts non-null values in a specific column.
COUNT(*): Counts all rows, including NULL values.
COUNT(DISTINCT column_name): Counts unique non-null values in a column.
Example:
What are the different types of filters in Power BI?
Power BI provides several types of filters:
Visual-level filters: Apply to a single visual.
Page-level filters: Apply to all visuals on a report page.
Report-level filters: Apply to the entire report.
Drillthrough filters: Allow focusing on specific details by navigating to another report page.
Top N filters: Show only the top N values based on a measure.
Example: Using a Top N filter to show the top 5 performing products in sales.
How do you use the VLOOKUP function in Excel?
VLOOKUP searches for a value in the first column of a range and returns a corresponding value from another column.
Syntax:
Example: To find an employee’s department based on their ID:
101 → Value to search for
A2:C10 → Table range
2 → Column number to return data from
FALSE → Exact match
What is the difference between a Bar Chart and a Column Chart?
Bar Chart: Uses horizontal bars, suitable for comparing categories.
Column Chart: Uses vertical bars, good for showing trends over time.
Example:
A Bar Chart is useful for comparing sales across regions.
A Column Chart is useful for showing monthly revenue growth.
How do you handle missing data in Pandas?
Pandas provides multiple ways to handle missing data:
Remove missing values:
Fill missing values with a default value:
Fill missing values with the column mean:
Forward fill (copy previous value):
Backward fill (copy next value):
These methods ensure data quality while preventing errors in analysis.
Like this post for if you want me to continue the interview series 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
What is the difference between COUNT(), COUNT(*), and COUNT(DISTINCT) in SQL?
COUNT(column_name): Counts non-null values in a specific column.
COUNT(*): Counts all rows, including NULL values.
COUNT(DISTINCT column_name): Counts unique non-null values in a column.
Example:
SELECT COUNT(salary) FROM employees; -- Counts non-null salaries SELECT COUNT(*) FROM employees; -- Counts all rows SELECT COUNT(DISTINCT department) FROM employees; -- Counts unique departments
What are the different types of filters in Power BI?
Power BI provides several types of filters:
Visual-level filters: Apply to a single visual.
Page-level filters: Apply to all visuals on a report page.
Report-level filters: Apply to the entire report.
Drillthrough filters: Allow focusing on specific details by navigating to another report page.
Top N filters: Show only the top N values based on a measure.
Example: Using a Top N filter to show the top 5 performing products in sales.
How do you use the VLOOKUP function in Excel?
VLOOKUP searches for a value in the first column of a range and returns a corresponding value from another column.
Syntax:
=VLOOKUP(lookup_value, table_array, col_index_num, [range_lookup]) Example: To find an employee’s department based on their ID:
=VLOOKUP(101, A2:C10, 2, FALSE) 101 → Value to search for
A2:C10 → Table range
2 → Column number to return data from
FALSE → Exact match
What is the difference between a Bar Chart and a Column Chart?
Bar Chart: Uses horizontal bars, suitable for comparing categories.
Column Chart: Uses vertical bars, good for showing trends over time.
Example:
A Bar Chart is useful for comparing sales across regions.
A Column Chart is useful for showing monthly revenue growth.
How do you handle missing data in Pandas?
Pandas provides multiple ways to handle missing data:
Remove missing values:
df.dropna() Fill missing values with a default value:
df.fillna(0) Fill missing values with the column mean:
df['Salary'].fillna(df['Salary'].mean(), inplace=True) Forward fill (copy previous value):
df.fillna(method='ffill') Backward fill (copy next value):
df.fillna(method='bfill') These methods ensure data quality while preventing errors in analysis.
Like this post for if you want me to continue the interview series 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
👍15❤7
As a data analytics enthusiast, the end goal is not just to learn SQL, Power BI, Python, Excel, etc. but to get a job as a Data Analyst👨💻
Back then, when I was trying to switch my career into data analytics, I used to keep aside 1:00-1:30 hours of my day aside so that I can utilize those hours to search for job openings related to Data analytics and Business Intelligence.
Before going to bed, I used to utilize the first 30 minutes by going through various job portals such as naukri, LinkedIn, etc to find relevant openings and next 1 hour by collecting the keywords from the job denoscription to curate the resume accordingly and searching for profile of people who can refer me for the role.
📍 I will advise every aspiring data analyst to have a dedicated timing for searching and applying for the jobs.
📍To get into data analytics, applying for jobs is as important as learning and upskilling.
If you are not applying for the jobs, you are simply delaying your success to get into data analytics👨💻📊
Hope this helps you 😊
Back then, when I was trying to switch my career into data analytics, I used to keep aside 1:00-1:30 hours of my day aside so that I can utilize those hours to search for job openings related to Data analytics and Business Intelligence.
Before going to bed, I used to utilize the first 30 minutes by going through various job portals such as naukri, LinkedIn, etc to find relevant openings and next 1 hour by collecting the keywords from the job denoscription to curate the resume accordingly and searching for profile of people who can refer me for the role.
📍 I will advise every aspiring data analyst to have a dedicated timing for searching and applying for the jobs.
📍To get into data analytics, applying for jobs is as important as learning and upskilling.
If you are not applying for the jobs, you are simply delaying your success to get into data analytics👨💻📊
Hope this helps you 😊
👍20❤8👎1