SQL Interview Questions with Answers
1. How to change a table name in SQL?
This is the command to change a table name in SQL:
ALTER TABLE table_name
RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.
2. How to use LIKE in SQL?
The LIKE operator checks if an attribute value matches a given string pattern. Here is an example of LIKE operator
SELECT * FROM employees WHERE first_name like ‘Steven’;
With this command, we will be able to extract all the records where the first name is like “Steven”.
3. If we drop a table, does it also drop related objects like constraints, indexes, columns, default, views and sorted procedures?
Yes, SQL server drops all related objects, which exists inside a table like constraints, indexes, columns, defaults etc. But dropping a table will not drop views and sorted procedures as they exist outside the table.
4. Explain SQL Constraints.
SQL Constraints are used to specify the rules of data type in a table. They can be specified while creating and altering the table. The following are the constraints in SQL: NOT NULL CHECK DEFAULT UNIQUE PRIMARY KEY FOREIGN KEY
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1. How to change a table name in SQL?
This is the command to change a table name in SQL:
ALTER TABLE table_name
RENAME TO new_table_name;
We will start off by giving the keywords ALTER TABLE, then we will follow it up by giving the original name of the table, after that, we will give in the keywords RENAME TO and finally, we will give the new table name.
2. How to use LIKE in SQL?
The LIKE operator checks if an attribute value matches a given string pattern. Here is an example of LIKE operator
SELECT * FROM employees WHERE first_name like ‘Steven’;
With this command, we will be able to extract all the records where the first name is like “Steven”.
3. If we drop a table, does it also drop related objects like constraints, indexes, columns, default, views and sorted procedures?
Yes, SQL server drops all related objects, which exists inside a table like constraints, indexes, columns, defaults etc. But dropping a table will not drop views and sorted procedures as they exist outside the table.
4. Explain SQL Constraints.
SQL Constraints are used to specify the rules of data type in a table. They can be specified while creating and altering the table. The following are the constraints in SQL: NOT NULL CHECK DEFAULT UNIQUE PRIMARY KEY FOREIGN KEY
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SQL (Structured Query Language) is a standard programming language used to manage and manipulate relational databases. Here are some key concepts to understand the basics of SQL:
1. Database: A database is a structured collection of data organized in tables, which consist of rows and columns.
2. Table: A table is a collection of related data organized in rows and columns. Each row represents a record, and each column represents a specific attribute or field.
3. Query: A SQL query is a request for data or information from a database. Queries are used to retrieve, insert, update, or delete data in a database.
4. CRUD Operations: CRUD stands for Create, Read, Update, and Delete. These are the basic operations performed on data in a database using SQL:
- Create (INSERT): Adds new records to a table.
- Read (SELECT): Retrieves data from one or more tables.
- Update (UPDATE): Modifies existing records in a table.
- Delete (DELETE): Removes records from a table.
5. Data Types: SQL supports various data types to define the type of data that can be stored in each column of a table, such as integer, text, date, and decimal.
6. Constraints: Constraints are rules enforced on data columns to ensure data integrity and consistency. Common constraints include:
- Primary Key: Uniquely identifies each record in a table.
- Foreign Key: Establishes a relationship between two tables.
- Unique: Ensures that all values in a column are unique.
- Not Null: Specifies that a column cannot contain NULL values.
7. Joins: Joins are used to combine rows from two or more tables based on a related column between them. Common types of joins include INNER JOIN, LEFT JOIN (or LEFT OUTER JOIN), RIGHT JOIN (or RIGHT OUTER JOIN), and FULL JOIN (or FULL OUTER JOIN).
8. Aggregate Functions: SQL provides aggregate functions to perform calculations on sets of values. Common aggregate functions include SUM, AVG, COUNT, MIN, and MAX.
9. Group By: The GROUP BY clause is used to group rows that have the same values into summary rows. It is often used with aggregate functions to perform calculations on grouped data.
10. Order By: The ORDER BY clause is used to sort the result set of a query based on one or more columns in ascending or descending order.
Understanding these basic concepts of SQL will help you write queries to interact with databases effectively. Practice writing SQL queries and experimenting with different commands to become proficient in using SQL for database management and manipulation.
SQL Learning Series: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v/1075
1. Database: A database is a structured collection of data organized in tables, which consist of rows and columns.
2. Table: A table is a collection of related data organized in rows and columns. Each row represents a record, and each column represents a specific attribute or field.
3. Query: A SQL query is a request for data or information from a database. Queries are used to retrieve, insert, update, or delete data in a database.
4. CRUD Operations: CRUD stands for Create, Read, Update, and Delete. These are the basic operations performed on data in a database using SQL:
- Create (INSERT): Adds new records to a table.
- Read (SELECT): Retrieves data from one or more tables.
- Update (UPDATE): Modifies existing records in a table.
- Delete (DELETE): Removes records from a table.
5. Data Types: SQL supports various data types to define the type of data that can be stored in each column of a table, such as integer, text, date, and decimal.
6. Constraints: Constraints are rules enforced on data columns to ensure data integrity and consistency. Common constraints include:
- Primary Key: Uniquely identifies each record in a table.
- Foreign Key: Establishes a relationship between two tables.
- Unique: Ensures that all values in a column are unique.
- Not Null: Specifies that a column cannot contain NULL values.
7. Joins: Joins are used to combine rows from two or more tables based on a related column between them. Common types of joins include INNER JOIN, LEFT JOIN (or LEFT OUTER JOIN), RIGHT JOIN (or RIGHT OUTER JOIN), and FULL JOIN (or FULL OUTER JOIN).
8. Aggregate Functions: SQL provides aggregate functions to perform calculations on sets of values. Common aggregate functions include SUM, AVG, COUNT, MIN, and MAX.
9. Group By: The GROUP BY clause is used to group rows that have the same values into summary rows. It is often used with aggregate functions to perform calculations on grouped data.
10. Order By: The ORDER BY clause is used to sort the result set of a query based on one or more columns in ascending or descending order.
Understanding these basic concepts of SQL will help you write queries to interact with databases effectively. Practice writing SQL queries and experimenting with different commands to become proficient in using SQL for database management and manipulation.
SQL Learning Series: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v/1075
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✨The STAR method is a powerful technique used to answer behavioral interview questions effectively.
It helps structure responses by focusing on Situation, Task, Action, and Result. For analytics professionals, using the STAR method ensures that you demonstrate your problem-solving abilities, technical skills, and business acumen in a clear and concise way.
Here’s how the STAR method works, tailored for an analytics interview:
📍 1. Situation
Describe the context or challenge you faced. For analysts, this might be related to data challenges, business processes, or system inefficiencies. Be specific about the setting, whether it was a project, a recurring task, or a special initiative.
Example: “At my previous role as a data analyst at XYZ Company, we were experiencing a high churn rate among our subnoscription customers. This was a critical issue because it directly impacted revenue.”*
📍 2. Task
Explain the responsibilities you had or the goals you needed to achieve in that situation. In analytics, this usually revolves around diagnosing the problem, designing experiments, or conducting data analysis.
Example: “I was tasked with identifying the factors contributing to customer churn and providing actionable insights to the marketing team to help them improve retention.”*
📍 3. Action
Detail the specific actions you took to address the problem. Be sure to mention any tools, software, or methodologies you used (e.g., SQL, Python, data #visualization tools, #statistical #models). This is your opportunity to showcase your technical expertise and approach to problem-solving.
Example: “I collected and analyzed customer data using #SQL to extract key trends. I then used #Python for data cleaning and statistical analysis, focusing on engagement metrics, product usage patterns, and customer feedback. I also collaborated with the marketing and product teams to understand business priorities.”*
📍 4. Result
Highlight the outcome of your actions, especially any measurable impact. Quantify your results if possible, as this demonstrates your effectiveness as an analyst. Show how your analysis directly influenced business decisions or outcomes.
Example: “As a result of my analysis, we discovered that customers were disengaging due to a lack of certain product features. My insights led to a targeted marketing campaign and product improvements, reducing churn by 15% over the next quarter.”*
Example STAR Answer for an Analytics Interview Question:
Question: *"Tell me about a time you used data to solve a business problem."*
Answer (STAR format):
🔻*S*: “At my previous company, our sales team was struggling with inconsistent performance, and management wasn’t sure which factors were driving the variance.”
🔻*T*: “I was assigned the task of conducting a detailed analysis to identify key drivers of sales performance and propose data-driven recommendations.”
🔻*A*: “I began by collecting sales data over the past year and segmented it by region, product line, and sales representative. I then used Python for #statistical #analysis and developed a regression model to determine the key factors influencing sales outcomes. I also visualized the data using #Tableau to present the findings to non-technical stakeholders.”
🔻*R*: “The analysis revealed that product mix and regional seasonality were significant contributors to the variability. Based on my findings, the company adjusted their sales strategy, leading to a 20% increase in sales efficiency in the next quarter.”
Hope this helps you 😊
It helps structure responses by focusing on Situation, Task, Action, and Result. For analytics professionals, using the STAR method ensures that you demonstrate your problem-solving abilities, technical skills, and business acumen in a clear and concise way.
Here’s how the STAR method works, tailored for an analytics interview:
📍 1. Situation
Describe the context or challenge you faced. For analysts, this might be related to data challenges, business processes, or system inefficiencies. Be specific about the setting, whether it was a project, a recurring task, or a special initiative.
Example: “At my previous role as a data analyst at XYZ Company, we were experiencing a high churn rate among our subnoscription customers. This was a critical issue because it directly impacted revenue.”*
📍 2. Task
Explain the responsibilities you had or the goals you needed to achieve in that situation. In analytics, this usually revolves around diagnosing the problem, designing experiments, or conducting data analysis.
Example: “I was tasked with identifying the factors contributing to customer churn and providing actionable insights to the marketing team to help them improve retention.”*
📍 3. Action
Detail the specific actions you took to address the problem. Be sure to mention any tools, software, or methodologies you used (e.g., SQL, Python, data #visualization tools, #statistical #models). This is your opportunity to showcase your technical expertise and approach to problem-solving.
Example: “I collected and analyzed customer data using #SQL to extract key trends. I then used #Python for data cleaning and statistical analysis, focusing on engagement metrics, product usage patterns, and customer feedback. I also collaborated with the marketing and product teams to understand business priorities.”*
📍 4. Result
Highlight the outcome of your actions, especially any measurable impact. Quantify your results if possible, as this demonstrates your effectiveness as an analyst. Show how your analysis directly influenced business decisions or outcomes.
Example: “As a result of my analysis, we discovered that customers were disengaging due to a lack of certain product features. My insights led to a targeted marketing campaign and product improvements, reducing churn by 15% over the next quarter.”*
Example STAR Answer for an Analytics Interview Question:
Question: *"Tell me about a time you used data to solve a business problem."*
Answer (STAR format):
🔻*S*: “At my previous company, our sales team was struggling with inconsistent performance, and management wasn’t sure which factors were driving the variance.”
🔻*T*: “I was assigned the task of conducting a detailed analysis to identify key drivers of sales performance and propose data-driven recommendations.”
🔻*A*: “I began by collecting sales data over the past year and segmented it by region, product line, and sales representative. I then used Python for #statistical #analysis and developed a regression model to determine the key factors influencing sales outcomes. I also visualized the data using #Tableau to present the findings to non-technical stakeholders.”
🔻*R*: “The analysis revealed that product mix and regional seasonality were significant contributors to the variability. Based on my findings, the company adjusted their sales strategy, leading to a 20% increase in sales efficiency in the next quarter.”
Hope this helps you 😊
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Roadmap to become a data analyst
1. Foundation Skills:
•Strengthen Mathematics: Focus on statistics relevant to data analysis.
•Excel Basics: Master fundamental Excel functions and formulas.
2. SQL Proficiency:
•Learn SQL Basics: Understand SELECT statements, JOINs, and filtering.
•Practice Database Queries: Work with databases to retrieve and manipulate data.
3. Excel Advanced Techniques:
•Data Cleaning in Excel: Learn to handle missing data and outliers.
•PivotTables and PivotCharts: Master these powerful tools for data summarization.
4. Data Visualization with Excel:
•Create Visualizations: Learn to build charts and graphs in Excel.
•Dashboard Creation: Understand how to design effective dashboards.
5. Power BI Introduction:
•Install and Explore Power BI: Familiarize yourself with the interface.
•Import Data: Learn to import and transform data using Power BI.
6. Power BI Data Modeling:
•Relationships: Understand and establish relationships between tables.
•DAX (Data Analysis Expressions): Learn the basics of DAX for calculations.
7. Advanced Power BI Features:
•Advanced Visualizations: Explore complex visualizations in Power BI.
•Custom Measures and Columns: Utilize DAX for customized data calculations.
8. Integration of Excel, SQL, and Power BI:
•Importing Data from SQL to Power BI: Practice connecting and importing data.
•Excel and Power BI Integration: Learn how to use Excel data in Power BI.
9. Business Intelligence Best Practices:
•Data Storytelling: Develop skills in presenting insights effectively.
•Performance Optimization: Optimize reports and dashboards for efficiency.
10. Build a Portfolio:
•Showcase Excel Projects: Highlight your data analysis skills using Excel.
•Power BI Projects: Feature Power BI dashboards and reports in your portfolio.
11. Continuous Learning and Certification:
•Stay Updated: Keep track of new features in Excel, SQL, and Power BI.
•Consider Certifications: Obtain relevant certifications to validate your skills.
1. Foundation Skills:
•Strengthen Mathematics: Focus on statistics relevant to data analysis.
•Excel Basics: Master fundamental Excel functions and formulas.
2. SQL Proficiency:
•Learn SQL Basics: Understand SELECT statements, JOINs, and filtering.
•Practice Database Queries: Work with databases to retrieve and manipulate data.
3. Excel Advanced Techniques:
•Data Cleaning in Excel: Learn to handle missing data and outliers.
•PivotTables and PivotCharts: Master these powerful tools for data summarization.
4. Data Visualization with Excel:
•Create Visualizations: Learn to build charts and graphs in Excel.
•Dashboard Creation: Understand how to design effective dashboards.
5. Power BI Introduction:
•Install and Explore Power BI: Familiarize yourself with the interface.
•Import Data: Learn to import and transform data using Power BI.
6. Power BI Data Modeling:
•Relationships: Understand and establish relationships between tables.
•DAX (Data Analysis Expressions): Learn the basics of DAX for calculations.
7. Advanced Power BI Features:
•Advanced Visualizations: Explore complex visualizations in Power BI.
•Custom Measures and Columns: Utilize DAX for customized data calculations.
8. Integration of Excel, SQL, and Power BI:
•Importing Data from SQL to Power BI: Practice connecting and importing data.
•Excel and Power BI Integration: Learn how to use Excel data in Power BI.
9. Business Intelligence Best Practices:
•Data Storytelling: Develop skills in presenting insights effectively.
•Performance Optimization: Optimize reports and dashboards for efficiency.
10. Build a Portfolio:
•Showcase Excel Projects: Highlight your data analysis skills using Excel.
•Power BI Projects: Feature Power BI dashboards and reports in your portfolio.
11. Continuous Learning and Certification:
•Stay Updated: Keep track of new features in Excel, SQL, and Power BI.
•Consider Certifications: Obtain relevant certifications to validate your skills.
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𝐉𝐮𝐧𝐢𝐨𝐫 𝐯𝐬. 𝐒𝐞𝐧𝐢𝐨𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭
What’s the real difference between Junior and Senior Data Analyst?
It’s not just SQL skills or years on the job — it’s how they think.
📚Juniors code right away
🧠Seniors figure out the problem first
Example: Juniors query without asking, Seniors check the goal.
📚Juniors follow orders
🧠Seniors ask questions
Example: Juniors build blindly, Seniors confirm metrics.
📚Juniors patch data
🧠Seniors fix the source
Example: Juniors fill gaps, Seniors debug the ETL.
📚Juniors stall in chaos
🧠Seniors make a plan
Example: Juniors wait, Seniors step up.
📚Juniors focus on tasks
🧠Seniors see the big picture
Example: Juniors report, Seniors connect to goals.
📚Juniors guess
🧠Seniors clarify
Example: Juniors assume, Seniors ask the team.
📚Juniors stick to old tools
🧠Seniors try new ones
Example: Juniors love Excel, Seniors code in Python.
📚Juniors give data
🧠Seniors give insights
Example: Juniors share stats, Seniors spot trends.
Seniority is about mindset, not just time.
What’s the real difference between Junior and Senior Data Analyst?
It’s not just SQL skills or years on the job — it’s how they think.
📚Juniors code right away
🧠Seniors figure out the problem first
Example: Juniors query without asking, Seniors check the goal.
📚Juniors follow orders
🧠Seniors ask questions
Example: Juniors build blindly, Seniors confirm metrics.
📚Juniors patch data
🧠Seniors fix the source
Example: Juniors fill gaps, Seniors debug the ETL.
📚Juniors stall in chaos
🧠Seniors make a plan
Example: Juniors wait, Seniors step up.
📚Juniors focus on tasks
🧠Seniors see the big picture
Example: Juniors report, Seniors connect to goals.
📚Juniors guess
🧠Seniors clarify
Example: Juniors assume, Seniors ask the team.
📚Juniors stick to old tools
🧠Seniors try new ones
Example: Juniors love Excel, Seniors code in Python.
📚Juniors give data
🧠Seniors give insights
Example: Juniors share stats, Seniors spot trends.
Seniority is about mindset, not just time.
👍6❤3🔥1
Scenario based Interview Questions & Answers for Data Analyst
1. Scenario: You are working on a SQL database that stores customer information. The database has a table called "Orders" that contains order details. Your task is to write a SQL query to retrieve the total number of orders placed by each customer.
Question:
- Write a SQL query to find the total number of orders placed by each customer.
Expected Answer:
SELECT CustomerID, COUNT(*) AS TotalOrders
FROM Orders
GROUP BY CustomerID;
2. Scenario: You are working on a SQL database that stores employee information. The database has a table called "Employees" that contains employee details. Your task is to write a SQL query to retrieve the names of all employees who have been with the company for more than 5 years.
Question:
- Write a SQL query to find the names of employees who have been with the company for more than 5 years.
Expected Answer:
SELECT Name
FROM Employees
WHERE DATEDIFF(year, HireDate, GETDATE()) > 5;
Power BI Scenario-Based Questions
1. Scenario: You have been given a dataset in Power BI that contains sales data for a company. Your task is to create a report that shows the total sales by product category and region.
Expected Answer:
- Load the dataset into Power BI.
- Create relationships if necessary.
- Use the "Fields" pane to select the necessary fields (Product Category, Region, Sales).
- Drag these fields into the "Values" area of a new visualization (e.g., a table or bar chart).
- Use the "Filters" pane to filter data as needed.
- Format the visualization to enhance clarity and readability.
2. Scenario: You have been asked to create a Power BI dashboard that displays real-time stock prices for a set of companies. The stock prices are available through an API.
Expected Answer:
- Use Power BI Desktop to connect to the API.
- Go to "Get Data" > "Web" and enter the API URL.
- Configure the data refresh settings to ensure real-time updates (e.g., setting up a scheduled refresh or using DirectQuery if supported).
- Create visualizations using the imported data.
- Publish the report to the Power BI service and set up a data gateway if needed for continuous refresh.
3. Scenario: You have been given a Power BI report that contains multiple visualizations. The report is taking a long time to load and is impacting the performance of the application.
Expected Answer:
- Analyze the current performance using Performance Analyzer.
- Optimize data model by reducing the number of columns and rows, and removing unnecessary calculations.
- Use aggregated tables to pre-compute results.
- Simplify DAX calculations.
- Optimize visualizations by reducing the number of visuals per page and avoiding complex custom visuals.
- Ensure proper indexing on the data source.
Free SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Like if you need more similar content
Hope it helps :)
1. Scenario: You are working on a SQL database that stores customer information. The database has a table called "Orders" that contains order details. Your task is to write a SQL query to retrieve the total number of orders placed by each customer.
Question:
- Write a SQL query to find the total number of orders placed by each customer.
Expected Answer:
SELECT CustomerID, COUNT(*) AS TotalOrders
FROM Orders
GROUP BY CustomerID;
2. Scenario: You are working on a SQL database that stores employee information. The database has a table called "Employees" that contains employee details. Your task is to write a SQL query to retrieve the names of all employees who have been with the company for more than 5 years.
Question:
- Write a SQL query to find the names of employees who have been with the company for more than 5 years.
Expected Answer:
SELECT Name
FROM Employees
WHERE DATEDIFF(year, HireDate, GETDATE()) > 5;
Power BI Scenario-Based Questions
1. Scenario: You have been given a dataset in Power BI that contains sales data for a company. Your task is to create a report that shows the total sales by product category and region.
Expected Answer:
- Load the dataset into Power BI.
- Create relationships if necessary.
- Use the "Fields" pane to select the necessary fields (Product Category, Region, Sales).
- Drag these fields into the "Values" area of a new visualization (e.g., a table or bar chart).
- Use the "Filters" pane to filter data as needed.
- Format the visualization to enhance clarity and readability.
2. Scenario: You have been asked to create a Power BI dashboard that displays real-time stock prices for a set of companies. The stock prices are available through an API.
Expected Answer:
- Use Power BI Desktop to connect to the API.
- Go to "Get Data" > "Web" and enter the API URL.
- Configure the data refresh settings to ensure real-time updates (e.g., setting up a scheduled refresh or using DirectQuery if supported).
- Create visualizations using the imported data.
- Publish the report to the Power BI service and set up a data gateway if needed for continuous refresh.
3. Scenario: You have been given a Power BI report that contains multiple visualizations. The report is taking a long time to load and is impacting the performance of the application.
Expected Answer:
- Analyze the current performance using Performance Analyzer.
- Optimize data model by reducing the number of columns and rows, and removing unnecessary calculations.
- Use aggregated tables to pre-compute results.
- Simplify DAX calculations.
- Optimize visualizations by reducing the number of visuals per page and avoiding complex custom visuals.
- Ensure proper indexing on the data source.
Free SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Like if you need more similar content
Hope it helps :)
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What seperates a good 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 from a great one?
The journey to becoming an exceptional data analyst requires mastering a blend of technical and soft skills.
☑ Technical skills:
- Querying Data with SQL
- Data Visualization (Tableau/PowerBI)
- Data Storytelling and Reporting
- Data Exploration and Analytics
- Data Modeling
☑ Soft Skills:
- Problem Solving
- Communication
- Business Acumen
- Curiosity
- Critical Thinking
- Learning Mindset
But how do you develop these soft skills?
◆ Tackle real-world data projects or case studies. The more complex, the better.
◆ Practice explaining your analysis to non-technical audiences. If they understand, you’ve nailed it!
◆ Learn how industries use data for decision-making. Align your analysis with business outcomes.
◆ Stay curious, ask 'why,' and dig deeper into your data. Don’t settle for surface-level insights.
◆ Keep evolving. Attend webinars, read books, or engage with industry experts regularly.
The journey to becoming an exceptional data analyst requires mastering a blend of technical and soft skills.
☑ Technical skills:
- Querying Data with SQL
- Data Visualization (Tableau/PowerBI)
- Data Storytelling and Reporting
- Data Exploration and Analytics
- Data Modeling
☑ Soft Skills:
- Problem Solving
- Communication
- Business Acumen
- Curiosity
- Critical Thinking
- Learning Mindset
But how do you develop these soft skills?
◆ Tackle real-world data projects or case studies. The more complex, the better.
◆ Practice explaining your analysis to non-technical audiences. If they understand, you’ve nailed it!
◆ Learn how industries use data for decision-making. Align your analysis with business outcomes.
◆ Stay curious, ask 'why,' and dig deeper into your data. Don’t settle for surface-level insights.
◆ Keep evolving. Attend webinars, read books, or engage with industry experts regularly.
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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.
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 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
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 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
👍8❤1🔥1
📊🚀A beginner's roadmap for learning SQL:
🔹Understand Basics:
Learn what SQL is and its purpose in managing relational databases.
Understand basic database concepts like tables, rows, columns, and relationships.
🔹Learn SQL Syntax:
Familiarize yourself with SQL syntax for common commands like SELECT, INSERT, UPDATE, DELETE.
Understand clauses like WHERE, ORDER BY, GROUP BY, and JOIN.
🔹Setup a Database:
Install a relational database management system (RDBMS) like MySQL, SQLite, or PostgreSQL.
Practice creating databases, tables, and inserting data.
🔹Retrieve Data (SELECT):
Learn to retrieve data from a database using SELECT statements.
Practice filtering data using WHERE clause and sorting using ORDER BY.
🔹Modify Data (INSERT, UPDATE, DELETE):
Understand how to insert new records, update existing ones, and delete data.
Be cautious with DELETE to avoid unintentional data loss.
🔹Working with Functions:
Explore SQL functions like COUNT, AVG, SUM, MAX, MIN for data analysis.
Understand string functions, date functions, and mathematical functions.
🔹Data Filtering and Sorting:
Learn advanced filtering techniques using AND, OR, and IN operators.
Practice sorting data using multiple columns.
🔹Table Relationships (JOIN):
Understand the concept of joining tables to retrieve data from multiple tables.
Learn about INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.
🔹Grouping and Aggregation:
Explore GROUP BY clause to group data based on specific columns.
Understand aggregate functions for summarizing data (SUM, AVG, COUNT).
🔹Subqueries:
Learn to use subqueries to perform complex queries.
Understand how to use subqueries in SELECT, WHERE, and FROM clauses.
🔹Indexes and Optimization:
Gain knowledge about indexes and their role in optimizing queries.
Understand how to optimize SQL queries for better performance.
🔹Transactions and ACID Properties:
Learn about transactions and the ACID properties (Atomicity, Consistency, Isolation, Durability).
Understand how to use transactions to maintain data integrity.
🔹Normalization:
Understand the basics of database normalization to design efficient databases.
Learn about 1NF, 2NF, 3NF, and BCNF.
🔹Backup and Recovery:
Understand the importance of database backups.
Learn how to perform backups and recovery operations.
🔹Practice and Projects:
Apply your knowledge through hands-on projects.
Practice on platforms like LeetCode, HackerRank, or build your own small database-driven projects.
👀👍Remember to practice regularly and build real-world projects to reinforce your learning. Happy coding!
🔹Understand Basics:
Learn what SQL is and its purpose in managing relational databases.
Understand basic database concepts like tables, rows, columns, and relationships.
🔹Learn SQL Syntax:
Familiarize yourself with SQL syntax for common commands like SELECT, INSERT, UPDATE, DELETE.
Understand clauses like WHERE, ORDER BY, GROUP BY, and JOIN.
🔹Setup a Database:
Install a relational database management system (RDBMS) like MySQL, SQLite, or PostgreSQL.
Practice creating databases, tables, and inserting data.
🔹Retrieve Data (SELECT):
Learn to retrieve data from a database using SELECT statements.
Practice filtering data using WHERE clause and sorting using ORDER BY.
🔹Modify Data (INSERT, UPDATE, DELETE):
Understand how to insert new records, update existing ones, and delete data.
Be cautious with DELETE to avoid unintentional data loss.
🔹Working with Functions:
Explore SQL functions like COUNT, AVG, SUM, MAX, MIN for data analysis.
Understand string functions, date functions, and mathematical functions.
🔹Data Filtering and Sorting:
Learn advanced filtering techniques using AND, OR, and IN operators.
Practice sorting data using multiple columns.
🔹Table Relationships (JOIN):
Understand the concept of joining tables to retrieve data from multiple tables.
Learn about INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.
🔹Grouping and Aggregation:
Explore GROUP BY clause to group data based on specific columns.
Understand aggregate functions for summarizing data (SUM, AVG, COUNT).
🔹Subqueries:
Learn to use subqueries to perform complex queries.
Understand how to use subqueries in SELECT, WHERE, and FROM clauses.
🔹Indexes and Optimization:
Gain knowledge about indexes and their role in optimizing queries.
Understand how to optimize SQL queries for better performance.
🔹Transactions and ACID Properties:
Learn about transactions and the ACID properties (Atomicity, Consistency, Isolation, Durability).
Understand how to use transactions to maintain data integrity.
🔹Normalization:
Understand the basics of database normalization to design efficient databases.
Learn about 1NF, 2NF, 3NF, and BCNF.
🔹Backup and Recovery:
Understand the importance of database backups.
Learn how to perform backups and recovery operations.
🔹Practice and Projects:
Apply your knowledge through hands-on projects.
Practice on platforms like LeetCode, HackerRank, or build your own small database-driven projects.
👀👍Remember to practice regularly and build real-world projects to reinforce your learning. Happy coding!
👍7❤4
The best doesn't come from working more.
It comes from working smarter.
The most common mistakes people make,
With practical tips to avoid each:
1) Working late every night.
• Prioritize quality time with loved ones.
Understand that long hours won't be remembered as fondly as time spent with family and friends.
2) Believing more hours mean more productivity.
• Focus on efficiency.
Complete tasks in less time to free up hours for personal activities and rest.
3) Ignoring the need for breaks.
• Take regular breaks to rejuvenate your mind.
Creativity and productivity suffer without proper rest.
4) Sacrificing personal well-being.
• Maintain a healthy work-life balance.
Ensure you don't compromise your health or relationships for work.
5) Feeling pressured to constantly produce.
• Quality over quantity.
6) Neglecting hobbies and interests.
• Engage in activities you love outside of work.
This helps to keep your mind fresh and inspired.
7) Failing to set boundaries.
• Set clear work hours and stick to them.
This helps to prevent overworking and ensures you have time for yourself.
8) Not delegating tasks.
• Delegate when possible.
Sharing the workload can enhance productivity and give you more free time.
9) Overlooking the importance of sleep.
• Prioritize sleep for better performance.
A well-rested mind is more creative and effective.
10) Underestimating the impact of overworking.
• Recognize the long-term effects.
👉WhatsApp Channel: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
All the best 👍 👍
It comes from working smarter.
The most common mistakes people make,
With practical tips to avoid each:
1) Working late every night.
• Prioritize quality time with loved ones.
Understand that long hours won't be remembered as fondly as time spent with family and friends.
2) Believing more hours mean more productivity.
• Focus on efficiency.
Complete tasks in less time to free up hours for personal activities and rest.
3) Ignoring the need for breaks.
• Take regular breaks to rejuvenate your mind.
Creativity and productivity suffer without proper rest.
4) Sacrificing personal well-being.
• Maintain a healthy work-life balance.
Ensure you don't compromise your health or relationships for work.
5) Feeling pressured to constantly produce.
• Quality over quantity.
6) Neglecting hobbies and interests.
• Engage in activities you love outside of work.
This helps to keep your mind fresh and inspired.
7) Failing to set boundaries.
• Set clear work hours and stick to them.
This helps to prevent overworking and ensures you have time for yourself.
8) Not delegating tasks.
• Delegate when possible.
Sharing the workload can enhance productivity and give you more free time.
9) Overlooking the importance of sleep.
• Prioritize sleep for better performance.
A well-rested mind is more creative and effective.
10) Underestimating the impact of overworking.
• Recognize the long-term effects.
👉WhatsApp Channel: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
All the best 👍 👍
👍9❤2
Guys, Big Announcement!
I’m launching a Complete SQL Learning Series — designed for everyone — whether you're a beginner, intermediate, or someone preparing for data interviews.
This is a complete step-by-step journey — from scratch to advanced — filled with practical examples, relatable scenarios, and short quizzes after each topic to solidify your learning.
Here’s the 5-Week Plan:
Week 1: SQL Fundamentals (No Prior Knowledge Needed)
- What is SQL? Real-world Use Cases
- Databases vs Tables
- SELECT Queries — The Heart of SQL
- Filtering Data with WHERE
- Sorting with ORDER BY
- Using DISTINCT and LIMIT
- Basic Arithmetic and Column Aliases
Week 2: Aggregations & Grouping
- COUNT, SUM, AVG, MIN, MAX — When and How
- GROUP BY — The Right Way
- HAVING vs WHERE
- Dealing with NULLs in Aggregations
- CASE Statements for Conditional Logic
*Week 3: Mastering JOINS & Relationships*
- Understanding Table Relationships (1-to-1, 1-to-Many)
- INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN
- Practical Examples with Two or More Tables
- SELF JOIN & CROSS JOIN — What, When & Why
- Common Join Mistakes & Fixes
Week 4: Advanced SQL Concepts
- Subqueries: Writing Queries Inside Queries
- CTEs (WITH Clause): Cleaner & More Readable SQL
- Window Functions: RANK, DENSE_RANK, ROW_NUMBER
- Using PARTITION BY and ORDER BY
- EXISTS vs IN: Performance and Use Cases
Week 5: Real-World Scenarios & Interview-Ready SQL
- Using SQL to Solve Real Business Problems
- SQL for Sales, Marketing, HR & Product Analytics
- Writing Clean, Efficient & Complex Queries
- Most Common SQL Interview Questions like:
“Find the second highest salary”
“Detect duplicates in a table”
“Calculate running totals”
“Identify top N products per category”
- Practice Challenges Based on Real Interviews
React with ❤️ if you're ready for this series
Join our WhatsApp channel to access it: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v/1075
I’m launching a Complete SQL Learning Series — designed for everyone — whether you're a beginner, intermediate, or someone preparing for data interviews.
This is a complete step-by-step journey — from scratch to advanced — filled with practical examples, relatable scenarios, and short quizzes after each topic to solidify your learning.
Here’s the 5-Week Plan:
Week 1: SQL Fundamentals (No Prior Knowledge Needed)
- What is SQL? Real-world Use Cases
- Databases vs Tables
- SELECT Queries — The Heart of SQL
- Filtering Data with WHERE
- Sorting with ORDER BY
- Using DISTINCT and LIMIT
- Basic Arithmetic and Column Aliases
Week 2: Aggregations & Grouping
- COUNT, SUM, AVG, MIN, MAX — When and How
- GROUP BY — The Right Way
- HAVING vs WHERE
- Dealing with NULLs in Aggregations
- CASE Statements for Conditional Logic
*Week 3: Mastering JOINS & Relationships*
- Understanding Table Relationships (1-to-1, 1-to-Many)
- INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN
- Practical Examples with Two or More Tables
- SELF JOIN & CROSS JOIN — What, When & Why
- Common Join Mistakes & Fixes
Week 4: Advanced SQL Concepts
- Subqueries: Writing Queries Inside Queries
- CTEs (WITH Clause): Cleaner & More Readable SQL
- Window Functions: RANK, DENSE_RANK, ROW_NUMBER
- Using PARTITION BY and ORDER BY
- EXISTS vs IN: Performance and Use Cases
Week 5: Real-World Scenarios & Interview-Ready SQL
- Using SQL to Solve Real Business Problems
- SQL for Sales, Marketing, HR & Product Analytics
- Writing Clean, Efficient & Complex Queries
- Most Common SQL Interview Questions like:
“Find the second highest salary”
“Detect duplicates in a table”
“Calculate running totals”
“Identify top N products per category”
- Practice Challenges Based on Real Interviews
React with ❤️ if you're ready for this series
Join our WhatsApp channel to access it: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v/1075
❤28👍7🥰1👏1
Soft skills questions will be part of your next data job interview!
Here is what you should prepare for:
1. 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Be ready to discuss how you explain complex data insights to non-technical stakeholders.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“How do you ensure that your data insights are understood and get used by non-technical stakeholders?”
2. 𝗧𝗲𝗮𝗺 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: Show your ability to work well with others.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“Can you talk about a time when you had to manage a conflict within a team? How did you resolve it?”
3. 𝗣𝗿𝗼𝗯𝗹𝗲𝗺-𝗦𝗼𝗹𝘃𝗶𝗻𝗴: Highlight your critical thinking and problem-solving skills.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“Describe a situation where you had to make a quick decision based on incomplete data. What was the outcome?”
4. 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Demonstrate your flexibility and openness to change.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“How do you handle sudden changes in project priorities or scope?”
5. 𝗧𝗶𝗺𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Prove your ability to manage multiple tasks and deadlines.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“Tell me about a time when you were under tight deadlines. How did you manage to meet them?”
6. 𝗘𝗺𝗽𝗮𝘁𝗵𝘆 𝗮𝗻𝗱 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴: Show your ability to understand stakeholder needs.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“How do you approach understanding the needs of different stakeholders when starting a new project?”
Structure your answers using the STAR method (Situation, Task, Action, Result). This helps you provide clear and concise responses that highlight your skills.
By preparing for these soft skills questions, you’ll demonstrate that you’re not just technically fit, but also a well-rounded professional ready to make an impact on the business.
You can find useful tips to improve your soft skills here: 👇 https://news.1rj.ru/str/englishlearnerspro/
Here is what you should prepare for:
1. 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Be ready to discuss how you explain complex data insights to non-technical stakeholders.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“How do you ensure that your data insights are understood and get used by non-technical stakeholders?”
2. 𝗧𝗲𝗮𝗺 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: Show your ability to work well with others.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“Can you talk about a time when you had to manage a conflict within a team? How did you resolve it?”
3. 𝗣𝗿𝗼𝗯𝗹𝗲𝗺-𝗦𝗼𝗹𝘃𝗶𝗻𝗴: Highlight your critical thinking and problem-solving skills.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“Describe a situation where you had to make a quick decision based on incomplete data. What was the outcome?”
4. 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Demonstrate your flexibility and openness to change.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“How do you handle sudden changes in project priorities or scope?”
5. 𝗧𝗶𝗺𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Prove your ability to manage multiple tasks and deadlines.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“Tell me about a time when you were under tight deadlines. How did you manage to meet them?”
6. 𝗘𝗺𝗽𝗮𝘁𝗵𝘆 𝗮𝗻𝗱 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴: Show your ability to understand stakeholder needs.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯:
“How do you approach understanding the needs of different stakeholders when starting a new project?”
Structure your answers using the STAR method (Situation, Task, Action, Result). This helps you provide clear and concise responses that highlight your skills.
By preparing for these soft skills questions, you’ll demonstrate that you’re not just technically fit, but also a well-rounded professional ready to make an impact on the business.
You can find useful tips to improve your soft skills here: 👇 https://news.1rj.ru/str/englishlearnerspro/
👍2❤1
If you are targeting your first Data Analyst job then this is why you should avoid guided projects
The common thing nowadays is "Coffee Sales Analysis" and "Pizza Sales Analysis"
I don't see these projects as PROJECTS
But as big RED flags
We are showing our SKILLS through projects, RIGHT?
Then what's WRONG with these projects?
Don't think from YOUR side
Think from the HIRING team's side
These projects have more than a MILLION views on YouTube
Even if you consider 50% of this NUMBER
Then just IMAGINE how many aspiring Data Analysts would have created this same project
Hiring teams see hundreds of resumes and portfolios on a DAILY basis
Just imagine how many times they would have seen the SAME noscripts of projects again and again
They would know that these projects are PUBLICLY available for EVERYONE
You have simply copied pasted the ENTIRE project from YouTube
So now if I want to hire a Data Analyst then how would I JUDGE you or your technical skills?
What is the USE of Pizza or Coffee sales analysis projects for MY company?
By doing such guided projects, you are involving yourself in a big circle of COMPETITION
I repeat, there were more than a MILLION views
So please AVOID guided projects at all costs
Guided projects are good for your personal PRACTICE and LinkedIn CONTENT
But try not to involve them in your PORTFOLIO or RESUME
The common thing nowadays is "Coffee Sales Analysis" and "Pizza Sales Analysis"
I don't see these projects as PROJECTS
But as big RED flags
We are showing our SKILLS through projects, RIGHT?
Then what's WRONG with these projects?
Don't think from YOUR side
Think from the HIRING team's side
These projects have more than a MILLION views on YouTube
Even if you consider 50% of this NUMBER
Then just IMAGINE how many aspiring Data Analysts would have created this same project
Hiring teams see hundreds of resumes and portfolios on a DAILY basis
Just imagine how many times they would have seen the SAME noscripts of projects again and again
They would know that these projects are PUBLICLY available for EVERYONE
You have simply copied pasted the ENTIRE project from YouTube
So now if I want to hire a Data Analyst then how would I JUDGE you or your technical skills?
What is the USE of Pizza or Coffee sales analysis projects for MY company?
By doing such guided projects, you are involving yourself in a big circle of COMPETITION
I repeat, there were more than a MILLION views
So please AVOID guided projects at all costs
Guided projects are good for your personal PRACTICE and LinkedIn CONTENT
But try not to involve them in your PORTFOLIO or RESUME
👍15❤3👏1🎉1
Common Data Cleaning Techniques for Data Analysts
Remove Duplicates:
Purpose: Eliminate repeated rows to maintain unique data.
Example: SELECT DISTINCT column_name FROM table;
Handle Missing Values:
Purpose: Fill, remove, or impute missing data.
Example:
Remove: df.dropna() (in Python/Pandas)
Fill: df.fillna(0)
Standardize Data:
Purpose: Convert data to a consistent format (e.g., dates, numbers).
Example: Convert text to lowercase: df['column'] = df['column'].str.lower()
Remove Outliers:
Purpose: Identify and remove extreme values.
Example: df = df[df['column'] < threshold]
Correct Data Types:
Purpose: Ensure columns have the correct data type (e.g., dates as datetime, numeric values as integers).
Example: df['date'] = pd.to_datetime(df['date'])
Normalize Data:
Purpose: Scale numerical data to a standard range (0 to 1).
Example: from sklearn.preprocessing import MinMaxScaler; df['scaled'] = MinMaxScaler().fit_transform(df[['column']])
Data Transformation:
Purpose: Transform or aggregate data for better analysis (e.g., log transformations, aggregating columns).
Example: Apply log transformation: df['log_column'] = np.log(df['column'] + 1)
Handle Categorical Data:
Purpose: Convert categorical data into numerical data using encoding techniques.
Example: df['encoded_column'] = pd.get_dummies(df['category_column'])
Impute Missing Values:
Purpose: Fill missing values with a meaningful value (e.g., mean, median, or a specific value).
Example: df['column'] = df['column'].fillna(df['column'].mean())
Data Cleaning: https://whatsapp.com/channel/0029VarxgFqATRSpdUeHUA27
Like this post for more content like this 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
Remove Duplicates:
Purpose: Eliminate repeated rows to maintain unique data.
Example: SELECT DISTINCT column_name FROM table;
Handle Missing Values:
Purpose: Fill, remove, or impute missing data.
Example:
Remove: df.dropna() (in Python/Pandas)
Fill: df.fillna(0)
Standardize Data:
Purpose: Convert data to a consistent format (e.g., dates, numbers).
Example: Convert text to lowercase: df['column'] = df['column'].str.lower()
Remove Outliers:
Purpose: Identify and remove extreme values.
Example: df = df[df['column'] < threshold]
Correct Data Types:
Purpose: Ensure columns have the correct data type (e.g., dates as datetime, numeric values as integers).
Example: df['date'] = pd.to_datetime(df['date'])
Normalize Data:
Purpose: Scale numerical data to a standard range (0 to 1).
Example: from sklearn.preprocessing import MinMaxScaler; df['scaled'] = MinMaxScaler().fit_transform(df[['column']])
Data Transformation:
Purpose: Transform or aggregate data for better analysis (e.g., log transformations, aggregating columns).
Example: Apply log transformation: df['log_column'] = np.log(df['column'] + 1)
Handle Categorical Data:
Purpose: Convert categorical data into numerical data using encoding techniques.
Example: df['encoded_column'] = pd.get_dummies(df['category_column'])
Impute Missing Values:
Purpose: Fill missing values with a meaningful value (e.g., mean, median, or a specific value).
Example: df['column'] = df['column'].fillna(df['column'].mean())
Data Cleaning: https://whatsapp.com/channel/0029VarxgFqATRSpdUeHUA27
Like this post for more content like this 👍♥️
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
👍12❤2
🧠 Technologies for Data Analysts!
📊 Data Manipulation & Analysis
▪️ Excel – Spreadsheet Data Analysis & Visualization
▪️ SQL – Structured Query Language for Data Extraction
▪️ Pandas (Python) – Data Analysis with DataFrames
▪️ NumPy (Python) – Numerical Computing for Large Datasets
▪️ Google Sheets – Online Collaboration for Data Analysis
📈 Data Visualization
▪️ Power BI – Business Intelligence & Dashboarding
▪️ Tableau – Interactive Data Visualization
▪️ Matplotlib (Python) – Plotting Graphs & Charts
▪️ Seaborn (Python) – Statistical Data Visualization
▪️ Google Data Studio – Free, Web-Based Visualization Tool
🔄 ETL (Extract, Transform, Load)
▪️ SQL Server Integration Services (SSIS) – Data Integration & ETL
▪️ Apache NiFi – Automating Data Flows
▪️ Talend – Data Integration for Cloud & On-premises
🧹 Data Cleaning & Preparation
▪️ OpenRefine – Clean & Transform Messy Data
▪️ Pandas Profiling (Python) – Data Profiling & Preprocessing
▪️ DataWrangler – Data Transformation Tool
📦 Data Storage & Databases
▪️ SQL – Relational Databases (MySQL, PostgreSQL, MS SQL)
▪️ NoSQL (MongoDB) – Flexible, Schema-less Data Storage
▪️ Google BigQuery – Scalable Cloud Data Warehousing
▪️ Redshift – Amazon’s Cloud Data Warehouse
⚙️ Data Automation
▪️ Alteryx – Data Blending & Advanced Analytics
▪️ Knime – Data Analytics & Reporting Automation
▪️ Zapier – Connect & Automate Data Workflows
📊 Advanced Analytics & Statistical Tools
▪️ R – Statistical Computing & Analysis
▪️ Python (SciPy, Statsmodels) – Statistical Modeling & Hypothesis Testing
▪️ SPSS – Statistical Software for Data Analysis
▪️ SAS – Advanced Analytics & Predictive Modeling
🌐 Collaboration & Reporting
▪️ Power BI Service – Online Sharing & Collaboration for Dashboards
▪️ Tableau Online – Cloud-Based Visualization & Sharing
▪️ Google Analytics – Web Traffic Data Insights
▪️ Trello / JIRA – Project & Task Management for Data Projects
Data-Driven Decisions with the Right Tools!
React ❤️ for more
📊 Data Manipulation & Analysis
▪️ Excel – Spreadsheet Data Analysis & Visualization
▪️ SQL – Structured Query Language for Data Extraction
▪️ Pandas (Python) – Data Analysis with DataFrames
▪️ NumPy (Python) – Numerical Computing for Large Datasets
▪️ Google Sheets – Online Collaboration for Data Analysis
📈 Data Visualization
▪️ Power BI – Business Intelligence & Dashboarding
▪️ Tableau – Interactive Data Visualization
▪️ Matplotlib (Python) – Plotting Graphs & Charts
▪️ Seaborn (Python) – Statistical Data Visualization
▪️ Google Data Studio – Free, Web-Based Visualization Tool
🔄 ETL (Extract, Transform, Load)
▪️ SQL Server Integration Services (SSIS) – Data Integration & ETL
▪️ Apache NiFi – Automating Data Flows
▪️ Talend – Data Integration for Cloud & On-premises
🧹 Data Cleaning & Preparation
▪️ OpenRefine – Clean & Transform Messy Data
▪️ Pandas Profiling (Python) – Data Profiling & Preprocessing
▪️ DataWrangler – Data Transformation Tool
📦 Data Storage & Databases
▪️ SQL – Relational Databases (MySQL, PostgreSQL, MS SQL)
▪️ NoSQL (MongoDB) – Flexible, Schema-less Data Storage
▪️ Google BigQuery – Scalable Cloud Data Warehousing
▪️ Redshift – Amazon’s Cloud Data Warehouse
⚙️ Data Automation
▪️ Alteryx – Data Blending & Advanced Analytics
▪️ Knime – Data Analytics & Reporting Automation
▪️ Zapier – Connect & Automate Data Workflows
📊 Advanced Analytics & Statistical Tools
▪️ R – Statistical Computing & Analysis
▪️ Python (SciPy, Statsmodels) – Statistical Modeling & Hypothesis Testing
▪️ SPSS – Statistical Software for Data Analysis
▪️ SAS – Advanced Analytics & Predictive Modeling
🌐 Collaboration & Reporting
▪️ Power BI Service – Online Sharing & Collaboration for Dashboards
▪️ Tableau Online – Cloud-Based Visualization & Sharing
▪️ Google Analytics – Web Traffic Data Insights
▪️ Trello / JIRA – Project & Task Management for Data Projects
Data-Driven Decisions with the Right Tools!
React ❤️ for more
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10 SQL Concepts Every Data Analyst Should Master 👇
✅ SELECT, WHERE, ORDER BY – Core of querying your data
✅ JOINs (INNER, LEFT, RIGHT, FULL) – Combine data from multiple tables
✅ GROUP BY & HAVING – Aggregate and filter grouped data
✅ Subqueries – Nest queries inside queries for complex logic
✅ CTEs (Common Table Expressions) – Write cleaner, reusable SQL logic
✅ Window Functions – Perform advanced analytics like rankings & running totals
✅ Indexes – Boost your query performance
✅ Normalization – Structure your database efficiently
✅ UNION vs UNION ALL – Combine result sets with or without duplicates
✅ Stored Procedures & Functions – Reusable logic inside your DB
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Share with credits: https://news.1rj.ru/str/sqlspecialist
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✅ SELECT, WHERE, ORDER BY – Core of querying your data
✅ JOINs (INNER, LEFT, RIGHT, FULL) – Combine data from multiple tables
✅ GROUP BY & HAVING – Aggregate and filter grouped data
✅ Subqueries – Nest queries inside queries for complex logic
✅ CTEs (Common Table Expressions) – Write cleaner, reusable SQL logic
✅ Window Functions – Perform advanced analytics like rankings & running totals
✅ Indexes – Boost your query performance
✅ Normalization – Structure your database efficiently
✅ UNION vs UNION ALL – Combine result sets with or without duplicates
✅ Stored Procedures & Functions – Reusable logic inside your DB
React with ❤️ if you want me to cover each topic in detail
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
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Excel Scenario-Based Questions Interview Questions and Answers :
Scenario 1) Imagine you have a dataset with missing values. How would you approach this problem in Excel?
Answer:
To handle missing values in Excel:
1. Identify Missing Data:
Use filters to quickly find blank cells.
Apply conditional formatting:
Home → Conditional Formatting → New Rule → Format only cells that are blank.
2. Handle Missing Data:
Delete rows with missing critical data (if appropriate).
Fill missing values:
Use =IF(A2="", "N/A", A2) to replace blanks with “N/A”.
Use Fill Down (Ctrl + D) if the previous value applies.
Use functions like =AVERAGEIF(range, "<>", range) to fill with average.
3. Use Power Query (for large datasets):
Load data into Power Query and use “Replace Values” or “Remove Empty” options.
Scenario 2) You are given a dataset with multiple sheets. How would you consolidate the data for analysis?
Answer:
Approach 1: Manual Consolidation
1. Use Copy-Paste from each sheet into a master sheet.
2. Add a new column to identify the source sheet (optional but useful).
3. Convert the master data into a table for analysis.
Approach 2: Use Power Query (Recommended for large datasets)
1. Go to Data → Get & Transform → Get Data → From Workbook.
2. Load each sheet into Power Query.
3. Use the Append Queries option to merge all sheets.
4. Clean and transform as needed, then load it back to Excel.
Approach 3: Use VBA (Advanced Users)
Write a macro to loop through all sheets and append data to a master sheet.
Hope it helps :)
Scenario 1) Imagine you have a dataset with missing values. How would you approach this problem in Excel?
Answer:
To handle missing values in Excel:
1. Identify Missing Data:
Use filters to quickly find blank cells.
Apply conditional formatting:
Home → Conditional Formatting → New Rule → Format only cells that are blank.
2. Handle Missing Data:
Delete rows with missing critical data (if appropriate).
Fill missing values:
Use =IF(A2="", "N/A", A2) to replace blanks with “N/A”.
Use Fill Down (Ctrl + D) if the previous value applies.
Use functions like =AVERAGEIF(range, "<>", range) to fill with average.
3. Use Power Query (for large datasets):
Load data into Power Query and use “Replace Values” or “Remove Empty” options.
Scenario 2) You are given a dataset with multiple sheets. How would you consolidate the data for analysis?
Answer:
Approach 1: Manual Consolidation
1. Use Copy-Paste from each sheet into a master sheet.
2. Add a new column to identify the source sheet (optional but useful).
3. Convert the master data into a table for analysis.
Approach 2: Use Power Query (Recommended for large datasets)
1. Go to Data → Get & Transform → Get Data → From Workbook.
2. Load each sheet into Power Query.
3. Use the Append Queries option to merge all sheets.
4. Clean and transform as needed, then load it back to Excel.
Approach 3: Use VBA (Advanced Users)
Write a macro to loop through all sheets and append data to a master sheet.
Hope it helps :)
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🔟 Data Analyst Project Ideas for Beginners
1. Sales Analysis Dashboard: Use tools like Excel or Tableau to create a dashboard analyzing sales data. Visualize trends, top products, and seasonal patterns.
2. Customer Segmentation: Analyze customer data using clustering techniques (like K-means) to segment customers based on purchasing behavior and demographics.
3. Social Media Metrics Analysis: Gather data from social media platforms to analyze engagement metrics. Create visualizations to highlight trends and performance.
4. Survey Data Analysis: Conduct a survey and analyze the results using statistical techniques. Present findings with visualizations to showcase insights.
5. Exploratory Data Analysis (EDA): Choose a public dataset and perform EDA using Python (Pandas, Matplotlib) or R (tidyverse). Summarize key insights and visualizations.
6. Employee Performance Analysis: Analyze employee performance data to identify trends in productivity, turnover rates, and training effectiveness.
7. Public Health Data Analysis: Use datasets from public health sources (like CDC) to analyze trends in health metrics (e.g., vaccination rates, disease outbreaks) and visualize findings.
8. Real Estate Market Analysis: Analyze real estate listings to find trends in pricing, location, and features. Use data visualization to present your findings.
9. Weather Data Visualization: Collect weather data and analyze trends over time. Create visualizations to show changes in temperature, precipitation, or extreme weather events.
10. Financial Analysis: Analyze a company’s financial statements to assess its performance over time. Create visualizations to highlight key financial ratios and trends.
Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope it helps :)
1. Sales Analysis Dashboard: Use tools like Excel or Tableau to create a dashboard analyzing sales data. Visualize trends, top products, and seasonal patterns.
2. Customer Segmentation: Analyze customer data using clustering techniques (like K-means) to segment customers based on purchasing behavior and demographics.
3. Social Media Metrics Analysis: Gather data from social media platforms to analyze engagement metrics. Create visualizations to highlight trends and performance.
4. Survey Data Analysis: Conduct a survey and analyze the results using statistical techniques. Present findings with visualizations to showcase insights.
5. Exploratory Data Analysis (EDA): Choose a public dataset and perform EDA using Python (Pandas, Matplotlib) or R (tidyverse). Summarize key insights and visualizations.
6. Employee Performance Analysis: Analyze employee performance data to identify trends in productivity, turnover rates, and training effectiveness.
7. Public Health Data Analysis: Use datasets from public health sources (like CDC) to analyze trends in health metrics (e.g., vaccination rates, disease outbreaks) and visualize findings.
8. Real Estate Market Analysis: Analyze real estate listings to find trends in pricing, location, and features. Use data visualization to present your findings.
9. Weather Data Visualization: Collect weather data and analyze trends over time. Create visualizations to show changes in temperature, precipitation, or extreme weather events.
10. Financial Analysis: Analyze a company’s financial statements to assess its performance over time. Create visualizations to highlight key financial ratios and trends.
Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope it helps :)
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If you are interested to learn SQL for data analytics purpose and clear the interviews, just cover the following topics
1)Install MYSQL workbench
2) Select
3) From
4) where
5) group by
6) having
7) limit
8) Joins (Left, right , inner, self, cross)
9) Aggregate function ( Sum, Max, Min , Avg)
9) windows function ( row num, rank, dense rank, lead, lag, Sum () over)
10)Case
11) Like
12) Sub queries
13) CTE
14) Replace CTE with temp tables
15) Methods to optimize Sql queries
16) Solve problems and case studies at Ankit Bansal youtube channel
Trick: Just copy each term and paste on youtube and watch any 10 to 15 minute on each topic and practise it while learning , By doing this , you get the basics understanding
17) Now time to go on youtube and search data analysis end to end project using sql
18) Watch them and practise them end to end.
17) learn integration with power bi
In this way , you will not only memorize the concepts but also learn how to implement them in your current working and projects and will be able to defend it in your interviews as well.
Like for more
Here you can find essential SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Hope it helps :)
1)Install MYSQL workbench
2) Select
3) From
4) where
5) group by
6) having
7) limit
8) Joins (Left, right , inner, self, cross)
9) Aggregate function ( Sum, Max, Min , Avg)
9) windows function ( row num, rank, dense rank, lead, lag, Sum () over)
10)Case
11) Like
12) Sub queries
13) CTE
14) Replace CTE with temp tables
15) Methods to optimize Sql queries
16) Solve problems and case studies at Ankit Bansal youtube channel
Trick: Just copy each term and paste on youtube and watch any 10 to 15 minute on each topic and practise it while learning , By doing this , you get the basics understanding
17) Now time to go on youtube and search data analysis end to end project using sql
18) Watch them and practise them end to end.
17) learn integration with power bi
In this way , you will not only memorize the concepts but also learn how to implement them in your current working and projects and will be able to defend it in your interviews as well.
Like for more
Here you can find essential SQL Interview Resources👇
https://news.1rj.ru/str/DataSimplifier
Hope it helps :)
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Step-by-step guide to become a Data Analyst in 2025—📊
1. Learn the Fundamentals:
Start with Excel, basic statistics, and data visualization concepts.
2. Pick Up Key Tools & Languages:
Master SQL, Python (or R), and data visualization tools like Tableau or Power BI.
3. Get Formal Education or Certification:
A bachelor’s degree in a relevant field (like Computer Science, Math, or Economics) helps, but you can also do online courses or certifications in data analytics.
4. Build Hands-on Experience:
Work on real-world projects—use Kaggle datasets, internships, or freelance gigs to practice data cleaning, analysis, and visualization.
5. Create a Portfolio:
Showcase your projects on GitHub or a personal website. Include dashboards, reports, and code samples.
6. Develop Soft Skills:
Focus on communication, problem-solving, teamwork, and attention to detail—these are just as important as technical skills.
7. Apply for Entry-Level Jobs:
Look for roles like “Junior Data Analyst” or “Business Analyst.” Tailor your resume to highlight your skills and portfolio.
8. Keep Learning:
Stay updated with new tools (like AI-driven analytics), trends, and advanced topics such as machine learning or domain-specific analytics.
React ❤️ for more
1. Learn the Fundamentals:
Start with Excel, basic statistics, and data visualization concepts.
2. Pick Up Key Tools & Languages:
Master SQL, Python (or R), and data visualization tools like Tableau or Power BI.
3. Get Formal Education or Certification:
A bachelor’s degree in a relevant field (like Computer Science, Math, or Economics) helps, but you can also do online courses or certifications in data analytics.
4. Build Hands-on Experience:
Work on real-world projects—use Kaggle datasets, internships, or freelance gigs to practice data cleaning, analysis, and visualization.
5. Create a Portfolio:
Showcase your projects on GitHub or a personal website. Include dashboards, reports, and code samples.
6. Develop Soft Skills:
Focus on communication, problem-solving, teamwork, and attention to detail—these are just as important as technical skills.
7. Apply for Entry-Level Jobs:
Look for roles like “Junior Data Analyst” or “Business Analyst.” Tailor your resume to highlight your skills and portfolio.
8. Keep Learning:
Stay updated with new tools (like AI-driven analytics), trends, and advanced topics such as machine learning or domain-specific analytics.
React ❤️ for more
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Excel Hack of the Week—super simple and super useful! 😎
🧹 Remove Duplicates in Seconds!
1️⃣ Select your data range.
2️⃣ Go to Data > Remove Duplicates.
3️⃣ Pick the columns to check for duplicates and hit OK—done!
🔍 Example:
✅ Got a list of emails with repeats? Remove Duplicates keeps only unique ones!
✅ Cleaning up sales data? Instantly get rid of double entries!
📌 Bonus: Use this trick to tidy up contact lists, inventory records, or survey responses—no formulas needed!
Like this post if you want more Excel and data hacks every week! 👍✨
Credits: https://whatsapp.com/channel/0029VaifY548qIzv0u1AHz3i
🧹 Remove Duplicates in Seconds!
1️⃣ Select your data range.
2️⃣ Go to Data > Remove Duplicates.
3️⃣ Pick the columns to check for duplicates and hit OK—done!
🔍 Example:
✅ Got a list of emails with repeats? Remove Duplicates keeps only unique ones!
✅ Cleaning up sales data? Instantly get rid of double entries!
📌 Bonus: Use this trick to tidy up contact lists, inventory records, or survey responses—no formulas needed!
Like this post if you want more Excel and data hacks every week! 👍✨
Credits: https://whatsapp.com/channel/0029VaifY548qIzv0u1AHz3i
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