Data Analyst Interview Questions
1. Is indentation required in python?
Indentation is necessary for Python. It specifies a block of code. All code within loops, classes, functions, etc is specified within an indented block. It is usually done using four space characters. If your code is not indented necessarily, it will not execute accurately and will throw errors as well.
2. What are Entities and Relationships?
Entity: An entity can be a real-world object that can be easily identifiable. For example, in a college database, students, professors, workers, departments, and projects can be referred to as entities.
Relationships: Relations or links between entities that have something to do with each other. For example β The employeeβs table in a companyβs database can be associated with the salary table in the same database.
3. What is a stored procedure?
Stored Procedure is a function consists of many SQL statements to access the database system. Several SQL statements are consolidated into a stored procedure and execute them whenever and wherever required.
4. What is Auto Increment?
Auto increment keyword allows the user to create a unique number to be generated when a new record is inserted into the table. AUTO INCREMENT keyword can be used in Oracle and IDENTITY keyword can be used in SQL SERVER.
Mostly this keyword can be used whenever PRIMARY KEY is used.
5. Which operator is used in query for pattern matching?
LIKE operator is used for pattern matching, and it can be used as -.
1. % β Matches zero or more characters.
2. _(Underscore) β Matching exactly one character.
1. Is indentation required in python?
Indentation is necessary for Python. It specifies a block of code. All code within loops, classes, functions, etc is specified within an indented block. It is usually done using four space characters. If your code is not indented necessarily, it will not execute accurately and will throw errors as well.
2. What are Entities and Relationships?
Entity: An entity can be a real-world object that can be easily identifiable. For example, in a college database, students, professors, workers, departments, and projects can be referred to as entities.
Relationships: Relations or links between entities that have something to do with each other. For example β The employeeβs table in a companyβs database can be associated with the salary table in the same database.
3. What is a stored procedure?
Stored Procedure is a function consists of many SQL statements to access the database system. Several SQL statements are consolidated into a stored procedure and execute them whenever and wherever required.
4. What is Auto Increment?
Auto increment keyword allows the user to create a unique number to be generated when a new record is inserted into the table. AUTO INCREMENT keyword can be used in Oracle and IDENTITY keyword can be used in SQL SERVER.
Mostly this keyword can be used whenever PRIMARY KEY is used.
5. Which operator is used in query for pattern matching?
LIKE operator is used for pattern matching, and it can be used as -.
1. % β Matches zero or more characters.
2. _(Underscore) β Matching exactly one character.
π15β€3
1. Define the term 'Data Wrangling.
Data Wrangling is the process wherein raw data is cleaned, structured, and enriched into a desired usable format for better decision making. It involves discovering, structuring, cleaning, enriching, validating, and analyzing data. This process can turn and map out large amounts of data extracted from various sources into a more useful format.
2. What are the best methods for data cleaning?
Create a data cleaning plan by understanding where the common errors take place and keep all the communications open. Before working with the data, identify and remove the duplicates. This will lead to an easy and effective data analysis process.Focus on the accuracy of the data. Set cross-field validation, maintain the value types of data, and provide mandatory constraints.Normalize the data at the entry point so that it is less chaotic. You will be able to ensure that all information is standardized, leading to fewer errors on entry.
3. Explain the Type I and Type II errors in Statistics?
In Hypothesis testing, a Type I error occurs when the null hypothesis is rejected even if it is true. It is also known as a false positive.
A Type II error occurs when the null hypothesis is not rejected, even if it is false. It is also known as a false negative.
4. How do you make a dropdown list in MS Excel?
First, click on the Data tab that is present in the ribbon.Under the Data Tools group, select Data Validation.Then navigate to Settings > Allow > List.Select the source you want to provide as a list array.
5. State some ways to improve the performance of Tableau?
Use an Extract to make workbooks run faster.
Reduce the scope of data to decrease the volume of data.
Reduce the number of marks on the view to avoid information overload.
Hide unused fields.
Use Context filters.
Use indexing in tables and use the same fields for filtering.
Remove unnecessary calculations and sheets.
Data Wrangling is the process wherein raw data is cleaned, structured, and enriched into a desired usable format for better decision making. It involves discovering, structuring, cleaning, enriching, validating, and analyzing data. This process can turn and map out large amounts of data extracted from various sources into a more useful format.
2. What are the best methods for data cleaning?
Create a data cleaning plan by understanding where the common errors take place and keep all the communications open. Before working with the data, identify and remove the duplicates. This will lead to an easy and effective data analysis process.Focus on the accuracy of the data. Set cross-field validation, maintain the value types of data, and provide mandatory constraints.Normalize the data at the entry point so that it is less chaotic. You will be able to ensure that all information is standardized, leading to fewer errors on entry.
3. Explain the Type I and Type II errors in Statistics?
In Hypothesis testing, a Type I error occurs when the null hypothesis is rejected even if it is true. It is also known as a false positive.
A Type II error occurs when the null hypothesis is not rejected, even if it is false. It is also known as a false negative.
4. How do you make a dropdown list in MS Excel?
First, click on the Data tab that is present in the ribbon.Under the Data Tools group, select Data Validation.Then navigate to Settings > Allow > List.Select the source you want to provide as a list array.
5. State some ways to improve the performance of Tableau?
Use an Extract to make workbooks run faster.
Reduce the scope of data to decrease the volume of data.
Reduce the number of marks on the view to avoid information overload.
Hide unused fields.
Use Context filters.
Use indexing in tables and use the same fields for filtering.
Remove unnecessary calculations and sheets.
π20β€8π1
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β
Basic Level (Focuses on fundamental concepts and operations in SQL, including syntax, basic commands, and the definitions of key terms.)
1. Explain the difference between drop and truncate.
2. What are Constraints in SQL?
3. Describe the use of the SELECT statement in SQL.
4. What is a primary key in SQL?
5. Explain the difference between CHAR and VARCHAR data types in SQL.
6. What is a foreign key in SQL?
7. How do you use the GROUP BY statement in SQL?
8. What is a JOIN in SQL, and can you describe a scenario where you would use it?
9. How does the WHERE clause work in SQL?
10. Explain the use of the INSERT statement in SQL.
β Intermediate Level ( Involves more complex queries, including the use of sub-queries, joins, and functions. It requires a deeper understanding of SQL for data manipulation and analysis.)
1. Describe the Difference Between Window Functions and Aggregate Functions in SQL.
2. Write a SQL query to find the top three products with the highest revenue in the last quarter from a sales database.
3. What do you understand by sub-queries in SQL?
4. What is CTE in SQL?
5. Explain the use of the HAVING clause in SQL.
6. How do you implement pagination in SQL queries?
7. Describe the differences between INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.
8. Explain the concept of indexing in SQL and its benefits.
9. How can you prevent SQL injection in your queries?
10. Write a SQL query to find the second highest salary in a given table.
β Advanced Level (Covers topics related to database optimization, advanced data manipulation techniques, and understanding SQL's impact on database performance and design.)
1. Describe a SQL query challenge you faced related to optimizing database performance.
2. What is a Recursive Stored Procedure in SQL?
3. What are the subsets of SQL?
4. How do you use window functions for running totals and moving averages?
5. Explain the process and considerations for denormalizing a database.
6. Discuss the implications and solutions for dealing with NULL values in SQL operations.
7. How do you handle large datasets and optimize queries for big data in SQL?
8. Describe how to implement transaction control in SQL and its importance.
9. Explain the concept of materialized views in SQL and their use cases.
10. Discuss strategies for database sharding and partitioning in SQL and their impact on performance.
1. Explain the difference between drop and truncate.
2. What are Constraints in SQL?
3. Describe the use of the SELECT statement in SQL.
4. What is a primary key in SQL?
5. Explain the difference between CHAR and VARCHAR data types in SQL.
6. What is a foreign key in SQL?
7. How do you use the GROUP BY statement in SQL?
8. What is a JOIN in SQL, and can you describe a scenario where you would use it?
9. How does the WHERE clause work in SQL?
10. Explain the use of the INSERT statement in SQL.
β Intermediate Level ( Involves more complex queries, including the use of sub-queries, joins, and functions. It requires a deeper understanding of SQL for data manipulation and analysis.)
1. Describe the Difference Between Window Functions and Aggregate Functions in SQL.
2. Write a SQL query to find the top three products with the highest revenue in the last quarter from a sales database.
3. What do you understand by sub-queries in SQL?
4. What is CTE in SQL?
5. Explain the use of the HAVING clause in SQL.
6. How do you implement pagination in SQL queries?
7. Describe the differences between INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.
8. Explain the concept of indexing in SQL and its benefits.
9. How can you prevent SQL injection in your queries?
10. Write a SQL query to find the second highest salary in a given table.
β Advanced Level (Covers topics related to database optimization, advanced data manipulation techniques, and understanding SQL's impact on database performance and design.)
1. Describe a SQL query challenge you faced related to optimizing database performance.
2. What is a Recursive Stored Procedure in SQL?
3. What are the subsets of SQL?
4. How do you use window functions for running totals and moving averages?
5. Explain the process and considerations for denormalizing a database.
6. Discuss the implications and solutions for dealing with NULL values in SQL operations.
7. How do you handle large datasets and optimize queries for big data in SQL?
8. Describe how to implement transaction control in SQL and its importance.
9. Explain the concept of materialized views in SQL and their use cases.
10. Discuss strategies for database sharding and partitioning in SQL and their impact on performance.
π32β€7π4π2π€1
Excel Interview Questions with Answers
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Here are the questions With Answers β¨
1. Write a query to get the EmpFname from the EmployeeInfo table in the upper case using the alias name as EmpName.
[
SELECT UPPER(EmpFname) AS EmpName FROM EmployeeInfo;
]
2. Write a query to get the number of employees working in the department βHRβ.
[
SELECT COUNT(*) FROM EmployeeInfo WHERE Department = 'HR';
]
3. What query will you write to fetch the current date?
[
-- For SQL Server:
SELECT GETDATE();
-- For MySQL:
SELECT SYSDATE();
]
4. Write a query to fetch only the place name (string before brackets) from the Address column of the EmployeeInfo table.
[
-- Using MID function in MySQL:
SELECT MID(Address, 1, LOCATE('(', Address) - 1) FROM EmployeeInfo;
-- Using SUBSTRING function:
SELECT SUBSTRING(Address, 1, CHARINDEX('(', Address) - 1) FROM EmployeeInfo;
]
5. Write a query to create a new table whose data and structure are copied from another table.
[
-- Using SELECT INTO in SQL Server:
SELECT * INTO NewTable FROM EmployeeInfo WHERE 1 = 0;
-- Using CREATE TABLE AS in MySQL:
CREATE TABLE NewTable AS SELECT * FROM EmployeeInfo;
]
6. Write a query to display the names of employees that begin with βSβ.
[
SELECT * FROM EmployeeInfo WHERE EmpFname LIKE 'S%';
]
7. Write a query to retrieve the top N records.
[
-- Using TOP in SQL Server:
SELECT TOP N * FROM EmployeePosition ORDER BY Salary DESC;
-- Using LIMIT in MySQL:
SELECT * FROM EmployeePosition ORDER BY Salary DESC LIMIT N;
]
8. Write a query to obtain relevant records from the EmployeeInfo table ordered by Department in ascending order and EmpLname in descending order.
[
SELECT * FROM EmployeeInfo ORDER BY Department ASC, EmpLname DESC;
]
9. Write a query to get the details of employees whose EmpFname ends with βAβ.
[
SELECT * FROM EmployeeInfo WHERE EmpFname LIKE '%A';
]
10. Create a query to fetch details of employees having βDELHIβ as their address.
[
SELECT * FROM EmployeeInfo WHERE Address LIKE '%DELHI%';
]
11. Write a query to fetch all employees who also hold the managerial position.
[
SELECT E.EmpFname, E.EmpLname, P.EmpPosition
FROM EmployeeInfo E
INNER JOIN EmployeePosition P ON E.EmpID = P.EmpID
WHERE P.EmpPosition = 'Manager';
]
12. Create a query to generate the first and last records from the EmployeeInfo table.
[
-- First record:
SELECT * FROM EmployeeInfo WHERE EmpID = (SELECT MIN(EmpID) FROM EmployeeInfo);
-- Last record:
SELECT * FROM EmployeeInfo WHERE EmpID = (SELECT MAX(EmpID) FROM EmployeeInfo);
]
13. Create a query to check if the passed value to the query follows the EmployeeInfo and EmployeePosition tablesβ date format.
[
SELECT ISDATE('01/04/2020') AS "MM/DD/YY";
]
14. Create a query to obtain display employees having salaries equal to or greater than 150000.
[
SELECT EmpName FROM EmployeePosition WHERE Salary >= 150000;
]
15. Write a query to fetch the year using a date.
[
SELECT YEAR(GETDATE()) AS "Year";
]
16. Create an SQL query to fetch EmpPosition and the total salary paid for each employee position.
[
SELECT EmpPosition, SUM(Salary) FROM EmployeePosition GROUP BY EmpPosition;
]
17. Write a query to find duplicate records from a table.
[
SELECT EmpID, EmpFname, Department, COUNT(*)
FROM EmployeeInfo
GROUP BY EmpID, EmpFname, Department
HAVING COUNT(*) > 1;
]
18. Create a query to fetch the third-highest salary from the EmpPosition table.
[
SELECT TOP 1 Salary
FROM (
SELECT TOP 3 Salary
FROM EmpPosition
ORDER BY Salary DESC
) AS ThirdHighestSalary
ORDER BY Salary ASC;
]
19. Write an SQL query to find even and odd records in the EmployeeInfo table.
[
-- Even records:
SELECT EmpID FROM (SELECT ROW_NUMBER() OVER (ORDER BY EmpID) AS rowno, EmpID FROM EmployeeInfo) AS T1 WHERE MOD(rowno, 2) = 0;
-- Odd records:
SELECT EmpID FROM (SELECT ROW_NUMBER() OVER (ORDER BY EmpID) AS rowno, EmpID FROM EmployeeInfo) AS T1 WHERE MOD(rowno, 2) = 1;
]
20. Create a query to fetch the list of employees of the same department.
[
SELECT DISTINCT E1.EmpID, E1.EmpFname, E1.Department
FROM EmployeeInfo E1
INNER JOIN EmployeeInfo E2 ON E1.Department = E2.
]
1. Write a query to get the EmpFname from the EmployeeInfo table in the upper case using the alias name as EmpName.
[
SELECT UPPER(EmpFname) AS EmpName FROM EmployeeInfo;
]
2. Write a query to get the number of employees working in the department βHRβ.
[
SELECT COUNT(*) FROM EmployeeInfo WHERE Department = 'HR';
]
3. What query will you write to fetch the current date?
[
-- For SQL Server:
SELECT GETDATE();
-- For MySQL:
SELECT SYSDATE();
]
4. Write a query to fetch only the place name (string before brackets) from the Address column of the EmployeeInfo table.
[
-- Using MID function in MySQL:
SELECT MID(Address, 1, LOCATE('(', Address) - 1) FROM EmployeeInfo;
-- Using SUBSTRING function:
SELECT SUBSTRING(Address, 1, CHARINDEX('(', Address) - 1) FROM EmployeeInfo;
]
5. Write a query to create a new table whose data and structure are copied from another table.
[
-- Using SELECT INTO in SQL Server:
SELECT * INTO NewTable FROM EmployeeInfo WHERE 1 = 0;
-- Using CREATE TABLE AS in MySQL:
CREATE TABLE NewTable AS SELECT * FROM EmployeeInfo;
]
6. Write a query to display the names of employees that begin with βSβ.
[
SELECT * FROM EmployeeInfo WHERE EmpFname LIKE 'S%';
]
7. Write a query to retrieve the top N records.
[
-- Using TOP in SQL Server:
SELECT TOP N * FROM EmployeePosition ORDER BY Salary DESC;
-- Using LIMIT in MySQL:
SELECT * FROM EmployeePosition ORDER BY Salary DESC LIMIT N;
]
8. Write a query to obtain relevant records from the EmployeeInfo table ordered by Department in ascending order and EmpLname in descending order.
[
SELECT * FROM EmployeeInfo ORDER BY Department ASC, EmpLname DESC;
]
9. Write a query to get the details of employees whose EmpFname ends with βAβ.
[
SELECT * FROM EmployeeInfo WHERE EmpFname LIKE '%A';
]
10. Create a query to fetch details of employees having βDELHIβ as their address.
[
SELECT * FROM EmployeeInfo WHERE Address LIKE '%DELHI%';
]
11. Write a query to fetch all employees who also hold the managerial position.
[
SELECT E.EmpFname, E.EmpLname, P.EmpPosition
FROM EmployeeInfo E
INNER JOIN EmployeePosition P ON E.EmpID = P.EmpID
WHERE P.EmpPosition = 'Manager';
]
12. Create a query to generate the first and last records from the EmployeeInfo table.
[
-- First record:
SELECT * FROM EmployeeInfo WHERE EmpID = (SELECT MIN(EmpID) FROM EmployeeInfo);
-- Last record:
SELECT * FROM EmployeeInfo WHERE EmpID = (SELECT MAX(EmpID) FROM EmployeeInfo);
]
13. Create a query to check if the passed value to the query follows the EmployeeInfo and EmployeePosition tablesβ date format.
[
SELECT ISDATE('01/04/2020') AS "MM/DD/YY";
]
14. Create a query to obtain display employees having salaries equal to or greater than 150000.
[
SELECT EmpName FROM EmployeePosition WHERE Salary >= 150000;
]
15. Write a query to fetch the year using a date.
[
SELECT YEAR(GETDATE()) AS "Year";
]
16. Create an SQL query to fetch EmpPosition and the total salary paid for each employee position.
[
SELECT EmpPosition, SUM(Salary) FROM EmployeePosition GROUP BY EmpPosition;
]
17. Write a query to find duplicate records from a table.
[
SELECT EmpID, EmpFname, Department, COUNT(*)
FROM EmployeeInfo
GROUP BY EmpID, EmpFname, Department
HAVING COUNT(*) > 1;
]
18. Create a query to fetch the third-highest salary from the EmpPosition table.
[
SELECT TOP 1 Salary
FROM (
SELECT TOP 3 Salary
FROM EmpPosition
ORDER BY Salary DESC
) AS ThirdHighestSalary
ORDER BY Salary ASC;
]
19. Write an SQL query to find even and odd records in the EmployeeInfo table.
[
-- Even records:
SELECT EmpID FROM (SELECT ROW_NUMBER() OVER (ORDER BY EmpID) AS rowno, EmpID FROM EmployeeInfo) AS T1 WHERE MOD(rowno, 2) = 0;
-- Odd records:
SELECT EmpID FROM (SELECT ROW_NUMBER() OVER (ORDER BY EmpID) AS rowno, EmpID FROM EmployeeInfo) AS T1 WHERE MOD(rowno, 2) = 1;
]
20. Create a query to fetch the list of employees of the same department.
[
SELECT DISTINCT E1.EmpID, E1.EmpFname, E1.Department
FROM EmployeeInfo E1
INNER JOIN EmployeeInfo E2 ON E1.Department = E2.
]
π47β€4π1π€1π1
Forwarded from Data Analytics
Here are the different ways to create views in Tableau
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Drag fields from the Data pane and drop them onto the cards and shelves that are part of every Tableau worksheet.Double-click one or more fields in the Data pane.Select one or more fields in the Data pane and then choose a chart type from Show Me, which identifies the chart types that are appropriate for the fields you selected.Drop a field on the Drop field here grid, to start creating a view from a tabular perspective.π7π1
Most asked SQL interview questions for Data Analyst/Data Engineer role-
1 - What is SQL and what are its main features?
2 - Order of writing SQL query?
3- Order of execution of SQL query?
4- What are some of the most common SQL commands?
5- Whatβs a primary key & foreign key?
6 - All types of joins and questions on their outputs?
7 - Explain all window functions and difference between them?
8 - What is stored procedure?
9 - Difference between stored procedure & Functions in SQL?
10 - What is trigger in SQL?
11 - Difference between where and having?
1 - What is SQL and what are its main features?
2 - Order of writing SQL query?
3- Order of execution of SQL query?
4- What are some of the most common SQL commands?
5- Whatβs a primary key & foreign key?
6 - All types of joins and questions on their outputs?
7 - Explain all window functions and difference between them?
8 - What is stored procedure?
9 - Difference between stored procedure & Functions in SQL?
10 - What is trigger in SQL?
11 - Difference between where and having?
π18π4β€3
Data Analyst Interview Resources
Most asked SQL interview questions for Data Analyst/Data Engineer role- 1 - What is SQL and what are its main features? 2 - Order of writing SQL query? 3- Order of execution of SQL query? 4- What are some of the most common SQL commands? 5- Whatβs aβ¦
Answers to above SQL interview questions:
1. SQL (Structured Query Language) is a programming language used to manage and manipulate relational databases. Its main features include querying and managing data, defining and modifying database structures, and controlling access to data.
2. The order of writing an SQL query typically starts with the SELECT clause to specify columns, followed by the FROM clause to specify tables, then optional clauses like WHERE (for filtering), GROUP BY (for grouping), HAVING (for filtering after grouping), ORDER BY (for sorting), and finally, LIMIT/OFFSET (for pagination).
3. The order of execution of an SQL query is generally: FROM (specify data sources), WHERE (apply conditions), GROUP BY (perform grouping), HAVING (filter grouped data), SELECT (retrieve columns), DISTINCT (remove duplicates), ORDER BY (sort results), and finally, LIMIT/OFFSET (apply result limits).
4. Common SQL commands include SELECT (retrieve data), INSERT (add new records), UPDATE (modify existing records), DELETE (remove records), CREATE TABLE (create a new table), ALTER TABLE (modify existing table structure), and DROP TABLE (delete a table).
5. A primary key uniquely identifies each record in a table and ensures no duplicate values.
A foreign key establishes a link between two tables, referencing the primary key of another table to maintain referential integrity.
6. SQL joins include INNER JOIN (returns rows where there is a match in both tables), LEFT JOIN (returns all rows from the left table and matching rows from the right table), RIGHT JOIN (returns all rows from the right table and matching rows from the left table), and FULL JOIN (returns all rows when there is a match in either table).
7. Window functions (like ROW_NUMBER, RANK, DENSE_RANK, etc.) operate over a window of rows and can perform calculations across rows related to the current row. Differences lie in how they assign ranks or sequence numbers based on specified criteria within the window.
8. A stored procedure is a precompiled collection of SQL statements and procedural logic stored in the database and executed as a unit. It can accept input parameters, perform operations, and return results.
9. The main difference between stored procedures and functions in SQL is that stored procedures can perform DML (Data Manipulation Language) operations, such as INSERT, UPDATE, and DELETE, whereas functions are primarily used to compute values and cannot change data.
10. A trigger in SQL is a special type of stored procedure that automatically executes when a specific event (like INSERT, UPDATE, or DELETE) occurs on a table. Triggers are used to enforce business rules, maintain data integrity, or automate tasks.
11. The WHERE clause is used to filter rows before any groupings are made
(typically used in SELECT, UPDATE, or DELETE statements).
The HAVING clause is used to filter rows after the grouping has been done, based on aggregate values
(typically used in SELECT statements with GROUP BY).
Like β€οΈ this post if you need more data analytics interview Questions with Answers
1. SQL (Structured Query Language) is a programming language used to manage and manipulate relational databases. Its main features include querying and managing data, defining and modifying database structures, and controlling access to data.
2. The order of writing an SQL query typically starts with the SELECT clause to specify columns, followed by the FROM clause to specify tables, then optional clauses like WHERE (for filtering), GROUP BY (for grouping), HAVING (for filtering after grouping), ORDER BY (for sorting), and finally, LIMIT/OFFSET (for pagination).
3. The order of execution of an SQL query is generally: FROM (specify data sources), WHERE (apply conditions), GROUP BY (perform grouping), HAVING (filter grouped data), SELECT (retrieve columns), DISTINCT (remove duplicates), ORDER BY (sort results), and finally, LIMIT/OFFSET (apply result limits).
4. Common SQL commands include SELECT (retrieve data), INSERT (add new records), UPDATE (modify existing records), DELETE (remove records), CREATE TABLE (create a new table), ALTER TABLE (modify existing table structure), and DROP TABLE (delete a table).
5. A primary key uniquely identifies each record in a table and ensures no duplicate values.
A foreign key establishes a link between two tables, referencing the primary key of another table to maintain referential integrity.
6. SQL joins include INNER JOIN (returns rows where there is a match in both tables), LEFT JOIN (returns all rows from the left table and matching rows from the right table), RIGHT JOIN (returns all rows from the right table and matching rows from the left table), and FULL JOIN (returns all rows when there is a match in either table).
7. Window functions (like ROW_NUMBER, RANK, DENSE_RANK, etc.) operate over a window of rows and can perform calculations across rows related to the current row. Differences lie in how they assign ranks or sequence numbers based on specified criteria within the window.
8. A stored procedure is a precompiled collection of SQL statements and procedural logic stored in the database and executed as a unit. It can accept input parameters, perform operations, and return results.
9. The main difference between stored procedures and functions in SQL is that stored procedures can perform DML (Data Manipulation Language) operations, such as INSERT, UPDATE, and DELETE, whereas functions are primarily used to compute values and cannot change data.
10. A trigger in SQL is a special type of stored procedure that automatically executes when a specific event (like INSERT, UPDATE, or DELETE) occurs on a table. Triggers are used to enforce business rules, maintain data integrity, or automate tasks.
11. The WHERE clause is used to filter rows before any groupings are made
(typically used in SELECT, UPDATE, or DELETE statements).
The HAVING clause is used to filter rows after the grouping has been done, based on aggregate values
(typically used in SELECT statements with GROUP BY).
Like β€οΈ this post if you need more data analytics interview Questions with Answers
π18β€16π€1
Here's a list of commonly asked data analyst interview questions:
1. Tell me about yourself : This is often the opener, allowing you to summarize your background, skills, and experiences.
2. What is the difference between data analytics and data science?: Be ready to explain these terms and how they differ.
3. Describe a typical data analysis process you follow: Walk through steps like data collection, cleaning, analysis, and interpretation.
4. What programming languages are you proficient in?: Typically SQL, Python, R are common; mention any others you're familiar with.
5. How do you handle missing or incomplete data?: Discuss methods like imputation or excluding records based on criteria.
6. Explain a time when you used data to solve a problem: Provide a detailed example showcasing your analytical skills.
7. What data visualization tools have you used?: Tableau, Power BI, or others; discuss your experience.
8. How do you ensure the quality and accuracy of your analytical work?: Mention techniques like validation, peer reviews, or data audits.
9. What is your approach to presenting complex data findings to non-technical stakeholders?: Highlight your communication skills and ability to simplify complex information.
10. Describe a challenging data project you've worked on: Explain the project, challenges faced, and how you overcame them.
11. How do you stay updated with the latest trends in data analytics?: Talk about blogs, courses, or communities you follow.
12. What statistical techniques are you familiar with?: Regression, clustering, hypothesis testing, etc.; explain when you've used them.
13. How would you assess the effectiveness of a new data model?: Discuss metrics like accuracy, precision, recall, etc.
14. Give an example of a time when you dealt with a large dataset: Explain how you managed and processed the data efficiently.
15. Why do you want to work for this company?: Tailor your response to highlight why their industry or culture appeals to you
1. Tell me about yourself : This is often the opener, allowing you to summarize your background, skills, and experiences.
2. What is the difference between data analytics and data science?: Be ready to explain these terms and how they differ.
3. Describe a typical data analysis process you follow: Walk through steps like data collection, cleaning, analysis, and interpretation.
4. What programming languages are you proficient in?: Typically SQL, Python, R are common; mention any others you're familiar with.
5. How do you handle missing or incomplete data?: Discuss methods like imputation or excluding records based on criteria.
6. Explain a time when you used data to solve a problem: Provide a detailed example showcasing your analytical skills.
7. What data visualization tools have you used?: Tableau, Power BI, or others; discuss your experience.
8. How do you ensure the quality and accuracy of your analytical work?: Mention techniques like validation, peer reviews, or data audits.
9. What is your approach to presenting complex data findings to non-technical stakeholders?: Highlight your communication skills and ability to simplify complex information.
10. Describe a challenging data project you've worked on: Explain the project, challenges faced, and how you overcame them.
11. How do you stay updated with the latest trends in data analytics?: Talk about blogs, courses, or communities you follow.
12. What statistical techniques are you familiar with?: Regression, clustering, hypothesis testing, etc.; explain when you've used them.
13. How would you assess the effectiveness of a new data model?: Discuss metrics like accuracy, precision, recall, etc.
14. Give an example of a time when you dealt with a large dataset: Explain how you managed and processed the data efficiently.
15. Why do you want to work for this company?: Tailor your response to highlight why their industry or culture appeals to you
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1. What do you understand about the E-R model?
Answer: E-R model is an Entity-Relationship model which defines the conceptual view of the database.
The E-R model basically shows the real-world entities and their association/relations. Entities here represent the set of attributes in the database.
2. Explain the terms βAttributeβ and βRelationsβ
Answer:
Attribute is described as the properties or characteristics of an entity. For Example, Employee ID, Employee Name, Age, etc., can be attributes of the entity Employee.
Relation is a two-dimensional table containing a number of rows and columns where every row represents a record of the relation. Here, rows are also known as βTuplesβ and columns are known as βAttributesβ.
3. What is the Database transaction?
Answer: Sequence of operation performed which changes the consistent state of the database to another is known as the database transaction. After the completion of the transaction, either the successful completion is reflected in the system or the transaction fails and no change is reflected.
4. What do you understand about βAtomicityβ and βAggregationβ?
Answer: Atomicity is the condition where either all the actions of the transaction are performed or none. This means, when there is an incomplete transaction, the database management system itself will undo the effects done by the incomplete transaction.
Aggregation is the concept of expressing the relationship with the collection of entities and their relationships.
Answer: E-R model is an Entity-Relationship model which defines the conceptual view of the database.
The E-R model basically shows the real-world entities and their association/relations. Entities here represent the set of attributes in the database.
2. Explain the terms βAttributeβ and βRelationsβ
Answer:
Attribute is described as the properties or characteristics of an entity. For Example, Employee ID, Employee Name, Age, etc., can be attributes of the entity Employee.
Relation is a two-dimensional table containing a number of rows and columns where every row represents a record of the relation. Here, rows are also known as βTuplesβ and columns are known as βAttributesβ.
3. What is the Database transaction?
Answer: Sequence of operation performed which changes the consistent state of the database to another is known as the database transaction. After the completion of the transaction, either the successful completion is reflected in the system or the transaction fails and no change is reflected.
4. What do you understand about βAtomicityβ and βAggregationβ?
Answer: Atomicity is the condition where either all the actions of the transaction are performed or none. This means, when there is an incomplete transaction, the database management system itself will undo the effects done by the incomplete transaction.
Aggregation is the concept of expressing the relationship with the collection of entities and their relationships.
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1. What are the ways to detect outliers?
Outliers are detected using two methods:
Box Plot Method: According to this method, the value is considered an outlier if it exceeds or falls below 1.5*IQR (interquartile range), that is, if it lies above the top quartile (Q3) or below the bottom quartile (Q1).
Standard Deviation Method: According to this method, an outlier is defined as a value that is greater or lower than the mean Β± (3*standard deviation).
2. What is a Recursive Stored Procedure?
A stored procedure that calls itself until a boundary condition is reached, is called a recursive stored procedure. This recursive function helps the programmers to deploy the same set of code several times as and when required.
3. What is the shortcut to add a filter to a table in EXCEL?
The filter mechanism is used when you want to display only specific data from the entire dataset. By doing so, there is no change being made to the data. The shortcut to add a filter to a table is Ctrl+Shift+L.
4. What is DAX in Power BI?
DAX stands for Data Analysis Expressions. It's a collection of functions, operators, and constants used in formulas to calculate and return values. In other words, it helps you create new info from data you already have.
Outliers are detected using two methods:
Box Plot Method: According to this method, the value is considered an outlier if it exceeds or falls below 1.5*IQR (interquartile range), that is, if it lies above the top quartile (Q3) or below the bottom quartile (Q1).
Standard Deviation Method: According to this method, an outlier is defined as a value that is greater or lower than the mean Β± (3*standard deviation).
2. What is a Recursive Stored Procedure?
A stored procedure that calls itself until a boundary condition is reached, is called a recursive stored procedure. This recursive function helps the programmers to deploy the same set of code several times as and when required.
3. What is the shortcut to add a filter to a table in EXCEL?
The filter mechanism is used when you want to display only specific data from the entire dataset. By doing so, there is no change being made to the data. The shortcut to add a filter to a table is Ctrl+Shift+L.
4. What is DAX in Power BI?
DAX stands for Data Analysis Expressions. It's a collection of functions, operators, and constants used in formulas to calculate and return values. In other words, it helps you create new info from data you already have.
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1. Define the term 'Data Wrangling.
Data Wrangling is the process wherein raw data is cleaned, structured, and enriched into a desired usable format for better decision making. It involves discovering, structuring, cleaning, enriching, validating, and analyzing data. This process can turn and map out large amounts of data extracted from various sources into a more useful format.
2. What are the best methods for data cleaning?
Create a data cleaning plan by understanding where the common errors take place and keep all the communications open. Before working with the data, identify and remove the duplicates. This will lead to an easy and effective data analysis process.Focus on the accuracy of the data. Set cross-field validation, maintain the value types of data, and provide mandatory constraints.Normalize the data at the entry point so that it is less chaotic. You will be able to ensure that all information is standardized, leading to fewer errors on entry.
3. Explain 4 steps to use CTE in sql.
All CTE starts with "with" clause.
After with you need to define CTE name and the field names. For instance in the below code snippet I have 3 fields Count,Column and Id. The name of CTE is "MyTemp".
Once you have defined CTE we need to specify the SQL which will give the result for the CTE.
Finally you can use the CTE in your SQL query.
Data Wrangling is the process wherein raw data is cleaned, structured, and enriched into a desired usable format for better decision making. It involves discovering, structuring, cleaning, enriching, validating, and analyzing data. This process can turn and map out large amounts of data extracted from various sources into a more useful format.
2. What are the best methods for data cleaning?
Create a data cleaning plan by understanding where the common errors take place and keep all the communications open. Before working with the data, identify and remove the duplicates. This will lead to an easy and effective data analysis process.Focus on the accuracy of the data. Set cross-field validation, maintain the value types of data, and provide mandatory constraints.Normalize the data at the entry point so that it is less chaotic. You will be able to ensure that all information is standardized, leading to fewer errors on entry.
3. Explain 4 steps to use CTE in sql.
All CTE starts with "with" clause.
After with you need to define CTE name and the field names. For instance in the below code snippet I have 3 fields Count,Column and Id. The name of CTE is "MyTemp".
Once you have defined CTE we need to specify the SQL which will give the result for the CTE.
Finally you can use the CTE in your SQL query.
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5β£ Important data analysis interview questions
Explain the Data Analysis Process:
Walk me through the typical steps you follow when conducting a data analysis project.
What is the Difference Between Descriptive and Inferential Statistics?:
Can you explain the distinction between descriptive statistics and inferential statistics and provide examples of when each is used?
How Do You Handle Missing Data in a Dataset?:
What strategies and techniques do you use to deal with missing or incomplete data in a dataset?
What is Exploratory Data Analysis (EDA)?:
Describe what EDA is and the various methods and visualizations you employ during this phase of data analysis.
Give an Example of a Time When You Used Data Analysis to Solve a Real-World Problem:
Share a specific project or scenario where you applied data analysis techniques to address a practical problem. What was the outcome, and what tools or methodologies did you use?
Like this post if you also need the answers for the above questions β€οΈπ
Explain the Data Analysis Process:
Walk me through the typical steps you follow when conducting a data analysis project.
What is the Difference Between Descriptive and Inferential Statistics?:
Can you explain the distinction between descriptive statistics and inferential statistics and provide examples of when each is used?
How Do You Handle Missing Data in a Dataset?:
What strategies and techniques do you use to deal with missing or incomplete data in a dataset?
What is Exploratory Data Analysis (EDA)?:
Describe what EDA is and the various methods and visualizations you employ during this phase of data analysis.
Give an Example of a Time When You Used Data Analysis to Solve a Real-World Problem:
Share a specific project or scenario where you applied data analysis techniques to address a practical problem. What was the outcome, and what tools or methodologies did you use?
Like this post if you also need the answers for the above questions β€οΈπ
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Glad to see the amazing response from you guys π
Here are the answers to these questions
Explain the Data Analysis Process:
The data analysis process typically involves several key steps. These steps include:
Data Collection: Gathering the relevant data from various sources.
Data Cleaning: Removing inconsistencies, handling missing values, and ensuring data quality.
Data Exploration: Using descriptive statistics, visualizations, and initial insights to understand the data.
Data Transformation: Preprocessing, feature engineering, and data formatting.
Data Modeling: Applying statistical or machine learning models to extract patterns or make predictions.
Evaluation: Assessing the model's performance and validity.
Interpretation: Drawing meaningful conclusions from the analysis.
Communication: Presenting findings to stakeholders effectively.
What is the Difference Between Descriptive and Inferential Statistics?:
Descriptive statistics summarize and describe data, providing insights into its main characteristics. Examples include measures like mean, median, and standard deviation.
Inferential statistics, on the other hand, involve making predictions or drawing conclusions about a population based on a sample of data. Hypothesis testing and confidence intervals are common inferential statistical techniques.
How Do You Handle Missing Data in a Dataset?:
Handling missing data is crucial for accurate analysis:
I start by identifying the extent of missing data.
For numerical data, I might impute missing values with the mean, median, or a predictive model.
For categorical data, I often use mode imputation.
If appropriate, I consider removing rows with too much missing data.
I also explore if the missingness pattern itself holds valuable information.
What is Exploratory Data Analysis (EDA)?:
EDA is the process of visually and statistically exploring a dataset to understand its characteristics:
I begin with summary statistics, histograms, and box plots to identify data trends.
I create scatterplots and correlation matrices to understand relationships.
Outlier detection and data distribution analysis are also part of EDA.
The goal is to gain insights, identify patterns, and inform subsequent analysis steps.
Give an Example of a Time When You Used Data Analysis to Solve a Real-World Problem:
In a previous role, I worked for an e-commerce company, and we wanted to reduce shopping cart abandonment rates. I conducted a data analysis project:
Collected user data, including browsing behavior, demographics, and purchase history.
Cleaned and preprocessed the data.
Explored the data through visualizations and statistical tests.
Built a predictive model to identify factors contributing to cart abandonment.
Found that longer page load times were a significant factor.
Proposed optimizations to reduce load times, resulting in a 15% decrease in cart abandonment rates over a quarter.
Hope it helps :)
Here are the answers to these questions
Explain the Data Analysis Process:
The data analysis process typically involves several key steps. These steps include:
Data Collection: Gathering the relevant data from various sources.
Data Cleaning: Removing inconsistencies, handling missing values, and ensuring data quality.
Data Exploration: Using descriptive statistics, visualizations, and initial insights to understand the data.
Data Transformation: Preprocessing, feature engineering, and data formatting.
Data Modeling: Applying statistical or machine learning models to extract patterns or make predictions.
Evaluation: Assessing the model's performance and validity.
Interpretation: Drawing meaningful conclusions from the analysis.
Communication: Presenting findings to stakeholders effectively.
What is the Difference Between Descriptive and Inferential Statistics?:
Descriptive statistics summarize and describe data, providing insights into its main characteristics. Examples include measures like mean, median, and standard deviation.
Inferential statistics, on the other hand, involve making predictions or drawing conclusions about a population based on a sample of data. Hypothesis testing and confidence intervals are common inferential statistical techniques.
How Do You Handle Missing Data in a Dataset?:
Handling missing data is crucial for accurate analysis:
I start by identifying the extent of missing data.
For numerical data, I might impute missing values with the mean, median, or a predictive model.
For categorical data, I often use mode imputation.
If appropriate, I consider removing rows with too much missing data.
I also explore if the missingness pattern itself holds valuable information.
What is Exploratory Data Analysis (EDA)?:
EDA is the process of visually and statistically exploring a dataset to understand its characteristics:
I begin with summary statistics, histograms, and box plots to identify data trends.
I create scatterplots and correlation matrices to understand relationships.
Outlier detection and data distribution analysis are also part of EDA.
The goal is to gain insights, identify patterns, and inform subsequent analysis steps.
Give an Example of a Time When You Used Data Analysis to Solve a Real-World Problem:
In a previous role, I worked for an e-commerce company, and we wanted to reduce shopping cart abandonment rates. I conducted a data analysis project:
Collected user data, including browsing behavior, demographics, and purchase history.
Cleaned and preprocessed the data.
Explored the data through visualizations and statistical tests.
Built a predictive model to identify factors contributing to cart abandonment.
Found that longer page load times were a significant factor.
Proposed optimizations to reduce load times, resulting in a 15% decrease in cart abandonment rates over a quarter.
Hope it helps :)
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https://t.iss.one/caseinterviewscracked/17
https://t.iss.one/caseinterviewscracked/17
If you're looking to build a career in Data Analytics but feel unsure about where to start, this post is for you.
It's important to know that you don't need to spend money on expensive courses to succeed in this field.
Many posts you see on LinkedIn promoting paid courses are often shared by individuals who are either trying to sell their own products or are being compensated to endorse these courses.
Through this post, I will share with you everything you need to start your data journey absolutely free.
π Source
Hope it helps :)
It's important to know that you don't need to spend money on expensive courses to succeed in this field.
Many posts you see on LinkedIn promoting paid courses are often shared by individuals who are either trying to sell their own products or are being compensated to endorse these courses.
Through this post, I will share with you everything you need to start your data journey absolutely free.
π Source
Hope it helps :)
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1. Define the term 'Data Wrangling.
Data Wrangling is the process wherein raw data is cleaned, structured, and enriched into a desired usable format for better decision making. It involves discovering, structuring, cleaning, enriching, validating, and analyzing data. This process can turn and map out large amounts of data extracted from various sources into a more useful format.
2. What are the best methods for data cleaning?
Create a data cleaning plan by understanding where the common errors take place and keep all the communications open. Before working with the data, identify and remove the duplicates. This will lead to an easy and effective data analysis process.Focus on the accuracy of the data. Set cross-field validation, maintain the value types of data, and provide mandatory constraints.Normalize the data at the entry point so that it is less chaotic. You will be able to ensure that all information is standardized, leading to fewer errors on entry.
3. Explain the Type I and Type II errors in Statistics?
In Hypothesis testing, a Type I error occurs when the null hypothesis is rejected even if it is true. It is also known as a false positive.
A Type II error occurs when the null hypothesis is not rejected, even if it is false. It is also known as a false negative.
4. How do you make a dropdown list in MS Excel?
First, click on the Data tab that is present in the ribbon.Under the Data Tools group, select Data Validation.Then navigate to Settings > Allow > List.Select the source you want to provide as a list array.
5. State some ways to improve the performance of Tableau?
Use an Extract to make workbooks run faster.
Reduce the scope of data to decrease the volume of data.
Reduce the number of marks on the view to avoid information overload.
Hide unused fields.
Use Context filters.
Use indexing in tables and use the same fields for filtering.
Remove unnecessary calculations and sheets.
Data Wrangling is the process wherein raw data is cleaned, structured, and enriched into a desired usable format for better decision making. It involves discovering, structuring, cleaning, enriching, validating, and analyzing data. This process can turn and map out large amounts of data extracted from various sources into a more useful format.
2. What are the best methods for data cleaning?
Create a data cleaning plan by understanding where the common errors take place and keep all the communications open. Before working with the data, identify and remove the duplicates. This will lead to an easy and effective data analysis process.Focus on the accuracy of the data. Set cross-field validation, maintain the value types of data, and provide mandatory constraints.Normalize the data at the entry point so that it is less chaotic. You will be able to ensure that all information is standardized, leading to fewer errors on entry.
3. Explain the Type I and Type II errors in Statistics?
In Hypothesis testing, a Type I error occurs when the null hypothesis is rejected even if it is true. It is also known as a false positive.
A Type II error occurs when the null hypothesis is not rejected, even if it is false. It is also known as a false negative.
4. How do you make a dropdown list in MS Excel?
First, click on the Data tab that is present in the ribbon.Under the Data Tools group, select Data Validation.Then navigate to Settings > Allow > List.Select the source you want to provide as a list array.
5. State some ways to improve the performance of Tableau?
Use an Extract to make workbooks run faster.
Reduce the scope of data to decrease the volume of data.
Reduce the number of marks on the view to avoid information overload.
Hide unused fields.
Use Context filters.
Use indexing in tables and use the same fields for filtering.
Remove unnecessary calculations and sheets.
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