Most Asked SQL Interview Questions at MAANG Companies๐ฅ๐ฅ
Preparing for an SQL Interview at MAANG Companies? Here are some crucial SQL Questions you should be ready to tackle:
1. How do you retrieve all columns from a table?
SELECT * FROM table_name;
2. What SQL statement is used to filter records?
SELECT * FROM table_name
WHERE condition;
The WHERE clause is used to filter records based on a specified condition.
3. How can you join multiple tables? Describe different types of JOINs.
SELECT columns
FROM table1
JOIN table2 ON table1.column = table2.column
JOIN table3 ON table2.column = table3.column;
Types of JOINs:
1. INNER JOIN: Returns records with matching values in both tables
SELECT * FROM table1
INNER JOIN table2 ON table1.column = table2.column;
2. LEFT JOIN: Returns all records from the left table & matched records from the right table. Unmatched records will have NULL values.
SELECT * FROM table1
LEFT JOIN table2 ON table1.column = table2.column;
3. RIGHT JOIN: Returns all records from the right table & matched records from the left table. Unmatched records will have NULL values.
SELECT * FROM table1
RIGHT JOIN table2 ON table1.column = table2.column;
4. FULL JOIN: Returns records when there is a match in either left or right table. Unmatched records will have NULL values.
SELECT * FROM table1
FULL JOIN table2 ON table1.column = table2.column;
4. What is the difference between WHERE & HAVING clauses?
WHERE: Filters records before any groupings are made.
SELECT * FROM table_name
WHERE condition;
HAVING: Filters records after groupings are made.
SELECT column, COUNT(*)
FROM table_name
GROUP BY column
HAVING COUNT(*) > value;
5. How do you calculate average, sum, minimum & maximum values in a column?
Average: SELECT AVG(column_name) FROM table_name;
Sum: SELECT SUM(column_name) FROM table_name;
Minimum: SELECT MIN(column_name) FROM table_name;
Maximum: SELECT MAX(column_name) FROM table_name;
Here you can find essential SQL Interview Resources๐
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Like this post if you need more ๐โค๏ธ
Hope it helps :)
Preparing for an SQL Interview at MAANG Companies? Here are some crucial SQL Questions you should be ready to tackle:
1. How do you retrieve all columns from a table?
SELECT * FROM table_name;
2. What SQL statement is used to filter records?
SELECT * FROM table_name
WHERE condition;
The WHERE clause is used to filter records based on a specified condition.
3. How can you join multiple tables? Describe different types of JOINs.
SELECT columns
FROM table1
JOIN table2 ON table1.column = table2.column
JOIN table3 ON table2.column = table3.column;
Types of JOINs:
1. INNER JOIN: Returns records with matching values in both tables
SELECT * FROM table1
INNER JOIN table2 ON table1.column = table2.column;
2. LEFT JOIN: Returns all records from the left table & matched records from the right table. Unmatched records will have NULL values.
SELECT * FROM table1
LEFT JOIN table2 ON table1.column = table2.column;
3. RIGHT JOIN: Returns all records from the right table & matched records from the left table. Unmatched records will have NULL values.
SELECT * FROM table1
RIGHT JOIN table2 ON table1.column = table2.column;
4. FULL JOIN: Returns records when there is a match in either left or right table. Unmatched records will have NULL values.
SELECT * FROM table1
FULL JOIN table2 ON table1.column = table2.column;
4. What is the difference between WHERE & HAVING clauses?
WHERE: Filters records before any groupings are made.
SELECT * FROM table_name
WHERE condition;
HAVING: Filters records after groupings are made.
SELECT column, COUNT(*)
FROM table_name
GROUP BY column
HAVING COUNT(*) > value;
5. How do you calculate average, sum, minimum & maximum values in a column?
Average: SELECT AVG(column_name) FROM table_name;
Sum: SELECT SUM(column_name) FROM table_name;
Minimum: SELECT MIN(column_name) FROM table_name;
Maximum: SELECT MAX(column_name) FROM table_name;
Here you can find essential SQL Interview Resources๐
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Like this post if you need more ๐โค๏ธ
Hope it helps :)
๐5โค1
Must Study: Key Questions for Data Analysts 4.0
Advanced SQL
1. How do you handle hierarchical data and perform recursive queries in SQL?
2. What are common techniques for SQL performance tuning beyond indexing?
3. How do you implement SQL transactions and ensure atomicity in complex queries?
Excel Advanced
1. How do you use Power Pivot to manage and analyze large datasets in Excel?
2. What are the best practices for creating and using Excel macros for automation?
3. How do you leverage Excelโs advanced charting tools for dynamic data visualization?
Power BI
1. How do you use Power Query to merge and transform data from multiple sources?
2. What are the key differences between calculated columns and measures in Power BI?
3. How do you design effective Power BI dashboards for executive reporting?
Python
1. How do you use Pythonโs pandas library for advanced data manipulation and analysis?
2. What are the best practices for deploying machine learning models using Python?
3. How do you perform time series analysis and forecasting with Python?
Data Visualization
1. How do you ensure your visualizations are accessible to people with visual impairments?
2. What are effective methods for visualizing multivariate data?
3. How do you use storytelling techniques to make your data visualizations more engaging?
Soft Skills
1. How do you handle conflicts and disagreements within a data team or with stakeholders?
2. What strategies do you use to effectively present complex data insights to a broad audience?
3. How do you stay updated with the latest trends and tools in data analytics?
I have curated Data Analytics Resources ๐๐
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Hope this helps you ๐
Advanced SQL
1. How do you handle hierarchical data and perform recursive queries in SQL?
2. What are common techniques for SQL performance tuning beyond indexing?
3. How do you implement SQL transactions and ensure atomicity in complex queries?
Excel Advanced
1. How do you use Power Pivot to manage and analyze large datasets in Excel?
2. What are the best practices for creating and using Excel macros for automation?
3. How do you leverage Excelโs advanced charting tools for dynamic data visualization?
Power BI
1. How do you use Power Query to merge and transform data from multiple sources?
2. What are the key differences between calculated columns and measures in Power BI?
3. How do you design effective Power BI dashboards for executive reporting?
Python
1. How do you use Pythonโs pandas library for advanced data manipulation and analysis?
2. What are the best practices for deploying machine learning models using Python?
3. How do you perform time series analysis and forecasting with Python?
Data Visualization
1. How do you ensure your visualizations are accessible to people with visual impairments?
2. What are effective methods for visualizing multivariate data?
3. How do you use storytelling techniques to make your data visualizations more engaging?
Soft Skills
1. How do you handle conflicts and disagreements within a data team or with stakeholders?
2. What strategies do you use to effectively present complex data insights to a broad audience?
3. How do you stay updated with the latest trends and tools in data analytics?
I have curated Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐
๐2โค1
Final Preparation Guide for Data Analytics Interviews: (IMP)
โกKey SQL Concepts:
- Master SELECT statements, focusing on WHERE, ORDER BY, GROUP BY, and HAVING clauses.
- Understand the basics of JOINS: INNER, LEFT, RIGHT, FULL.
- Get comfortable with aggregate functions like COUNT, SUM, AVG, MAX, and MIN.
- Study subqueries and Common Table Expressions.
- Explore advanced topics like CASE statements, complex JOIN strategies, and Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK).
โกPython for Data Analysis:
- Review the basics of Python syntax, control structures, and data structures (lists, dictionaries).
- Dive into data manipulation using Pandas and NumPy, covering DataFrames, Series, and group by operations.
- Learn basic plotting techniques with Matplotlib and Seaborn for data visualization.
โก Excel Skills:
- Practice cell operations and essential formulas like SUMIFS, COUNTIFS, and AVERAGEIFS.
- Familiarize yourself with PivotTables, PivotCharts, data validation, and What-if analysis.
- Explore advanced formulas and work with the Data Model & Power Pivot.
โก Power BI Proficiency:
- Focus on data modeling, including importing data and managing relationships.
- Learn data transformation techniques with Power Query and use DAX for calculated columns and measures.
- Create interactive reports and dashboards, and work on visualizations.
โก Basic Statistics:
- Understand fundamental concepts like Mean, Median, Mode, Standard Deviation, and Variance.
- Study probability distributions, Hypothesis Testing, and P-values.
- Learn about Confidence Intervals, Correlation, and Simple Linear Regression.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐
โกKey SQL Concepts:
- Master SELECT statements, focusing on WHERE, ORDER BY, GROUP BY, and HAVING clauses.
- Understand the basics of JOINS: INNER, LEFT, RIGHT, FULL.
- Get comfortable with aggregate functions like COUNT, SUM, AVG, MAX, and MIN.
- Study subqueries and Common Table Expressions.
- Explore advanced topics like CASE statements, complex JOIN strategies, and Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK).
โกPython for Data Analysis:
- Review the basics of Python syntax, control structures, and data structures (lists, dictionaries).
- Dive into data manipulation using Pandas and NumPy, covering DataFrames, Series, and group by operations.
- Learn basic plotting techniques with Matplotlib and Seaborn for data visualization.
โก Excel Skills:
- Practice cell operations and essential formulas like SUMIFS, COUNTIFS, and AVERAGEIFS.
- Familiarize yourself with PivotTables, PivotCharts, data validation, and What-if analysis.
- Explore advanced formulas and work with the Data Model & Power Pivot.
โก Power BI Proficiency:
- Focus on data modeling, including importing data and managing relationships.
- Learn data transformation techniques with Power Query and use DAX for calculated columns and measures.
- Create interactive reports and dashboards, and work on visualizations.
โก Basic Statistics:
- Understand fundamental concepts like Mean, Median, Mode, Standard Deviation, and Variance.
- Study probability distributions, Hypothesis Testing, and P-values.
- Learn about Confidence Intervals, Correlation, and Simple Linear Regression.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐
DATA ANALYST Interview Questions (0-3 yr) (SQL, Power BI)
๐ Power BI:
Q1: Explain step-by-step how you will create a sales dashboard from scratch.
Q2: Explain how you can optimize a slow Power BI report.
Q3: Explain Any 5 Chart Types and Their Uses in Representing Different Aspects of Data.
๐SQL:
Q1: Explain the difference between RANK(), DENSE_RANK(), and ROW_NUMBER() functions using example.
Q2 โ Q4 use Table: employee (EmpID, ManagerID, JoinDate, Dept, Salary)
Q2: Find the nth highest salary from the Employee table.
Q3: You have an employee table with employee ID and manager ID. Find all employees under a specific manager, including their subordinates at any level.
Q4: Write a query to find the cumulative salary of employees department-wise, who have joined the company in the last 30 days.
Q5: Find the top 2 customers with the highest order amount for each product category, handling ties appropriately. Table: Customer (CustomerID, ProductCategory, OrderAmount)
๐Behavioral:
Q1: Why do you want to become a data analyst and why did you apply to this company?
Q2: Describe a time when you had to manage a difficult task with tight deadlines. How did you handle it?
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐
๐ Power BI:
Q1: Explain step-by-step how you will create a sales dashboard from scratch.
Q2: Explain how you can optimize a slow Power BI report.
Q3: Explain Any 5 Chart Types and Their Uses in Representing Different Aspects of Data.
๐SQL:
Q1: Explain the difference between RANK(), DENSE_RANK(), and ROW_NUMBER() functions using example.
Q2 โ Q4 use Table: employee (EmpID, ManagerID, JoinDate, Dept, Salary)
Q2: Find the nth highest salary from the Employee table.
Q3: You have an employee table with employee ID and manager ID. Find all employees under a specific manager, including their subordinates at any level.
Q4: Write a query to find the cumulative salary of employees department-wise, who have joined the company in the last 30 days.
Q5: Find the top 2 customers with the highest order amount for each product category, handling ties appropriately. Table: Customer (CustomerID, ProductCategory, OrderAmount)
๐Behavioral:
Q1: Why do you want to become a data analyst and why did you apply to this company?
Q2: Describe a time when you had to manage a difficult task with tight deadlines. How did you handle it?
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐
๐4โค1
Excel interview questions for both data analysts and business analysts
1) What are the basic functions of Microsoft Excel?
2) Explain the difference between a workbook and a worksheet.
3) How would you freeze panes in Excel?
4) Can you name some common keyboard shortcuts in Excel?
5) What is the purpose of VLOOKUP and HLOOKUP?
7) How do you remove duplicate values in Excel?
8) Explain the steps to filter data in Excel.
9) What is the significance of the "IF" function in Excel, and can you provide an example of its use?
10) How would you create a pivot table in Excel?
11) Explain the use of the CONCATENATE function in Excel.
12) How do you create a chart in Excel?
13) Explain the difference between a line chart and a scatter plot.
14) What is conditional formatting, and how can it be applied in Excel?
15) How would you create a dynamic chart that updates with new data?
16) What is the INDEX-MATCH function, and how is it different from VLOOKUP?
17) Can you explain the concept of "PivotTables" and when you would use them?
18) How do you use the "COUNTIF" and "SUMIF" functions in Excel?
19) Explain the purpose of the "What-If Analysis" tools in Excel.
20) What are array formulas, and can you provide an example of their use?
Business Analysis Specific:
1) How would you analyze a set of sales data to identify trends and insights?
2) Explain how you might use Excel to perform financial modeling.
3) What Excel features would you use for forecasting and budgeting?
4) How do you handle large datasets in Excel, and what tools or techniques do you use for optimization?
5) What are some common techniques for cleaning and validating data in Excel?
6) How do you identify and handle errors in a dataset using Excel?
Scenario-based Questions:
1) Imagine you have a dataset with missing values. How would you approach this problem in Excel?
2) You are given a dataset with multiple sheets. How would you consolidate the data for analysis?
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐
1) What are the basic functions of Microsoft Excel?
2) Explain the difference between a workbook and a worksheet.
3) How would you freeze panes in Excel?
4) Can you name some common keyboard shortcuts in Excel?
5) What is the purpose of VLOOKUP and HLOOKUP?
7) How do you remove duplicate values in Excel?
8) Explain the steps to filter data in Excel.
9) What is the significance of the "IF" function in Excel, and can you provide an example of its use?
10) How would you create a pivot table in Excel?
11) Explain the use of the CONCATENATE function in Excel.
12) How do you create a chart in Excel?
13) Explain the difference between a line chart and a scatter plot.
14) What is conditional formatting, and how can it be applied in Excel?
15) How would you create a dynamic chart that updates with new data?
16) What is the INDEX-MATCH function, and how is it different from VLOOKUP?
17) Can you explain the concept of "PivotTables" and when you would use them?
18) How do you use the "COUNTIF" and "SUMIF" functions in Excel?
19) Explain the purpose of the "What-If Analysis" tools in Excel.
20) What are array formulas, and can you provide an example of their use?
Business Analysis Specific:
1) How would you analyze a set of sales data to identify trends and insights?
2) Explain how you might use Excel to perform financial modeling.
3) What Excel features would you use for forecasting and budgeting?
4) How do you handle large datasets in Excel, and what tools or techniques do you use for optimization?
5) What are some common techniques for cleaning and validating data in Excel?
6) How do you identify and handle errors in a dataset using Excel?
Scenario-based Questions:
1) Imagine you have a dataset with missing values. How would you approach this problem in Excel?
2) You are given a dataset with multiple sheets. How would you consolidate the data for analysis?
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐
๐4
๐จHere is a comprehensive list of #interview questions that are commonly asked in job interviews for Data Scientist, Data Analyst, and Data Engineer positions:
โก๏ธ Data Scientist Interview Questions
Technical Questions
1) What are your preferred programming languages for data science, and why?
2) Can you write a Python script to perform data cleaning on a given dataset?
3) Explain the Central Limit Theorem.
4) How do you handle missing data in a dataset?
5) Describe the difference between supervised and unsupervised learning.
6) How do you select the right algorithm for your model?
Questions Related To Problem-Solving and Projects
7) Walk me through a data science project you have worked on.
8) How did you handle data preprocessing in your project?
9) How do you evaluate the performance of a machine learning model?
10) What techniques do you use to prevent overfitting?
โก๏ธData Analyst Interview Questions
Technical Questions
1) Write a SQL query to find the second highest salary from the employee table.
2) How would you optimize a slow-running query?
3) How do you use pivot tables in Excel?
4) Explain the VLOOKUP function.
5) How do you handle outliers in your data?
6) Describe the steps you take to clean a dataset.
Analytical Questions
7) How do you interpret data to make business decisions?
8) Give an example of a time when your analysis directly influenced a business decision.
9) What are your preferred tools for data analysis and why?
10) How do you ensure the accuracy of your analysis?
โก๏ธData Engineer Interview Questions
Technical Questions
1) What is your experience with SQL and NoSQL databases?
2) How do you design a scalable database architecture?
3) Explain the ETL process you follow in your projects.
4) How do you handle data transformation and loading efficiently?
5) What is your experience with Hadoop/Spark?
6) How do you manage and process large datasets?
Questions Related To Problem-Solving and Optimization
7) Describe a data pipeline you have built.
8) What challenges did you face, and how did you overcome them?
9) How do you ensure your data processes run efficiently?
10) Describe a time when you had to optimize a slow data pipeline.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐
โก๏ธ Data Scientist Interview Questions
Technical Questions
1) What are your preferred programming languages for data science, and why?
2) Can you write a Python script to perform data cleaning on a given dataset?
3) Explain the Central Limit Theorem.
4) How do you handle missing data in a dataset?
5) Describe the difference between supervised and unsupervised learning.
6) How do you select the right algorithm for your model?
Questions Related To Problem-Solving and Projects
7) Walk me through a data science project you have worked on.
8) How did you handle data preprocessing in your project?
9) How do you evaluate the performance of a machine learning model?
10) What techniques do you use to prevent overfitting?
โก๏ธData Analyst Interview Questions
Technical Questions
1) Write a SQL query to find the second highest salary from the employee table.
2) How would you optimize a slow-running query?
3) How do you use pivot tables in Excel?
4) Explain the VLOOKUP function.
5) How do you handle outliers in your data?
6) Describe the steps you take to clean a dataset.
Analytical Questions
7) How do you interpret data to make business decisions?
8) Give an example of a time when your analysis directly influenced a business decision.
9) What are your preferred tools for data analysis and why?
10) How do you ensure the accuracy of your analysis?
โก๏ธData Engineer Interview Questions
Technical Questions
1) What is your experience with SQL and NoSQL databases?
2) How do you design a scalable database architecture?
3) Explain the ETL process you follow in your projects.
4) How do you handle data transformation and loading efficiently?
5) What is your experience with Hadoop/Spark?
6) How do you manage and process large datasets?
Questions Related To Problem-Solving and Optimization
7) Describe a data pipeline you have built.
8) What challenges did you face, and how did you overcome them?
9) How do you ensure your data processes run efficiently?
10) Describe a time when you had to optimize a slow data pipeline.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐
โค1
A - Always check your assumptions
B - Backup your data
C - Check your code
D - Do you know your data?
E - Evaluate your results
F - Find the anomalies
G - Get help when you need it
H - Have a backup plan
I - Investigate your outliers
J - Justify your methods
K - Keep your data clean
L - Let your data tell a story
M - Make your visualizations impactful
N - No one knows everything
O - Outline your analysis
P - Practice good documentation
Q - Quality control is key
R - Review your work
S - Stay organized
T - Test your assumptions
U - Use the right tools
V - Verify your results
W - Write clear and concise reports
X - Xamine for gaps in data
Y - Yield to the evidence
Z - Zero in on your findings
If you can master the ABCs of data analysis, you will be well on your way to being a successful Data Analyst.
B - Backup your data
C - Check your code
D - Do you know your data?
E - Evaluate your results
F - Find the anomalies
G - Get help when you need it
H - Have a backup plan
I - Investigate your outliers
J - Justify your methods
K - Keep your data clean
L - Let your data tell a story
M - Make your visualizations impactful
N - No one knows everything
O - Outline your analysis
P - Practice good documentation
Q - Quality control is key
R - Review your work
S - Stay organized
T - Test your assumptions
U - Use the right tools
V - Verify your results
W - Write clear and concise reports
X - Xamine for gaps in data
Y - Yield to the evidence
Z - Zero in on your findings
If you can master the ABCs of data analysis, you will be well on your way to being a successful Data Analyst.
๐4โค1
๐๐จ๐ฐ ๐ญ๐จ ๐๐ซ๐๐ฉ๐๐ซ๐ ๐ญ๐จ ๐๐๐๐จ๐ฆ๐ ๐ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ญ
๐. ๐๐ฑ๐๐๐ฅ- Learn formulas, Pivot tables, Lookup, VBA Macros.
๐. ๐๐๐- Joins, Windows, CTE is the most important
๐. ๐๐จ๐ฐ๐๐ซ ๐๐- Power Query Editor(PQE), DAX, MCode, RLS
๐. ๐๐ฒ๐ญ๐ก๐จ๐ง- Basics & Libraries(mainly pandas, numpy, matplotlib and seaborn libraries)
5. Practice SQL and Python questions on platforms like ๐๐๐๐ค๐๐ซ๐๐๐ง๐ค or ๐๐๐๐๐ก๐จ๐จ๐ฅ๐ฌ.
6. Know the basics of descriptive statistics(mean, median, mode, Probability, normal, binomial, Poisson distributions etc).
7. Learn to use ๐๐/๐๐จ๐ฉ๐ข๐ฅ๐จ๐ญ ๐ญ๐จ๐จ๐ฅ๐ฌ like GitHub Copilot or Power BI's AI features to automate tasks, generate insights, and improve your projects(Most demanding in Companies now)
8. Get hands-on experience with one cloud platform: ๐๐ณ๐ฎ๐ซ๐, ๐๐๐, ๐จ๐ซ ๐๐๐
9. Work on at least two end-to-end projects.
10. Prepare an ATS-friendly resume and start applying for jobs.
11. Prepare for interviews by going through common interview questions on Google and YouTube.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐
๐. ๐๐ฑ๐๐๐ฅ- Learn formulas, Pivot tables, Lookup, VBA Macros.
๐. ๐๐๐- Joins, Windows, CTE is the most important
๐. ๐๐จ๐ฐ๐๐ซ ๐๐- Power Query Editor(PQE), DAX, MCode, RLS
๐. ๐๐ฒ๐ญ๐ก๐จ๐ง- Basics & Libraries(mainly pandas, numpy, matplotlib and seaborn libraries)
5. Practice SQL and Python questions on platforms like ๐๐๐๐ค๐๐ซ๐๐๐ง๐ค or ๐๐๐๐๐ก๐จ๐จ๐ฅ๐ฌ.
6. Know the basics of descriptive statistics(mean, median, mode, Probability, normal, binomial, Poisson distributions etc).
7. Learn to use ๐๐/๐๐จ๐ฉ๐ข๐ฅ๐จ๐ญ ๐ญ๐จ๐จ๐ฅ๐ฌ like GitHub Copilot or Power BI's AI features to automate tasks, generate insights, and improve your projects(Most demanding in Companies now)
8. Get hands-on experience with one cloud platform: ๐๐ณ๐ฎ๐ซ๐, ๐๐๐, ๐จ๐ซ ๐๐๐
9. Work on at least two end-to-end projects.
10. Prepare an ATS-friendly resume and start applying for jobs.
11. Prepare for interviews by going through common interview questions on Google and YouTube.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐
โค2๐2
Data Analyst Interview!
๐๐จ๐ฎ๐ง๐ 1: Technical Round - 15 mins
1. Tell me about yourself
2. Tell me about your experience
3. What is VLookup, when we are using VLookup what do we have to check before applying?
4. Are you familiar with dashboards and generating reports
5. How do you generate reports generally
6. How to delete duplicates in Power BI
7. In Power BI do you know how to draw all charts
8. Do you have any questions?
๐๐จ๐ฎ๐ง๐ 2: Manager Round - 30 mins
1. Tell me about yourself
2. Tell me about our Organization
3. Tell me about your work experience
4. To whom do you report usually
5. Why do you choose this role
6. Why this organization only
7. Why do you think you will be suitable for this role
8. Do you have any questions
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐
๐๐จ๐ฎ๐ง๐ 1: Technical Round - 15 mins
1. Tell me about yourself
2. Tell me about your experience
3. What is VLookup, when we are using VLookup what do we have to check before applying?
4. Are you familiar with dashboards and generating reports
5. How do you generate reports generally
6. How to delete duplicates in Power BI
7. In Power BI do you know how to draw all charts
8. Do you have any questions?
๐๐จ๐ฎ๐ง๐ 2: Manager Round - 30 mins
1. Tell me about yourself
2. Tell me about our Organization
3. Tell me about your work experience
4. To whom do you report usually
5. Why do you choose this role
6. Why this organization only
7. Why do you think you will be suitable for this role
8. Do you have any questions
I have curated best 80+ top-notch Data Analytics Resources ๐๐
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Hope this helps you ๐
๐5
Statistical interview questions for entry-level data analyst roles in an MNC.
1. Explain the difference between mean, median, and mode. When would you use each?
2. How do you calculate the variance and standard deviation of a dataset?
3. What is skewness and kurtosis? How do they help in understanding data distribution?
4. What is the central limit theorem, and why is it important in statistics?
5. Describe different types of probability distributions (e.g., normal, binomial, Poisson).
6. Explain the difference between a population and a sample. Why is sampling important?
7. What are null and alternative hypotheses? How do you formulate them?
8. Describe the steps in conducting a hypothesis test.
9. What is a p-value? How do you interpret it in the context of a hypothesis test?
10. When would you use a t-test versus a z-test?
11. Explain how you would conduct an independent two-sample t-test. What assumptions must be met?
12. Describe a scenario where you would use a paired sample t-test.
13. What is ANOVA, and how does it differ from a t-test?
14. Explain how you would interpret the results of a one-way ANOVA.
15. Describe a situation where you might use a two-way ANOVA.
16. What is a chi-square test for independence? When would you use it?
17. How do you interpret the results of a chi-square goodness-of-fit test?
18. Explain the assumptions and limitations of chi-square tests.
19. What is the difference between simple linear regression and multiple regression?
20. How do you assess the goodness-of-fit of a regression model?
21. Explain multicollinearity and how you would detect and handle it in a regression model.
22. What is the difference between correlation and causation?
23. How do you interpret the Pearson correlation coefficient?
24. When would you use Spearman rank correlation instead of Pearson correlation?
25. What are some common methods for forecasting time series data?
26. Explain the components of a time series (trend, seasonality, residuals).
27. How would you handle missing data in a time series dataset?
28. Describe your approach to exploratory data analysis (EDA).
29. How do you handle outliers in a dataset?
30. Explain the steps you would take to validate the results of your analysis.
31. Give an example of how you have used statistical analysis to solve a real-world problem
Hope this helps you ๐
1. Explain the difference between mean, median, and mode. When would you use each?
2. How do you calculate the variance and standard deviation of a dataset?
3. What is skewness and kurtosis? How do they help in understanding data distribution?
4. What is the central limit theorem, and why is it important in statistics?
5. Describe different types of probability distributions (e.g., normal, binomial, Poisson).
6. Explain the difference between a population and a sample. Why is sampling important?
7. What are null and alternative hypotheses? How do you formulate them?
8. Describe the steps in conducting a hypothesis test.
9. What is a p-value? How do you interpret it in the context of a hypothesis test?
10. When would you use a t-test versus a z-test?
11. Explain how you would conduct an independent two-sample t-test. What assumptions must be met?
12. Describe a scenario where you would use a paired sample t-test.
13. What is ANOVA, and how does it differ from a t-test?
14. Explain how you would interpret the results of a one-way ANOVA.
15. Describe a situation where you might use a two-way ANOVA.
16. What is a chi-square test for independence? When would you use it?
17. How do you interpret the results of a chi-square goodness-of-fit test?
18. Explain the assumptions and limitations of chi-square tests.
19. What is the difference between simple linear regression and multiple regression?
20. How do you assess the goodness-of-fit of a regression model?
21. Explain multicollinearity and how you would detect and handle it in a regression model.
22. What is the difference between correlation and causation?
23. How do you interpret the Pearson correlation coefficient?
24. When would you use Spearman rank correlation instead of Pearson correlation?
25. What are some common methods for forecasting time series data?
26. Explain the components of a time series (trend, seasonality, residuals).
27. How would you handle missing data in a time series dataset?
28. Describe your approach to exploratory data analysis (EDA).
29. How do you handle outliers in a dataset?
30. Explain the steps you would take to validate the results of your analysis.
31. Give an example of how you have used statistical analysis to solve a real-world problem
Hope this helps you ๐
๐6โค2
Amazon Data Analyst Interview Questions for 1-3 years of experience role :-
A. SQL:
1. You have two tables: Employee and Department.
- Employee Table Columns: Employee_id, Employee_Name, Department_id, Salary
- Department Table Columns: Department_id, Department_Name, Location
Write an SQL query to find the name of the employee with the highest salary in each location.
2. You have two tables: Orders and Customers.
- Orders Table Columns: Order_id, Customer_id, Order_Date, Amount
- Customers Table Columns: Customer_id, Customer_Name, Join_Date
Write an SQL query to calculate the total order amount for each customer who joined in the current year. The output should contain Customer_Name and the total amount.
B. Python:
1. Basic oral questions on NumPy (e.g., array creation, slicing, broadcasting) and Matplotlib (e.g., plot types, customization).
2. Basic oral questions on pandas (like: groupby, loc/iloc, merge & join, etc.)
2. Write the code in NumPy and Pandas to replicate the functionality of your answer to the second SQL question.
C. Leadership or Situational Questions:
(Based on the leadership principle of Bias for Action)
- Describe a situation where you had to make a quick decision with limited information. How did you proceed, and what was the outcome?
(Based on the leadership principle of Dive Deep)
- Can you share an example of a project where you had to delve deeply into the data to uncover insights or solve a problem? What steps did you take, and what were the results?
(Based on the leadership principle of Customer Obsession)
- Tell us about a time when you went above and beyond to meet a customer's needs or expectations. How did you identify their requirements, and what actions did you take to deliver exceptional service?
D. Excel:
Questions on advanced functions like VLOOKUP, XLookup, SUMPRODUCT, INDIRECT, TEXT functions, SUMIFS, COUNTIFS, LOOKUPS, INDEX & MATCH, AVERAGEIFS. Plus, some basic questions on pivot tables, conditional formatting, data validation, and charts.
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A. SQL:
1. You have two tables: Employee and Department.
- Employee Table Columns: Employee_id, Employee_Name, Department_id, Salary
- Department Table Columns: Department_id, Department_Name, Location
Write an SQL query to find the name of the employee with the highest salary in each location.
2. You have two tables: Orders and Customers.
- Orders Table Columns: Order_id, Customer_id, Order_Date, Amount
- Customers Table Columns: Customer_id, Customer_Name, Join_Date
Write an SQL query to calculate the total order amount for each customer who joined in the current year. The output should contain Customer_Name and the total amount.
B. Python:
1. Basic oral questions on NumPy (e.g., array creation, slicing, broadcasting) and Matplotlib (e.g., plot types, customization).
2. Basic oral questions on pandas (like: groupby, loc/iloc, merge & join, etc.)
2. Write the code in NumPy and Pandas to replicate the functionality of your answer to the second SQL question.
C. Leadership or Situational Questions:
(Based on the leadership principle of Bias for Action)
- Describe a situation where you had to make a quick decision with limited information. How did you proceed, and what was the outcome?
(Based on the leadership principle of Dive Deep)
- Can you share an example of a project where you had to delve deeply into the data to uncover insights or solve a problem? What steps did you take, and what were the results?
(Based on the leadership principle of Customer Obsession)
- Tell us about a time when you went above and beyond to meet a customer's needs or expectations. How did you identify their requirements, and what actions did you take to deliver exceptional service?
D. Excel:
Questions on advanced functions like VLOOKUP, XLookup, SUMPRODUCT, INDIRECT, TEXT functions, SUMIFS, COUNTIFS, LOOKUPS, INDEX & MATCH, AVERAGEIFS. Plus, some basic questions on pivot tables, conditional formatting, data validation, and charts.
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๐2โค1
Some Imp Scenario Q & A for product based Company :
You are a data analyst at a logistics company. The company wants to analyze delivery performance and customer satisfaction. Your tasks are:
1. Identify late deliveries and their impact on customer satisfaction.
2. Calculate the average delivery time for each region.
3. Create a Power BI report to visualize delivery performance and identify areas for improvement
Answer:
SQL Queries to Retrieve Data
1. Identify Late Deliveries and Their Impact on Customer Satisfaction:
SELECT
d.DeliveryID,
d.CustomerID,
d.DeliveryDate,
d.ExpectedDeliveryDate,
d.DeliveryTime,
c.SatisfactionScore
FROM Deliveries d
JOIN Customers c ON d.CustomerID = c.CustomerID
WHERE d.DeliveryDate > d.ExpectedDeliveryDate;
2. Calculate the Average Delivery Time for Each Region:
SELECT
Region,
AVG(DATEDIFF(day, OrderDate, DeliveryDate)) AS AvgDeliveryTime
FROM Deliveries
GROUP BY Region;
3. Customer Satisfaction by Delivery Performance:
SELECT
DeliveryPerformance,
AVG(SatisfactionScore) AS AvgSatisfactionScore
FROM (
SELECT
d.CustomerID,
c.SatisfactionScore,
CASE
WHEN d.DeliveryDate <= d.ExpectedDeliveryDate THEN 'On Time'
ELSE 'Late'
END AS DeliveryPerformance
FROM Deliveries d
JOIN Customers c ON d.CustomerID = c.CustomerID
) AS DeliveryData
GROUP BY DeliveryPerformance;
Import Data into Power BI
1. Load Data:
Open Power BI Desktop.
Use the "Get Data" feature to connect to your SQL database.
Import the result sets from the SQL queries into Power BI.
2. Create Relationships (if necessary):
Ensure that the data tables are properly related, such as linking the Deliveries table to the Customers table.
Create Visualizations
1. Late Deliveries and Their Impact on Customer Satisfaction:
Create a table visual.
Drag DeliveryID, CustomerID, DeliveryDate, ExpectedDeliveryDate, DeliveryTime, and SatisfactionScore to the Values.
2. Average Delivery Time for Each Region:
Create a bar chart.
Drag Region to the Axis.
Drag AvgDeliveryTime to the Values.
3. Customer Satisfaction by Delivery Performance:
Create a bar chart.
Drag DeliveryPerformance to the Axis.
Drag AvgSatisfactionScore to the Values.
4. Overall Delivery Analysis:
Create a pie chart.
Drag Region to the Legend.
Drag AvgDeliveryTime to the Values.
Optimize Performance
1. Data Model Optimization:
Filter data to include only necessary columns and rows.
Use summarized tables to pre-aggregate data.
2. DAX Optimization:
Create measures for dynamic calculations.
Simplify DAX formulas to improve performance.
3. Visualization Optimization:
Limit the number of visuals per page.
Avoid excessive use of slicers or custom visuals that can impact performance.
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You are a data analyst at a logistics company. The company wants to analyze delivery performance and customer satisfaction. Your tasks are:
1. Identify late deliveries and their impact on customer satisfaction.
2. Calculate the average delivery time for each region.
3. Create a Power BI report to visualize delivery performance and identify areas for improvement
Answer:
SQL Queries to Retrieve Data
1. Identify Late Deliveries and Their Impact on Customer Satisfaction:
SELECT
d.DeliveryID,
d.CustomerID,
d.DeliveryDate,
d.ExpectedDeliveryDate,
d.DeliveryTime,
c.SatisfactionScore
FROM Deliveries d
JOIN Customers c ON d.CustomerID = c.CustomerID
WHERE d.DeliveryDate > d.ExpectedDeliveryDate;
2. Calculate the Average Delivery Time for Each Region:
SELECT
Region,
AVG(DATEDIFF(day, OrderDate, DeliveryDate)) AS AvgDeliveryTime
FROM Deliveries
GROUP BY Region;
3. Customer Satisfaction by Delivery Performance:
SELECT
DeliveryPerformance,
AVG(SatisfactionScore) AS AvgSatisfactionScore
FROM (
SELECT
d.CustomerID,
c.SatisfactionScore,
CASE
WHEN d.DeliveryDate <= d.ExpectedDeliveryDate THEN 'On Time'
ELSE 'Late'
END AS DeliveryPerformance
FROM Deliveries d
JOIN Customers c ON d.CustomerID = c.CustomerID
) AS DeliveryData
GROUP BY DeliveryPerformance;
Import Data into Power BI
1. Load Data:
Open Power BI Desktop.
Use the "Get Data" feature to connect to your SQL database.
Import the result sets from the SQL queries into Power BI.
2. Create Relationships (if necessary):
Ensure that the data tables are properly related, such as linking the Deliveries table to the Customers table.
Create Visualizations
1. Late Deliveries and Their Impact on Customer Satisfaction:
Create a table visual.
Drag DeliveryID, CustomerID, DeliveryDate, ExpectedDeliveryDate, DeliveryTime, and SatisfactionScore to the Values.
2. Average Delivery Time for Each Region:
Create a bar chart.
Drag Region to the Axis.
Drag AvgDeliveryTime to the Values.
3. Customer Satisfaction by Delivery Performance:
Create a bar chart.
Drag DeliveryPerformance to the Axis.
Drag AvgSatisfactionScore to the Values.
4. Overall Delivery Analysis:
Create a pie chart.
Drag Region to the Legend.
Drag AvgDeliveryTime to the Values.
Optimize Performance
1. Data Model Optimization:
Filter data to include only necessary columns and rows.
Use summarized tables to pre-aggregate data.
2. DAX Optimization:
Create measures for dynamic calculations.
Simplify DAX formulas to improve performance.
3. Visualization Optimization:
Limit the number of visuals per page.
Avoid excessive use of slicers or custom visuals that can impact performance.
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๐ฅฐ2
๐ช๐ฎ๐ป๐ ๐๐ผ ๐ธ๐ป๐ผ๐ ๐๐ต๐ฎ๐ ๐ต๐ฎ๐ฝ๐ฝ๐ฒ๐ป๐ ๐ถ๐ป ๐ฎ ๐ฟ๐ฒ๐ฎ๐น ๐ฑ๐ฎ๐๐ฎ ๐ฎ๐ป๐ฎ๐น๐๐๐ ๐ถ๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐?
๐๐ฎ๐๐ถ๐ฐ ๐๐ป๐๐ฟ๐ผ๐ฑ๐๐ฐ๐๐ถ๐ผ๐ป
-Brief introduction about yourself.
-Explanation of how you developed an interest in learning Power BI despite having a chemical background.
๐ง๐ผ๐ผ๐น๐ ๐ฃ๐ฟ๐ผ๐ณ๐ถ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐
-Discussion about the tools you are proficient in.
-Detailed explanation of a project that demonstrated your proficiency in these tools.
๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐๐ ๐ฝ๐น๐ฎ๐ป๐ฎ๐๐ถ๐ผ๐ป
Explain about any Data Analytics Project you did, below are some follow-up questions for sales related data analysis project
Follow-up Question:
Was there any improvement in sales after building the report?
Provide a clear before and after scenario in sales post-report creation.
What areas did you identify where the company was losing sales, and what were your recommendations?
- How do you check the quality of data when it's given to you?
Explain your methods for ensuring data quality.
- How do you handle null values? Describe your approach to managing null values in datasets.
๐ฆ๐ค๐ ๐พ๐๐ฒ๐๐๐ถ๐ผ๐ป๐
-Explain the order in which SQL clauses are executed.
-Write a query to find the percentage of the 18-year-old population.
Details: You are given two tables:
Table 1: Contains states and their respective populations.
Table 2: Contains three columns (state, gender, and population of 18-year-olds).
-Explain window functions and how to rank values in SQL.
- Difference between JOIN and UNION.
-How to return unique values in SQL.
๐๐ฒ๐ต๐ฎ๐๐ถ๐ผ๐ฟ๐ฎ๐น ๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป๐
-Solve a puzzle involving 3 gallons of water in one jar and 2 gallons in another to get exactly 4 gallons.
Step-by-step solution for the water puzzle.
- What skills have you learned on your own? Discuss the skills you self-taught and their impact on your career.
-Describe cases when you showcased team spirit.
-โญ ๐ฆ๐ผ๐ฐ๐ถ๐ฎ๐น ๐ ๐ฒ๐ฑ๐ถ๐ฎ ๐๐ฝ๐ฝ ๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป
Scenario: Choose any social media app (I choose Discord).
Question: What function/feature would you add to the Discord app, and how would you track its success?
- Rate yourself on Excel, SQL, and Python out of 10.
- What are your strengths in data analytics?
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๐๐ฎ๐๐ถ๐ฐ ๐๐ป๐๐ฟ๐ผ๐ฑ๐๐ฐ๐๐ถ๐ผ๐ป
-Brief introduction about yourself.
-Explanation of how you developed an interest in learning Power BI despite having a chemical background.
๐ง๐ผ๐ผ๐น๐ ๐ฃ๐ฟ๐ผ๐ณ๐ถ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐
-Discussion about the tools you are proficient in.
-Detailed explanation of a project that demonstrated your proficiency in these tools.
๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐๐ ๐ฝ๐น๐ฎ๐ป๐ฎ๐๐ถ๐ผ๐ป
Explain about any Data Analytics Project you did, below are some follow-up questions for sales related data analysis project
Follow-up Question:
Was there any improvement in sales after building the report?
Provide a clear before and after scenario in sales post-report creation.
What areas did you identify where the company was losing sales, and what were your recommendations?
- How do you check the quality of data when it's given to you?
Explain your methods for ensuring data quality.
- How do you handle null values? Describe your approach to managing null values in datasets.
๐ฆ๐ค๐ ๐พ๐๐ฒ๐๐๐ถ๐ผ๐ป๐
-Explain the order in which SQL clauses are executed.
-Write a query to find the percentage of the 18-year-old population.
Details: You are given two tables:
Table 1: Contains states and their respective populations.
Table 2: Contains three columns (state, gender, and population of 18-year-olds).
-Explain window functions and how to rank values in SQL.
- Difference between JOIN and UNION.
-How to return unique values in SQL.
๐๐ฒ๐ต๐ฎ๐๐ถ๐ผ๐ฟ๐ฎ๐น ๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป๐
-Solve a puzzle involving 3 gallons of water in one jar and 2 gallons in another to get exactly 4 gallons.
Step-by-step solution for the water puzzle.
- What skills have you learned on your own? Discuss the skills you self-taught and their impact on your career.
-Describe cases when you showcased team spirit.
-โญ ๐ฆ๐ผ๐ฐ๐ถ๐ฎ๐น ๐ ๐ฒ๐ฑ๐ถ๐ฎ ๐๐ฝ๐ฝ ๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป
Scenario: Choose any social media app (I choose Discord).
Question: What function/feature would you add to the Discord app, and how would you track its success?
- Rate yourself on Excel, SQL, and Python out of 10.
- What are your strengths in data analytics?
I have curated best 80+ top-notch Data Analytics Resources ๐๐
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โค4๐2
How to become a "Data Analyst" in 36 weeks?
Here's the plan -
โ Microsoft Excel (First 18 Weeks) (Mon-Fri)
โ Power BI (Next 18 Weeks) (Mon-Fri)
โ SQL (All 36 weeks) (Sat & Sun)
Important Point :-
โ Spend 2.5 hour's ( Mon - Fri)
โ Spend 4 hour's ( Sat & Sun)
โ Follow the Limited resources to avoid Confusion
I have curated best Data Analytics Resources ๐๐
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Here's the plan -
โ Microsoft Excel (First 18 Weeks) (Mon-Fri)
โ Power BI (Next 18 Weeks) (Mon-Fri)
โ SQL (All 36 weeks) (Sat & Sun)
Important Point :-
โ Spend 2.5 hour's ( Mon - Fri)
โ Spend 4 hour's ( Sat & Sun)
โ Follow the Limited resources to avoid Confusion
I have curated best Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Like if it helps :)
๐2
Some practical interview questions for data analyst role in Power BI:
โข Data Import Scenario: Describe how you would import data from various sources (Excel,SQL Server, CSV) into Power BI.
โข Data Cleaning Exercise: In Power BI, how would you handle a dataset with missing values and inconsistent formats to prepare it for analysis?
โข Handling Large Datasets: If you're working with a very large dataset in Power BI that is causing performance issues, what strategies would you use to optimize the data processing?
โข Calculated Columns and Measures: Explain how you would use calculated columns and measures in Power BI to analyze year-over-year growth.
โข Data Modeling Case: You have sales data in one table and customer data in another. How would you create a data model in Power BI to analyze customer purchase behavior?
โข Visualizations Task: Describe your approach to visualizing sales data in Power BI to highlight trends over time across different product categories.
โข Dashboard Optimization: A Power BI dashboard is loading slowly. What steps would you take to diagnose and improve its performance?
โข Data Refresh Scheduling: How would you set up and manage automatic data refreshes for a weekly sales report in Power BI?
โข Row-Level Security: How would you implement user-level security in Power BI for a report that needs different access levels for various users?
โข Troubleshooting a DAX Calculation: If a DAX formula in Power BI is not returning the expected results, how would you go about troubleshooting it?
โข Integration with Other Tools: Describe a scenario where you integrated Power BI with another tool or service (like Excel, Azure, or a web API).
โข Interactive Reports Creation: How would you design a Power BI report that allows user interaction, such as using slicers or drill-down features?
โข Adapting to Data Source Changes: If there are structural changes in a primary data source (like addition or removal of columns), how would you update your Power BI reports and dashboards?
โข Sharing Reports: Explain how you would share a report with your team and set up access controls using Power BI Service.
โข SQL Queries in Power BI: How do you use SQL queries in Power BI for advanced data transformation or analysis?
โข Error Handling in Data Sources: How do you manage and resolve errors in data sources or calculations in Power BI?
โข Custom Visuals Usage: Have you used custom visuals in Power BI? Describe the scenario and the benefits.
โข Power BI Templates: Provide an example of a situation where you created or used a Power BI template. What advantages did this offer?
โข Performance Tuning: What steps do you take to ensure your Power BI reports are performing optimally when dealing with large datasets or complex calculations?
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โข Data Import Scenario: Describe how you would import data from various sources (Excel,SQL Server, CSV) into Power BI.
โข Data Cleaning Exercise: In Power BI, how would you handle a dataset with missing values and inconsistent formats to prepare it for analysis?
โข Handling Large Datasets: If you're working with a very large dataset in Power BI that is causing performance issues, what strategies would you use to optimize the data processing?
โข Calculated Columns and Measures: Explain how you would use calculated columns and measures in Power BI to analyze year-over-year growth.
โข Data Modeling Case: You have sales data in one table and customer data in another. How would you create a data model in Power BI to analyze customer purchase behavior?
โข Visualizations Task: Describe your approach to visualizing sales data in Power BI to highlight trends over time across different product categories.
โข Dashboard Optimization: A Power BI dashboard is loading slowly. What steps would you take to diagnose and improve its performance?
โข Data Refresh Scheduling: How would you set up and manage automatic data refreshes for a weekly sales report in Power BI?
โข Row-Level Security: How would you implement user-level security in Power BI for a report that needs different access levels for various users?
โข Troubleshooting a DAX Calculation: If a DAX formula in Power BI is not returning the expected results, how would you go about troubleshooting it?
โข Integration with Other Tools: Describe a scenario where you integrated Power BI with another tool or service (like Excel, Azure, or a web API).
โข Interactive Reports Creation: How would you design a Power BI report that allows user interaction, such as using slicers or drill-down features?
โข Adapting to Data Source Changes: If there are structural changes in a primary data source (like addition or removal of columns), how would you update your Power BI reports and dashboards?
โข Sharing Reports: Explain how you would share a report with your team and set up access controls using Power BI Service.
โข SQL Queries in Power BI: How do you use SQL queries in Power BI for advanced data transformation or analysis?
โข Error Handling in Data Sources: How do you manage and resolve errors in data sources or calculations in Power BI?
โข Custom Visuals Usage: Have you used custom visuals in Power BI? Describe the scenario and the benefits.
โข Power BI Templates: Provide an example of a situation where you created or used a Power BI template. What advantages did this offer?
โข Performance Tuning: What steps do you take to ensure your Power BI reports are performing optimally when dealing with large datasets or complex calculations?
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๐1
Advanced Questions Asked by Big 4
๐ Excel Questions
1. How do you use Excel to forecast future trends based on historical data? Describe a scenario where you built a forecasting model.
2. Can you explain how you would automate repetitive tasks in Excel using VBA (Visual Basic for Applications)? Provide an example of a complex macro you created.
3. Describe a time when you had to merge and analyze data from multiple Excel workbooks. How did you ensure data integrity and accuracy?
๐ SQL Questions
1. How would you design a database schema for a new e-commerce platform to efficiently handle large volumes of transactions and user data?
2. Describe a complex SQL query you wrote to solve a business problem. What was the problem, and how did your query help resolve it?
3. How do you ensure data integrity and consistency in a multi-user database environment? Explain the techniques and tools you use.
๐ Python Questions
1. How would you use Python to automate data extraction from various APIs and combine the data for analysis? Provide an example.
2. Describe a machine learning project you worked on using Python. What was the objective, and how did you approach the data preprocessing, model selection, and evaluation?
3. Explain how you would use Python to detect and handle anomalies in a dataset. What techniques and libraries would you employ?
๐ Power BI Questions
1. How do you create interactive dashboards in Power BI that can dynamically update based on user inputs? Provide an example of a dashboard you built.
2. Describe a scenario where you used Power BI to integrate data from non-traditional sources (e.g., web scraping, APIs). How did you handle the data transformation and visualization?
3. How do you ensure the performance and scalability of Power BI reports when dealing with large datasets? Describe the techniques and best practices you follow.
๐ก Tips for Success:
Understand the business context: Tailor your answers to show how your technical skills solve real business problems.
Provide specific examples: Highlight your past experiences with concrete examples.
Stay updated: Continuously learn and adapt to new tools and methodologies.
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๐ Excel Questions
1. How do you use Excel to forecast future trends based on historical data? Describe a scenario where you built a forecasting model.
2. Can you explain how you would automate repetitive tasks in Excel using VBA (Visual Basic for Applications)? Provide an example of a complex macro you created.
3. Describe a time when you had to merge and analyze data from multiple Excel workbooks. How did you ensure data integrity and accuracy?
๐ SQL Questions
1. How would you design a database schema for a new e-commerce platform to efficiently handle large volumes of transactions and user data?
2. Describe a complex SQL query you wrote to solve a business problem. What was the problem, and how did your query help resolve it?
3. How do you ensure data integrity and consistency in a multi-user database environment? Explain the techniques and tools you use.
๐ Python Questions
1. How would you use Python to automate data extraction from various APIs and combine the data for analysis? Provide an example.
2. Describe a machine learning project you worked on using Python. What was the objective, and how did you approach the data preprocessing, model selection, and evaluation?
3. Explain how you would use Python to detect and handle anomalies in a dataset. What techniques and libraries would you employ?
๐ Power BI Questions
1. How do you create interactive dashboards in Power BI that can dynamically update based on user inputs? Provide an example of a dashboard you built.
2. Describe a scenario where you used Power BI to integrate data from non-traditional sources (e.g., web scraping, APIs). How did you handle the data transformation and visualization?
3. How do you ensure the performance and scalability of Power BI reports when dealing with large datasets? Describe the techniques and best practices you follow.
๐ก Tips for Success:
Understand the business context: Tailor your answers to show how your technical skills solve real business problems.
Provide specific examples: Highlight your past experiences with concrete examples.
Stay updated: Continuously learn and adapt to new tools and methodologies.
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You don't need to know everything about every data tool. Focus on what will help land you your job.
For Excel:
- IFS (all variations)
- XLOOKUP
- IMPORTRANGE (in GSheets)
- Pivot Tables
- Dynamic functions like TODAY()
For SQL:
- Sum
- Group By
- Window Functions
- CTEs
- Joins
For Tableau:
- Calculated Columns
- Sets
- Groups
- Formatting
For Power BI:
- Power Query for data transformation
- DAX (Data Analysis Expressions) for creating custom calculations
- Relationships between tables
- Creating interactive and dynamic dashboards
- Utilizing slicers and filters effectively
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For Excel:
- IFS (all variations)
- XLOOKUP
- IMPORTRANGE (in GSheets)
- Pivot Tables
- Dynamic functions like TODAY()
For SQL:
- Sum
- Group By
- Window Functions
- CTEs
- Joins
For Tableau:
- Calculated Columns
- Sets
- Groups
- Formatting
For Power BI:
- Power Query for data transformation
- DAX (Data Analysis Expressions) for creating custom calculations
- Relationships between tables
- Creating interactive and dynamic dashboards
- Utilizing slicers and filters effectively
I have created 100-Day Roadmap & Resources for Data Analyst ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope it helps :)
โค1
Essentials for Acing any Data Analytics Interviews-
SQL:
1. Beginner
- Fundamentals: SELECT, WHERE, ORDER BY, GROUP BY, HAVING
- Essential JOINS: INNER, LEFT, RIGHT, FULL
- Basics of database and table creation
2. Intermediate
- Aggregate functions: COUNT, SUM, AVG, MAX, MIN
- Subqueries and nested queries
- Common Table Expressions with the WITH clause
- Conditional logic in queries using CASE statements
3. Advanced
- Complex JOIN techniques: self-join, non-equi join
- Window functions: OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag
- Query optimization through indexing
- Manipulating data: INSERT, UPDATE, DELETE
Python:
1. Basics
- Understanding syntax, variables, and data types: integers, floats, strings, booleans
- Control structures: if-else, loops (for, while)
- Core data structures: lists, dictionaries, sets, tuples
- Functions and error handling: lambda functions, try-except
- Using modules and packages
2. Pandas & Numpy
- DataFrames and Series: creation and manipulation
- Techniques: indexing, selecting, filtering
- Handling missing data with fillna and dropna
- Data aggregation: groupby, data summarizing
- Data merging techniques: merge, join, concatenate
3. Visualization
- Plotting basics with Matplotlib: line plots, bar plots, histograms
- Advanced visualization with Seaborn: scatter plots, box plots, pair plots
- Plot customization: sizes, labels, legends, colors
- Introduction to interactive visualizations with Plotly
Excel:
1. Basics
- Cell operations and basic formulas: SUMIFS, COUNTIFS, AVERAGEIFS
- Charts and introductory data visualization
- Data sorting and filtering, Conditional formatting
2. Intermediate
- Advanced formulas: V/XLOOKUP, INDEX-MATCH, complex IF scenarios
- Summarizing data with PivotTables and PivotCharts
- Tools for data validation and what-if analysis: Data Tables, Goal Seek
3. Advanced
- Utilizing array formulas and sophisticated functions
- Building a Data Model & using Power Pivot
- Advanced filtering, Slicers and Timelines in Pivot Tables
- Crafting dynamic charts and interactive dashboards
Power BI:
1. Data Modeling
- Importing data from diverse sources
- Creating and managing dataset relationships
- Data modeling essentials: star schema, snowflake schema
2. Data Transformation
- Data cleaning and transformation with Power Query
- Advanced data shaping techniques
- Implementing calculated columns and measures with DAX
3. Data Visualization and Reporting
- Developing interactive reports and dashboards
- Visualization types: bar, line, pie charts, maps
- Report publishing and sharing, scheduling data refreshes
Statistics:
Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution
I have curated best 80+ top-notch Data Analytics Resources ๐๐
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Like if it helps :)
SQL:
1. Beginner
- Fundamentals: SELECT, WHERE, ORDER BY, GROUP BY, HAVING
- Essential JOINS: INNER, LEFT, RIGHT, FULL
- Basics of database and table creation
2. Intermediate
- Aggregate functions: COUNT, SUM, AVG, MAX, MIN
- Subqueries and nested queries
- Common Table Expressions with the WITH clause
- Conditional logic in queries using CASE statements
3. Advanced
- Complex JOIN techniques: self-join, non-equi join
- Window functions: OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag
- Query optimization through indexing
- Manipulating data: INSERT, UPDATE, DELETE
Python:
1. Basics
- Understanding syntax, variables, and data types: integers, floats, strings, booleans
- Control structures: if-else, loops (for, while)
- Core data structures: lists, dictionaries, sets, tuples
- Functions and error handling: lambda functions, try-except
- Using modules and packages
2. Pandas & Numpy
- DataFrames and Series: creation and manipulation
- Techniques: indexing, selecting, filtering
- Handling missing data with fillna and dropna
- Data aggregation: groupby, data summarizing
- Data merging techniques: merge, join, concatenate
3. Visualization
- Plotting basics with Matplotlib: line plots, bar plots, histograms
- Advanced visualization with Seaborn: scatter plots, box plots, pair plots
- Plot customization: sizes, labels, legends, colors
- Introduction to interactive visualizations with Plotly
Excel:
1. Basics
- Cell operations and basic formulas: SUMIFS, COUNTIFS, AVERAGEIFS
- Charts and introductory data visualization
- Data sorting and filtering, Conditional formatting
2. Intermediate
- Advanced formulas: V/XLOOKUP, INDEX-MATCH, complex IF scenarios
- Summarizing data with PivotTables and PivotCharts
- Tools for data validation and what-if analysis: Data Tables, Goal Seek
3. Advanced
- Utilizing array formulas and sophisticated functions
- Building a Data Model & using Power Pivot
- Advanced filtering, Slicers and Timelines in Pivot Tables
- Crafting dynamic charts and interactive dashboards
Power BI:
1. Data Modeling
- Importing data from diverse sources
- Creating and managing dataset relationships
- Data modeling essentials: star schema, snowflake schema
2. Data Transformation
- Data cleaning and transformation with Power Query
- Advanced data shaping techniques
- Implementing calculated columns and measures with DAX
3. Data Visualization and Reporting
- Developing interactive reports and dashboards
- Visualization types: bar, line, pie charts, maps
- Report publishing and sharing, scheduling data refreshes
Statistics:
Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution
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๐5โค1
Data Analyst Interview Questions
1. What are Support Vectors in SVM?
A Support Vector Machine (SVM) is an algorithm that tries to fit a line (or plane or hyperplane) between the different classes that maximizes the distance from the line to the points of the classes.
In this way, it tries to find a robust separation between the classes. The Support Vectors are the points of the edge of the dividing hyperplane.
2. Explain Correlation and Covariance?
Covariance signifies the direction of the linear relationship between two variables, whereas correlation indicates both the direction and strength of the linear relationship between variables.
3.What is the cluster sampling techniques used for sampling?
Cluster sampling also involves dividing the population into sub-populations, but each subpopulation should have analogous characteristics to that of the whole sample. Rather than sampling individuals from each subpopulation, you randomly select the entire subpopulation.
4. What is P-value?
P-values are used to make a decision about a hypothesis test. P-value is the minimum significant level at which you can reject the null hypothesis. The lower the p-value, the more likely you reject the null hypothesis.
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1. What are Support Vectors in SVM?
A Support Vector Machine (SVM) is an algorithm that tries to fit a line (or plane or hyperplane) between the different classes that maximizes the distance from the line to the points of the classes.
In this way, it tries to find a robust separation between the classes. The Support Vectors are the points of the edge of the dividing hyperplane.
2. Explain Correlation and Covariance?
Covariance signifies the direction of the linear relationship between two variables, whereas correlation indicates both the direction and strength of the linear relationship between variables.
3.What is the cluster sampling techniques used for sampling?
Cluster sampling also involves dividing the population into sub-populations, but each subpopulation should have analogous characteristics to that of the whole sample. Rather than sampling individuals from each subpopulation, you randomly select the entire subpopulation.
4. What is P-value?
P-values are used to make a decision about a hypothesis test. P-value is the minimum significant level at which you can reject the null hypothesis. The lower the p-value, the more likely you reject the null hypothesis.
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๐5
Data Analyst interview questions ๐
Excel:
1. Explain the difference between the "COUNT", "COUNTA", "COUNTIF", and "COUNTIFS" functions in Excel. When would you use each of these functions, and provide examples?
2. How do you create a pivot chart in Excel, and what are some advantages of using pivot charts for data visualization?
3. Describe the purpose and usage of Excel's "Solver" tool. Can you provide an example of a problem you could solve using the Solver tool?
4. How would you use Excel's "Data Validation" feature to ensure data integrity in a spreadsheet? Provide examples of different types of data validation rules you might implement.
5. What are Excel tables, and how do they differ from regular data ranges? What advantages do tables offer in terms of data management and analysis?
SQL:
1. Discuss the concept of data aggregation in SQL. How do you use aggregate functions such as SUM, AVG, MIN, and MAX to summarize data in a query?
2. Explain the difference between a primary key and a foreign key in SQL. Why are these constraints important in database design?
3. How do you handle duplicates in a SQL query result? Can you demonstrate how to remove duplicates using the DISTINCT keyword or other techniques?
4. Describe the purpose and benefits of using stored procedures in SQL databases. Provide an example of a scenario where you would use a stored procedure.
5. What is SQL injection, and how can you prevent it in your SQL queries or applications? Discuss best practices for writing secure SQL code.
Power BI:
1. How does Power BI handle data refresh and scheduling for reports and dashboards? What options are available for configuring data refresh settings?
2. Describe the concept of row-level security in Power BI. How can you implement row-level security to restrict access to specific data based on user roles or permissions?
3. What is the Power Query Editor in Power BI, and how do you use it to transform and clean data imported from different sources?
4. Discuss the benefits of using Power BI's Direct Query mode versus Import mode for connecting to data sources. When would you choose one mode over the other?
5. How do you share reports and dashboards with other users in Power BI? What options are available for distributing and collaborating on Power BI content within an organization?
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Excel:
1. Explain the difference between the "COUNT", "COUNTA", "COUNTIF", and "COUNTIFS" functions in Excel. When would you use each of these functions, and provide examples?
2. How do you create a pivot chart in Excel, and what are some advantages of using pivot charts for data visualization?
3. Describe the purpose and usage of Excel's "Solver" tool. Can you provide an example of a problem you could solve using the Solver tool?
4. How would you use Excel's "Data Validation" feature to ensure data integrity in a spreadsheet? Provide examples of different types of data validation rules you might implement.
5. What are Excel tables, and how do they differ from regular data ranges? What advantages do tables offer in terms of data management and analysis?
SQL:
1. Discuss the concept of data aggregation in SQL. How do you use aggregate functions such as SUM, AVG, MIN, and MAX to summarize data in a query?
2. Explain the difference between a primary key and a foreign key in SQL. Why are these constraints important in database design?
3. How do you handle duplicates in a SQL query result? Can you demonstrate how to remove duplicates using the DISTINCT keyword or other techniques?
4. Describe the purpose and benefits of using stored procedures in SQL databases. Provide an example of a scenario where you would use a stored procedure.
5. What is SQL injection, and how can you prevent it in your SQL queries or applications? Discuss best practices for writing secure SQL code.
Power BI:
1. How does Power BI handle data refresh and scheduling for reports and dashboards? What options are available for configuring data refresh settings?
2. Describe the concept of row-level security in Power BI. How can you implement row-level security to restrict access to specific data based on user roles or permissions?
3. What is the Power Query Editor in Power BI, and how do you use it to transform and clean data imported from different sources?
4. Discuss the benefits of using Power BI's Direct Query mode versus Import mode for connecting to data sources. When would you choose one mode over the other?
5. How do you share reports and dashboards with other users in Power BI? What options are available for distributing and collaborating on Power BI content within an organization?
I have curated best 80+ top-notch Data Analytics Resources ๐๐
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๐7