๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐ฉ๐ถ๐ฟ๐๐๐ฎ๐น ๐๐ป๐๐ฒ๐ฟ๐ป๐๐ต๐ถ๐ฝ ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐ ๐๐ป ๐ง๐ผ๐ฝ ๐๐ผ๐บ๐ฝ๐ฎ๐ป๐ถ๐ฒ๐๐
1๏ธโฃ BCG Data Science & Analytics Virtual Experience
2๏ธโฃ TATA Data Visualization Internship
3๏ธโฃ Accenture Data Analytics Virtual Internship
๐๐ข๐ง๐ค๐:-
https://pdlink.in/409RHXN
Enroll for FREE & Get Certified ๐
1๏ธโฃ BCG Data Science & Analytics Virtual Experience
2๏ธโฃ TATA Data Visualization Internship
3๏ธโฃ Accenture Data Analytics Virtual Internship
๐๐ข๐ง๐ค๐:-
https://pdlink.in/409RHXN
Enroll for FREE & Get Certified ๐
โค1
Data Analyst Interview Questions & Preparation Tips
Be prepared with a mix of technical, analytical, and business-oriented interview questions.
1. Technical Questions (Data Analysis & Reporting)
SQL Questions:
How do you write a query to fetch the top 5 highest revenue-generating customers?
Explain the difference between INNER JOIN, LEFT JOIN, and FULL OUTER JOIN.
How would you optimize a slow-running query?
What are CTEs and when would you use them?
Data Visualization (Power BI / Tableau / Excel)
How would you create a dashboard to track key performance metrics?
Explain the difference between measures and calculated columns in Power BI.
How do you handle missing data in Tableau?
What are DAX functions, and can you give an example?
ETL & Data Processing (Alteryx, Power BI, Excel)
What is ETL, and how does it relate to BI?
Have you used Alteryx for data transformation? Explain a complex workflow you built.
How do you automate reporting using Power Query in Excel?
2. Business and Analytical Questions
How do you define KPIs for a business process?
Give an example of how you used data to drive a business decision.
How would you identify cost-saving opportunities in a reporting process?
Explain a time when your report uncovered a hidden business insight.
3. Scenario-Based & Behavioral Questions
Stakeholder Management:
How do you handle a situation where different business units have conflicting reporting requirements?
How do you explain complex data insights to non-technical stakeholders?
Problem-Solving & Debugging:
What would you do if your report is showing incorrect numbers?
How do you ensure the accuracy of a new KPI you introduced?
Project Management & Process Improvement:
Have you led a project to automate or improve a reporting process?
What steps do you take to ensure the timely delivery of reports?
4. Industry-Specific Questions (Credit Reporting & Financial Services)
What are some key credit risk metrics used in financial services?
How would you analyze trends in customer credit behavior?
How do you ensure compliance and data security in reporting?
5. General HR Questions
Why do you want to work at this company?
Tell me about a challenging project and how you handled it.
What are your strengths and weaknesses?
Where do you see yourself in five years?
How to Prepare?
Brush up on SQL, Power BI, and ETL tools (especially Alteryx).
Learn about key financial and credit reporting metrics.(varies company to company)
Practice explaining data-driven insights in a business-friendly manner.
Be ready to showcase problem-solving skills with real-world examples.
React with โค๏ธ if you want me to also post sample answer for the above questions
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
Be prepared with a mix of technical, analytical, and business-oriented interview questions.
1. Technical Questions (Data Analysis & Reporting)
SQL Questions:
How do you write a query to fetch the top 5 highest revenue-generating customers?
Explain the difference between INNER JOIN, LEFT JOIN, and FULL OUTER JOIN.
How would you optimize a slow-running query?
What are CTEs and when would you use them?
Data Visualization (Power BI / Tableau / Excel)
How would you create a dashboard to track key performance metrics?
Explain the difference between measures and calculated columns in Power BI.
How do you handle missing data in Tableau?
What are DAX functions, and can you give an example?
ETL & Data Processing (Alteryx, Power BI, Excel)
What is ETL, and how does it relate to BI?
Have you used Alteryx for data transformation? Explain a complex workflow you built.
How do you automate reporting using Power Query in Excel?
2. Business and Analytical Questions
How do you define KPIs for a business process?
Give an example of how you used data to drive a business decision.
How would you identify cost-saving opportunities in a reporting process?
Explain a time when your report uncovered a hidden business insight.
3. Scenario-Based & Behavioral Questions
Stakeholder Management:
How do you handle a situation where different business units have conflicting reporting requirements?
How do you explain complex data insights to non-technical stakeholders?
Problem-Solving & Debugging:
What would you do if your report is showing incorrect numbers?
How do you ensure the accuracy of a new KPI you introduced?
Project Management & Process Improvement:
Have you led a project to automate or improve a reporting process?
What steps do you take to ensure the timely delivery of reports?
4. Industry-Specific Questions (Credit Reporting & Financial Services)
What are some key credit risk metrics used in financial services?
How would you analyze trends in customer credit behavior?
How do you ensure compliance and data security in reporting?
5. General HR Questions
Why do you want to work at this company?
Tell me about a challenging project and how you handled it.
What are your strengths and weaknesses?
Where do you see yourself in five years?
How to Prepare?
Brush up on SQL, Power BI, and ETL tools (especially Alteryx).
Learn about key financial and credit reporting metrics.(varies company to company)
Practice explaining data-driven insights in a business-friendly manner.
Be ready to showcase problem-solving skills with real-world examples.
React with โค๏ธ if you want me to also post sample answer for the above questions
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
๐3
๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ง๐ผ ๐ฆ๐ธ๐๐ฟ๐ผ๐ฐ๐ธ๐ฒ๐ ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ๐
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โค1๐1
Most Important Python Topics for Data Analyst Interview:
#Basics of Python:
1. Data Types
2. Lists
3. Dictionaries
4. Control Structures:
- if-elif-else
- Loops
5. Functions
6. Practice basic FAQs questions, below mentioned are few examples:
- How to reverse a string in Python?
- How to find the largest/smallest number in a list?
- How to remove duplicates from a list?
- How to count the occurrences of each element in a list?
- How to check if a string is a palindrome?
#Pandas:
1. Pandas Data Structures (Series, DataFrame)
2. Creating and Manipulating DataFrames
3. Filtering and Selecting Data
4. Grouping and Aggregating Data
5. Handling Missing Values
6. Merging and Joining DataFrames
7. Adding and Removing Columns
8. Exploratory Data Analysis (EDA):
- Descriptive Statistics
- Data Visualization with Pandas (Line Plots, Bar Plots, Histograms)
- Correlation and Covariance
- Handling Duplicates
- Data Transformation
#Numpy:
1. NumPy Arrays
2. Array Operations:
- Creating Arrays
- Slicing and Indexing
- Arithmetic Operations
#Integration with Other Libraries:
1. Basic Data Visualization with Pandas (Line Plots, Bar Plots)
#Key Concepts to Revise:
1. Data Manipulation with Pandas and NumPy
2. Data Cleaning Techniques
3. File Handling (reading and writing CSV files, JSON files)
4. Handling Missing and Duplicate Values
5. Data Transformation (scaling, normalization)
6. Data Aggregation and Group Operations
7. Combining and Merging Datasets
#Basics of Python:
1. Data Types
2. Lists
3. Dictionaries
4. Control Structures:
- if-elif-else
- Loops
5. Functions
6. Practice basic FAQs questions, below mentioned are few examples:
- How to reverse a string in Python?
- How to find the largest/smallest number in a list?
- How to remove duplicates from a list?
- How to count the occurrences of each element in a list?
- How to check if a string is a palindrome?
#Pandas:
1. Pandas Data Structures (Series, DataFrame)
2. Creating and Manipulating DataFrames
3. Filtering and Selecting Data
4. Grouping and Aggregating Data
5. Handling Missing Values
6. Merging and Joining DataFrames
7. Adding and Removing Columns
8. Exploratory Data Analysis (EDA):
- Descriptive Statistics
- Data Visualization with Pandas (Line Plots, Bar Plots, Histograms)
- Correlation and Covariance
- Handling Duplicates
- Data Transformation
#Numpy:
1. NumPy Arrays
2. Array Operations:
- Creating Arrays
- Slicing and Indexing
- Arithmetic Operations
#Integration with Other Libraries:
1. Basic Data Visualization with Pandas (Line Plots, Bar Plots)
#Key Concepts to Revise:
1. Data Manipulation with Pandas and NumPy
2. Data Cleaning Techniques
3. File Handling (reading and writing CSV files, JSON files)
4. Handling Missing and Duplicate Values
5. Data Transformation (scaling, normalization)
6. Data Aggregation and Group Operations
7. Combining and Merging Datasets
๐5
๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐ ๐ญ๐ฌ๐ฌ% ๐๐ฟ๐ฒ๐ฒ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ณ๐ผ๐ฟ ๐๐๐๐ฟ๐ฒ, ๐๐, ๐๐๐ฏ๐ฒ๐ฟ๐๐ฒ๐ฐ๐๐ฟ๐ถ๐๐ & ๐ ๐ผ๐ฟ๐ฒ๐
Want to upskill in Azure, AI, Cybersecurity, or App Developmentโwithout spending a single rupee?๐จโ๐ป๐ฏ
Enter Microsoft Learn โ a 100% free platform that offers expert-led learning paths to help you grow๐๐
๐๐ข๐ง๐ค๐:-
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Enjoy Learning โ ๏ธ
Want to upskill in Azure, AI, Cybersecurity, or App Developmentโwithout spending a single rupee?๐จโ๐ป๐ฏ
Enter Microsoft Learn โ a 100% free platform that offers expert-led learning paths to help you grow๐๐
๐๐ข๐ง๐ค๐:-
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Enjoy Learning โ ๏ธ
โค1
Here are some most popular Python libraries for data visualization:
Matplotlib โ The most fundamental library for static charts. Best for basic visualizations like line, bar, and scatter plots. Highly customizable but requires more coding.
Seaborn โ Built on Matplotlib, it simplifies statistical data visualization with beautiful defaults. Ideal for correlation heatmaps, categorical plots, and distribution analysis.
Plotly โ Best for interactive visualizations with zooming, hovering, and real-time updates. Great for dashboards, web applications, and 3D plotting.
Bokeh โ Designed for interactive and web-based visualizations. Excellent for handling large datasets, streaming data, and integrating with Flask/Django.
Altair โ A declarative library that makes complex statistical plots easy with minimal code. Best for quick and clean data exploration.
For static charts, start with Matplotlib or Seaborn. If you need interactivity, use Plotly or Bokeh. For quick EDA, Altair is a great choice.
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
#python
Matplotlib โ The most fundamental library for static charts. Best for basic visualizations like line, bar, and scatter plots. Highly customizable but requires more coding.
Seaborn โ Built on Matplotlib, it simplifies statistical data visualization with beautiful defaults. Ideal for correlation heatmaps, categorical plots, and distribution analysis.
Plotly โ Best for interactive visualizations with zooming, hovering, and real-time updates. Great for dashboards, web applications, and 3D plotting.
Bokeh โ Designed for interactive and web-based visualizations. Excellent for handling large datasets, streaming data, and integrating with Flask/Django.
Altair โ A declarative library that makes complex statistical plots easy with minimal code. Best for quick and clean data exploration.
For static charts, start with Matplotlib or Seaborn. If you need interactivity, use Plotly or Bokeh. For quick EDA, Altair is a great choice.
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
#python
๐3
๐๐ฅ๐๐ ๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐
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๐2
Excel vs SQL vs Python (pandas):
1๏ธโฃ Filtering Data
โณ Excel: =FILTER(A2:D100, B2:B100>50) (Excel 365 users)
โณ SQL: SELECT * FROM table WHERE column > 50;
โณ Python: df_filtered = df[df['column'] > 50]
2๏ธโฃ Sorting Data
โณ Excel: Data โ Sort (or =SORT(A2:A100, 1, TRUE))
โณ SQL: SELECT * FROM table ORDER BY column ASC;
โณ Python: df_sorted = df.sort_values(by="column")
3๏ธโฃ Counting Rows
โณ Excel: =COUNTA(A:A)
โณ SQL: SELECT COUNT(*) FROM table;
โณ Python: row_count = len(df)
4๏ธโฃ Removing Duplicates
โณ Excel: Data โ Remove Duplicates
โณ SQL: SELECT DISTINCT * FROM table;
โณ Python: df_unique = df.drop_duplicates()
5๏ธโฃ Joining Tables
โณ Excel: Power Query โ Merge Queries (or VLOOKUP/XLOOKUP)
โณ SQL: SELECT * FROM table1 JOIN table2 ON table1.id = table2.id;
โณ Python: df_merged = pd.merge(df1, df2, on="id")
6๏ธโฃ Ranking Data
โณ Excel: =RANK.EQ(A2, $A$2:$A$100)
โณ SQL: SELECT column, RANK() OVER (ORDER BY column DESC) AS rank FROM table;
โณ Python: df["rank"] = df["column"].rank(method="min", ascending=False)
7๏ธโฃ Moving Average Calculation
โณ Excel: =AVERAGE(B2:B4) (manually for rolling window)
โณ SQL: SELECT date, AVG(value) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg FROM table;
โณ Python: df["moving_avg"] = df["value"].rolling(window=3).mean()
8๏ธโฃ Running Total
โณ Excel: =SUM($B$2:B2) (drag down)
โณ SQL: SELECT date, SUM(value) OVER (ORDER BY date) AS running_total FROM table;
โณ Python: df["running_total"] = df["value"].cumsum()
1๏ธโฃ Filtering Data
โณ Excel: =FILTER(A2:D100, B2:B100>50) (Excel 365 users)
โณ SQL: SELECT * FROM table WHERE column > 50;
โณ Python: df_filtered = df[df['column'] > 50]
2๏ธโฃ Sorting Data
โณ Excel: Data โ Sort (or =SORT(A2:A100, 1, TRUE))
โณ SQL: SELECT * FROM table ORDER BY column ASC;
โณ Python: df_sorted = df.sort_values(by="column")
3๏ธโฃ Counting Rows
โณ Excel: =COUNTA(A:A)
โณ SQL: SELECT COUNT(*) FROM table;
โณ Python: row_count = len(df)
4๏ธโฃ Removing Duplicates
โณ Excel: Data โ Remove Duplicates
โณ SQL: SELECT DISTINCT * FROM table;
โณ Python: df_unique = df.drop_duplicates()
5๏ธโฃ Joining Tables
โณ Excel: Power Query โ Merge Queries (or VLOOKUP/XLOOKUP)
โณ SQL: SELECT * FROM table1 JOIN table2 ON table1.id = table2.id;
โณ Python: df_merged = pd.merge(df1, df2, on="id")
6๏ธโฃ Ranking Data
โณ Excel: =RANK.EQ(A2, $A$2:$A$100)
โณ SQL: SELECT column, RANK() OVER (ORDER BY column DESC) AS rank FROM table;
โณ Python: df["rank"] = df["column"].rank(method="min", ascending=False)
7๏ธโฃ Moving Average Calculation
โณ Excel: =AVERAGE(B2:B4) (manually for rolling window)
โณ SQL: SELECT date, AVG(value) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg FROM table;
โณ Python: df["moving_avg"] = df["value"].rolling(window=3).mean()
8๏ธโฃ Running Total
โณ Excel: =SUM($B$2:B2) (drag down)
โณ SQL: SELECT date, SUM(value) OVER (ORDER BY date) AS running_total FROM table;
โณ Python: df["running_total"] = df["value"].cumsum()
๐5
๐ง๐ผ๐ฝ ๐ฃ๐๐๐ต๐ผ๐ป ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ค๐๐ฒ๐๐๐ถ๐ผ๐ป๐ ๐ณ๐ผ๐ฟ ๐ฎ๐ฌ๐ฎ๐ฑ โ ๐ฅ๐ฒ๐ฐ๐ฒ๐ป๐๐น๐ ๐๐๐ธ๐ฒ๐ฑ ๐ฏ๐ ๐ ๐ก๐๐๐
๐ Preparing for Python Interviews in 2025?๐ฃ
If youโre aiming for roles in data analysis, backend development, or automation, Python is your key weaponโand so is preparing with the right questions.๐ปโจ๏ธ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3ZbAtrW
Crack your next Python interviewโ ๏ธ
๐ Preparing for Python Interviews in 2025?๐ฃ
If youโre aiming for roles in data analysis, backend development, or automation, Python is your key weaponโand so is preparing with the right questions.๐ปโจ๏ธ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3ZbAtrW
Crack your next Python interviewโ ๏ธ
๐2
Here is a powerful ๐๐ก๐ง๐๐ฅ๐ฉ๐๐๐ช ๐ง๐๐ฃ to help you land a job!
Most people who are skilled enough would be able to clear technical rounds with ease.
But when it comes to ๐ฏ๐ฒ๐ต๐ฎ๐๐ถ๐ผ๐ฟ๐ฎ๐น/๐ฐ๐๐น๐๐๐ฟ๐ฒ ๐ณ๐ถ๐ rounds, some folks may falter and lose the potential offer.
Many companies schedule a behavioral round with a top-level manager in the organization to understand the culture fit (except for freshers).
One needs to clear this round to reach the salary negotiation round.
Here are some tips to clear such rounds:
1๏ธโฃ Once the HR schedules the interview, try to find the LinkedIn profile of the interviewer using the name in their email ID.
2๏ธโฃ Learn more about his/her past experiences and try to strike up a conversation on that during the interview.
3๏ธโฃ This shows that you have done good research and also helps strike a personal connection.
4๏ธโฃ Also, this is the round not just to evaluate if you're a fit for the company, but also to assess if the company is a right fit for you.
5๏ธโฃ Hence, feel free to ask many questions about your role and company to get a clear understanding before taking the offer. This shows that you really care about the role you're getting into.
๐ก ๐๐ผ๐ป๐๐ ๐๐ถ๐ฝ - Be polite yet assertive in such interviews. It impresses a lot of senior folks.
Most people who are skilled enough would be able to clear technical rounds with ease.
But when it comes to ๐ฏ๐ฒ๐ต๐ฎ๐๐ถ๐ผ๐ฟ๐ฎ๐น/๐ฐ๐๐น๐๐๐ฟ๐ฒ ๐ณ๐ถ๐ rounds, some folks may falter and lose the potential offer.
Many companies schedule a behavioral round with a top-level manager in the organization to understand the culture fit (except for freshers).
One needs to clear this round to reach the salary negotiation round.
Here are some tips to clear such rounds:
1๏ธโฃ Once the HR schedules the interview, try to find the LinkedIn profile of the interviewer using the name in their email ID.
2๏ธโฃ Learn more about his/her past experiences and try to strike up a conversation on that during the interview.
3๏ธโฃ This shows that you have done good research and also helps strike a personal connection.
4๏ธโฃ Also, this is the round not just to evaluate if you're a fit for the company, but also to assess if the company is a right fit for you.
5๏ธโฃ Hence, feel free to ask many questions about your role and company to get a clear understanding before taking the offer. This shows that you really care about the role you're getting into.
๐ก ๐๐ผ๐ป๐๐ ๐๐ถ๐ฝ - Be polite yet assertive in such interviews. It impresses a lot of senior folks.
๐4
๐ฑ ๐๐ฟ๐ฒ๐ฒ ๐ ๐๐ง ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐บ๐ถ๐ป๐ด ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ง๐ต๐ฎ๐ ๐๐๐ฒ๐ฟ๐ ๐๐ฒ๐ด๐ถ๐ป๐ป๐ฒ๐ฟ ๐ฆ๐ต๐ผ๐๐น๐ฑ ๐ฆ๐๐ฎ๐ฟ๐ ๐ช๐ถ๐๐ต๐
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๐1
๐ณ ๐๐ฅ๐๐ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ง๐ผ ๐ ๐ฎ๐๐๐ฒ๐ฟ ๐ง๐ฒ๐ฐ๐ต ๐ฆ๐ธ๐ถ๐น๐น๐ ๐๐ป ๐ฎ๐ฌ๐ฎ๐ฑ ๐
If you dream of a tech career but donโt want to break the bank, youโre in the right place.
These 7 hand-picked resources are free and help you build real, job-ready skillsโfrom web development to machine learning and AI.
๐๐ข๐ง๐ค ๐:-
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Enroll for FREE & Get Certified ๐
If you dream of a tech career but donโt want to break the bank, youโre in the right place.
These 7 hand-picked resources are free and help you build real, job-ready skillsโfrom web development to machine learning and AI.
๐๐ข๐ง๐ค ๐:-
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Enroll for FREE & Get Certified ๐
๐2
๐ฑ ๐๐ฟ๐ฒ๐ฒ ๐ ๐๐ง ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ง๐ต๐ฎ๐ ๐ช๐ถ๐น๐น ๐๐ผ๐ผ๐๐ ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐ฟ๐ฒ๐ฒ๐ฟ๐
๐ Want to Learn Data Analytics but Hate the High Price Tags?๐ฐ๐
Good news: MIT is offering free, high-quality data analytics courses through their OpenCourseWare platform๐ป๐ฏ
๐๐ข๐ง๐ค๐:-
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All The Best ๐
๐ Want to Learn Data Analytics but Hate the High Price Tags?๐ฐ๐
Good news: MIT is offering free, high-quality data analytics courses through their OpenCourseWare platform๐ป๐ฏ
๐๐ข๐ง๐ค๐:-
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All The Best ๐
๐2
WhatsApp is no longer a platform just for chat.
It's an educational goldmine.
If you do, youโre sleeping on a goldmine of knowledge and community. WhatsApp channels are a great way to practice data science, make your own community, and find accountability partners.
I have curated the list of best WhatsApp channels to learn coding & data science for FREE
Free Courses with Certificate
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