Junior-level Data Analyst interview questions:
Introduction and Background
1. Can you tell me about your background and how you became interested in data analysis?
2. What do you know about our company/organization?
3. Why do you want to work as a data analyst?
Data Analysis and Interpretation
1. What is your experience with data analysis tools like Excel, SQL, or Tableau?
2. How would you approach analyzing a large dataset to identify trends and patterns?
3. Can you explain the concept of correlation versus causation?
4. How do you handle missing or incomplete data?
5. Can you walk me through a time when you had to interpret complex data results?
Technical Skills
1. Write a SQL query to extract data from a database.
2. How do you create a pivot table in Excel?
3. Can you explain the difference between a histogram and a box plot?
4. How do you perform data visualization using Tableau or Power BI?
5. Can you write a simple Python or R noscript to manipulate data?
Statistics and Math
1. What is the difference between mean, median, and mode?
2. Can you explain the concept of standard deviation and variance?
3. How do you calculate probability and confidence intervals?
4. Can you describe a time when you applied statistical concepts to a real-world problem?
5. How do you approach hypothesis testing?
Communication and Storytelling
1. Can you explain a complex data concept to a non-technical person?
2. How do you present data insights to stakeholders?
3. Can you walk me through a time when you had to communicate data results to a team?
4. How do you create effective data visualizations?
5. Can you tell a story using data?
Case Studies and Scenarios
1. You are given a dataset with customer purchase history. How would you analyze it to identify trends?
2. A company wants to increase sales. How would you use data to inform marketing strategies?
3. You notice a discrepancy in sales data. How would you investigate and resolve the issue?
4. Can you describe a time when you had to work with a stakeholder to understand their data needs?
5. How would you prioritize data projects with limited resources?
Behavioral Questions
1. Can you describe a time when you overcame a difficult data analysis challenge?
2. How do you handle tight deadlines and multiple projects?
3. Can you tell me about a project you worked on and your role in it?
4. How do you stay up-to-date with new data tools and technologies?
5. Can you describe a time when you received feedback on your data analysis work?
Final Questions
1. Do you have any questions about the company or role?
2. What do you think sets you apart from other candidates?
3. Can you summarize your experience and qualifications?
4. What are your long-term career goals?
Hope this helps you 😊
Introduction and Background
1. Can you tell me about your background and how you became interested in data analysis?
2. What do you know about our company/organization?
3. Why do you want to work as a data analyst?
Data Analysis and Interpretation
1. What is your experience with data analysis tools like Excel, SQL, or Tableau?
2. How would you approach analyzing a large dataset to identify trends and patterns?
3. Can you explain the concept of correlation versus causation?
4. How do you handle missing or incomplete data?
5. Can you walk me through a time when you had to interpret complex data results?
Technical Skills
1. Write a SQL query to extract data from a database.
2. How do you create a pivot table in Excel?
3. Can you explain the difference between a histogram and a box plot?
4. How do you perform data visualization using Tableau or Power BI?
5. Can you write a simple Python or R noscript to manipulate data?
Statistics and Math
1. What is the difference between mean, median, and mode?
2. Can you explain the concept of standard deviation and variance?
3. How do you calculate probability and confidence intervals?
4. Can you describe a time when you applied statistical concepts to a real-world problem?
5. How do you approach hypothesis testing?
Communication and Storytelling
1. Can you explain a complex data concept to a non-technical person?
2. How do you present data insights to stakeholders?
3. Can you walk me through a time when you had to communicate data results to a team?
4. How do you create effective data visualizations?
5. Can you tell a story using data?
Case Studies and Scenarios
1. You are given a dataset with customer purchase history. How would you analyze it to identify trends?
2. A company wants to increase sales. How would you use data to inform marketing strategies?
3. You notice a discrepancy in sales data. How would you investigate and resolve the issue?
4. Can you describe a time when you had to work with a stakeholder to understand their data needs?
5. How would you prioritize data projects with limited resources?
Behavioral Questions
1. Can you describe a time when you overcame a difficult data analysis challenge?
2. How do you handle tight deadlines and multiple projects?
3. Can you tell me about a project you worked on and your role in it?
4. How do you stay up-to-date with new data tools and technologies?
5. Can you describe a time when you received feedback on your data analysis work?
Final Questions
1. Do you have any questions about the company or role?
2. What do you think sets you apart from other candidates?
3. Can you summarize your experience and qualifications?
4. What are your long-term career goals?
Hope this helps you 😊
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📊 Data Analytics Interview Questions With Answers Part-1 👇
1️⃣ What is Data Analytics and how does it differ from Data Science?
Data Analytics focuses on examining past data using statistical tools & reporting to answer specific business questions. Data Science is broader, using algorithms & machine learning to predict future trends and deeper insights.
2️⃣ How do you handle missing or duplicate data?
⦁ Missing data: remove, impute with mean/median/mode, or predict missing values.
⦁ Duplicate data: identify with functions (e.g., duplicated()) and remove or merge based on context.
3️⃣ Explain denoscriptive vs diagnostic analytics.
⦁ Denoscriptive Analytics: What happened? Summarizes data trends.
⦁ Diagnostic Analytics: Why did it happen? Explores cause-effect relationships.
4️⃣ What is data cleaning and why is it important?
Data cleaning removes errors, inconsistencies, and duplicates to ensure accuracy for analysis and decision-making.
5️⃣ What are common data visualization techniques?
Bar charts, histograms, scatter plots, pie charts, heatmaps, and dashboards.
6️⃣ Explain correlation vs causation.
Correlation indicates a statistical relationship between variables; causation means one variable causes change in another.
7️⃣ How do you choose the right KPI for a project?
Based on business goals, relevance, measurability, and actionability.
8️⃣ What tools do you use for data analytics?
Excel, SQL, Tableau, Power BI, Python (Pandas, Matplotlib), R.
9️⃣ What is ETL and its importance?
Extract, Transform, Load – process to gather data from sources, clean & transform it, then load into data storage ready for querying.
🔟 Difference between structured and unstructured data?
Structured data fits rows & columns (databases). Unstructured data includes text, images, videos, lacking a predefined format.
💬 React ♥️ for Part-2!
1️⃣ What is Data Analytics and how does it differ from Data Science?
Data Analytics focuses on examining past data using statistical tools & reporting to answer specific business questions. Data Science is broader, using algorithms & machine learning to predict future trends and deeper insights.
2️⃣ How do you handle missing or duplicate data?
⦁ Missing data: remove, impute with mean/median/mode, or predict missing values.
⦁ Duplicate data: identify with functions (e.g., duplicated()) and remove or merge based on context.
3️⃣ Explain denoscriptive vs diagnostic analytics.
⦁ Denoscriptive Analytics: What happened? Summarizes data trends.
⦁ Diagnostic Analytics: Why did it happen? Explores cause-effect relationships.
4️⃣ What is data cleaning and why is it important?
Data cleaning removes errors, inconsistencies, and duplicates to ensure accuracy for analysis and decision-making.
5️⃣ What are common data visualization techniques?
Bar charts, histograms, scatter plots, pie charts, heatmaps, and dashboards.
6️⃣ Explain correlation vs causation.
Correlation indicates a statistical relationship between variables; causation means one variable causes change in another.
7️⃣ How do you choose the right KPI for a project?
Based on business goals, relevance, measurability, and actionability.
8️⃣ What tools do you use for data analytics?
Excel, SQL, Tableau, Power BI, Python (Pandas, Matplotlib), R.
9️⃣ What is ETL and its importance?
Extract, Transform, Load – process to gather data from sources, clean & transform it, then load into data storage ready for querying.
🔟 Difference between structured and unstructured data?
Structured data fits rows & columns (databases). Unstructured data includes text, images, videos, lacking a predefined format.
💬 React ♥️ for Part-2!
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9 tips to get started with Data Analysis:
Learn Excel, SQL, and a programming language (Python or R)
Understand basic statistics and probability
Practice with real-world datasets (Kaggle, Data.gov)
Clean and preprocess data effectively
Visualize data using charts and graphs
Ask the right questions before diving into data
Use libraries like Pandas, NumPy, and Matplotlib
Focus on storytelling with data insights
Build small projects to apply what you learn
Data Science & Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING 👍👍
Learn Excel, SQL, and a programming language (Python or R)
Understand basic statistics and probability
Practice with real-world datasets (Kaggle, Data.gov)
Clean and preprocess data effectively
Visualize data using charts and graphs
Ask the right questions before diving into data
Use libraries like Pandas, NumPy, and Matplotlib
Focus on storytelling with data insights
Build small projects to apply what you learn
Data Science & Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING 👍👍
❤8👏1
📊 Data Analytics Interview Questions With Answers – Part 2 👇
1️⃣ What is the difference between OLAP and OLTP?
- OLAP (Online Analytical Processing): Used for analysis, complex queries, historical data.
- OLTP (Online Transaction Processing): Used for day-to-day transactions like insert/update/delete.
2️⃣ What are outliers and how do you handle them?
Outliers are data points significantly different from others. Handle using:
- Removal
- Capping
- Transformation (e.g., log scale)
- Using robust models (e.g., decision trees)
3️⃣ What is data normalization?
Normalization scales data to bring all variables to a common range (like 0 to 1). Helps improve model performance.
4️⃣ What is the difference between inner join and outer join?
- Inner Join: Returns only matching rows from both tables.
- Outer Join: Returns all rows from one or both tables, filling with NULLs when no match.
5️⃣ Explain time series analysis.
A method to analyze data points collected or recorded at specific time intervals (e.g., stock prices, sales).
6️⃣ What is hypothesis testing?
A statistical method to test an assumption about a population parameter using sample data.
7️⃣ What are some key challenges in data analytics?
- Data quality & cleanliness
- Handling large volumes
- Data integration from multiple sources
- Choosing the right model/technique
8️⃣ What is A/B Testing?
A/B testing compares two versions of a variable to determine which one performs better (used in product experiments).
9️⃣ What’s the role of a dashboard?
Dashboards visualize KPIs and metrics in real-time for business monitoring and quick decisions.
🔟 How do you ensure data privacy and security?
By using encryption, access controls, anonymization, and following compliance standards like GDPR.
Tap ❤️ for Part-3!
1️⃣ What is the difference between OLAP and OLTP?
- OLAP (Online Analytical Processing): Used for analysis, complex queries, historical data.
- OLTP (Online Transaction Processing): Used for day-to-day transactions like insert/update/delete.
2️⃣ What are outliers and how do you handle them?
Outliers are data points significantly different from others. Handle using:
- Removal
- Capping
- Transformation (e.g., log scale)
- Using robust models (e.g., decision trees)
3️⃣ What is data normalization?
Normalization scales data to bring all variables to a common range (like 0 to 1). Helps improve model performance.
4️⃣ What is the difference between inner join and outer join?
- Inner Join: Returns only matching rows from both tables.
- Outer Join: Returns all rows from one or both tables, filling with NULLs when no match.
5️⃣ Explain time series analysis.
A method to analyze data points collected or recorded at specific time intervals (e.g., stock prices, sales).
6️⃣ What is hypothesis testing?
A statistical method to test an assumption about a population parameter using sample data.
7️⃣ What are some key challenges in data analytics?
- Data quality & cleanliness
- Handling large volumes
- Data integration from multiple sources
- Choosing the right model/technique
8️⃣ What is A/B Testing?
A/B testing compares two versions of a variable to determine which one performs better (used in product experiments).
9️⃣ What’s the role of a dashboard?
Dashboards visualize KPIs and metrics in real-time for business monitoring and quick decisions.
🔟 How do you ensure data privacy and security?
By using encryption, access controls, anonymization, and following compliance standards like GDPR.
Tap ❤️ for Part-3!
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Excel Shortcut Keys Part-1 🔐 🗝️
1. Ctrl + N: To create a new workbook.
2. Ctrl + O: To open a saved workbook.
3. Ctrl + S: To save a workbook.
4. Ctrl + A: To select all the contents in a workbook.
5. Ctrl + B: To turn highlighted cells bold.
6. Ctrl + C: To copy cells that are highlighted.
7. Ctrl + D: To fill the selected cell with the content of the cell right above.
8. Ctrl + F: To search for anything in a workbook.
9. Ctrl + G: To jump to a certain area with a single command.
10. Ctrl + H: To find and replace cell contents.
11. Ctrl + I: To italicise cell contents.
12. Ctrl + K: To insert a hyperlink in a cell.
13. Ctrl + L: To open the create table dialog box.
14. Ctrl + P: To print a workbook.
15. Ctrl + R: To fill the selected cell with the content of the cell on the left.
16. Ctrl + U: To underline highlighted cells.
17. Ctrl + V: To paste anything that was copied.
18. Ctrl + W: To close your current workbook.
19. Ctrl + Z: To undo the last action.
20. Ctrl + 1: To format the cell contents.
21. Ctrl + 5: To put a strikethrough in a cell.
22. Ctrl + 8: To show the outline symbols.
23. Ctrl + 9: To hide a row.
24. Ctrl + 0: To hide a column.
25. Ctrl + Shift + :: To enter the current time in a cell.
26. Ctrl + ;: To enter the current date in a cell.
27. Ctrl + `: To change the view from displaying cell values to formulas.
28. Ctrl + ‘: To copy the formula from the cell above.
29. Ctrl + -: To delete columns or rows.
30. Ctrl + Shift + =: To insert columns and rows.
31. Ctrl + Shift + ~: To switch between displaying Excel formulas or their values in cell.
32. Ctrl + Shift + @: To apply time formatting.
33. Ctrl + Shift + !: To apply comma formatting.
34. Ctrl + Shift + $: To apply currency formatting.
35. Ctrl + Shift + #: To apply date formatting.
36. Ctrl + Shift + %: To apply percentage formatting.
37. Ctrl + Shift + &: To place borders around the selected cells.
38. Ctrl + Shift + _: To remove a border.
39. Ctrl + -: To delete a selected row or column.
40. Ctrl + Spacebar: To select an entire column.
41. Ctrl + Shift + Spacebar: To select an entire workbook.
42. Ctrl + Home: To redirect to cell A1.
43. Ctrl + Shift + Tab: To switch to the previous workbook.
44. Ctrl + Shift + F: To open the fonts menu under format cells.
45. Ctrl + Shift + O: To select the cells containing comments.
46. Ctrl + Drag: To drag and copy a cell or to a duplicate worksheet.
47. Ctrl + Shift + Drag: To drag and insert copy.
48. Ctrl + Up arrow: To go to the top most cell in a current column.
49. Ctrl + Down arrow: To jump to the last cell in a current column.
50. Ctrl + Right arrow: To go to the last cell in a selected row.
1. Ctrl + N: To create a new workbook.
2. Ctrl + O: To open a saved workbook.
3. Ctrl + S: To save a workbook.
4. Ctrl + A: To select all the contents in a workbook.
5. Ctrl + B: To turn highlighted cells bold.
6. Ctrl + C: To copy cells that are highlighted.
7. Ctrl + D: To fill the selected cell with the content of the cell right above.
8. Ctrl + F: To search for anything in a workbook.
9. Ctrl + G: To jump to a certain area with a single command.
10. Ctrl + H: To find and replace cell contents.
11. Ctrl + I: To italicise cell contents.
12. Ctrl + K: To insert a hyperlink in a cell.
13. Ctrl + L: To open the create table dialog box.
14. Ctrl + P: To print a workbook.
15. Ctrl + R: To fill the selected cell with the content of the cell on the left.
16. Ctrl + U: To underline highlighted cells.
17. Ctrl + V: To paste anything that was copied.
18. Ctrl + W: To close your current workbook.
19. Ctrl + Z: To undo the last action.
20. Ctrl + 1: To format the cell contents.
21. Ctrl + 5: To put a strikethrough in a cell.
22. Ctrl + 8: To show the outline symbols.
23. Ctrl + 9: To hide a row.
24. Ctrl + 0: To hide a column.
25. Ctrl + Shift + :: To enter the current time in a cell.
26. Ctrl + ;: To enter the current date in a cell.
27. Ctrl + `: To change the view from displaying cell values to formulas.
28. Ctrl + ‘: To copy the formula from the cell above.
29. Ctrl + -: To delete columns or rows.
30. Ctrl + Shift + =: To insert columns and rows.
31. Ctrl + Shift + ~: To switch between displaying Excel formulas or their values in cell.
32. Ctrl + Shift + @: To apply time formatting.
33. Ctrl + Shift + !: To apply comma formatting.
34. Ctrl + Shift + $: To apply currency formatting.
35. Ctrl + Shift + #: To apply date formatting.
36. Ctrl + Shift + %: To apply percentage formatting.
37. Ctrl + Shift + &: To place borders around the selected cells.
38. Ctrl + Shift + _: To remove a border.
39. Ctrl + -: To delete a selected row or column.
40. Ctrl + Spacebar: To select an entire column.
41. Ctrl + Shift + Spacebar: To select an entire workbook.
42. Ctrl + Home: To redirect to cell A1.
43. Ctrl + Shift + Tab: To switch to the previous workbook.
44. Ctrl + Shift + F: To open the fonts menu under format cells.
45. Ctrl + Shift + O: To select the cells containing comments.
46. Ctrl + Drag: To drag and copy a cell or to a duplicate worksheet.
47. Ctrl + Shift + Drag: To drag and insert copy.
48. Ctrl + Up arrow: To go to the top most cell in a current column.
49. Ctrl + Down arrow: To jump to the last cell in a current column.
50. Ctrl + Right arrow: To go to the last cell in a selected row.
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Excel Shortcut Keys Part-2 🔐 🗝️
51. Ctrl + Left arrow: To jump back to the first cell in a selected row.
52. Ctrl + End: To go to the last cell in a workbook.
53. Alt + Page down: To move the screen towards the right.
54. Alt + Page Up: To move the screen towards the left.
55. Ctrl + F2: To open the print preview window.
56. Ctrl + F1: To expand or collapse the ribbon.
57. Alt: To open the access keys.
58. Tab: Move to the next cell.
59. Alt + F + T: To open the options.
60. Alt + Down arrow: To activate filters for cells.
61. F2: To edit a cell.
62. F3: To paste a cell name if the cells have been named.
63. Shift + F2: To add or edit a cell comment.
64. Alt + H + H: To select a fill colour.
65. Alt + H + B: To add a border.
66. Ctrl + 9: To hide the selected rows.
67. Ctrl + 0: To hide the selected columns.
68. Esc: To cancel an entry.
69. Enter: To complete the entry in a cell and move to the next one.
70. Shift + Right arrow: To extend the cell selection to the right.
71. Shift + Left arrow: To extend the cell selection to the left.
72. Shift + Space: To select the entire row.
73. Page up/ down: To move the screen up or down.
74. Alt + H: To go to the Home tab in Ribbon.
75. Alt + N: To go to the Insert tab in Ribbon.
76. Alt + P: To go to the Page Layout tab in Ribbon.
77. Alt + M: To go to the Formulas tab in Ribbon.
78. Alt + A: To go to the Data tab in Ribbon.
79. Alt + R: To go to the Review tab in Ribbon.
80. Alt + W: To go to the View tab in Ribbon.
81. Alt + Y: To open the Help tab in Ribbon.
82. Alt + Q: To quickly jump to search.
83. Alt + Enter: To start a new line in a current cell.
84. Shift + F3: To open the Insert function dialog box.
85. F9: To calculate workbooks.
86. Shift + F9: To calculate an active workbook.
87. Ctrl + Alt + F9: To force calculate all workbooks.
88. Ctrl + F3: To open the name manager.
89. Ctrl + Shift + F3: To create names from values in rows and columns.
90. Ctrl + Alt + +: To zoom in inside a workbook.
91. Ctrl + Alt +: To zoom out inside a workbook.
92. Alt + 1: To turn on Autosave.
93. Alt + 2: To save a workbook.
94. Alt + F + E: To export your workbook.
95. Alt + F + Z: To share your workbook.
96. Alt + F + C: To close and save your workbook.
97. Alt or F11: To turn key tips on or off.
98. Alt + Y + W: To know what's new in Microsoft Excel.
99. F1: To open Microsoft Excel help.
100. Ctrl + F4: To close Microsoft Excel.
Free Excel Resources: https://whatsapp.com/channel/0029VaifY548qIzv0u1AHz3i
Double Tap ♥️ For More
51. Ctrl + Left arrow: To jump back to the first cell in a selected row.
52. Ctrl + End: To go to the last cell in a workbook.
53. Alt + Page down: To move the screen towards the right.
54. Alt + Page Up: To move the screen towards the left.
55. Ctrl + F2: To open the print preview window.
56. Ctrl + F1: To expand or collapse the ribbon.
57. Alt: To open the access keys.
58. Tab: Move to the next cell.
59. Alt + F + T: To open the options.
60. Alt + Down arrow: To activate filters for cells.
61. F2: To edit a cell.
62. F3: To paste a cell name if the cells have been named.
63. Shift + F2: To add or edit a cell comment.
64. Alt + H + H: To select a fill colour.
65. Alt + H + B: To add a border.
66. Ctrl + 9: To hide the selected rows.
67. Ctrl + 0: To hide the selected columns.
68. Esc: To cancel an entry.
69. Enter: To complete the entry in a cell and move to the next one.
70. Shift + Right arrow: To extend the cell selection to the right.
71. Shift + Left arrow: To extend the cell selection to the left.
72. Shift + Space: To select the entire row.
73. Page up/ down: To move the screen up or down.
74. Alt + H: To go to the Home tab in Ribbon.
75. Alt + N: To go to the Insert tab in Ribbon.
76. Alt + P: To go to the Page Layout tab in Ribbon.
77. Alt + M: To go to the Formulas tab in Ribbon.
78. Alt + A: To go to the Data tab in Ribbon.
79. Alt + R: To go to the Review tab in Ribbon.
80. Alt + W: To go to the View tab in Ribbon.
81. Alt + Y: To open the Help tab in Ribbon.
82. Alt + Q: To quickly jump to search.
83. Alt + Enter: To start a new line in a current cell.
84. Shift + F3: To open the Insert function dialog box.
85. F9: To calculate workbooks.
86. Shift + F9: To calculate an active workbook.
87. Ctrl + Alt + F9: To force calculate all workbooks.
88. Ctrl + F3: To open the name manager.
89. Ctrl + Shift + F3: To create names from values in rows and columns.
90. Ctrl + Alt + +: To zoom in inside a workbook.
91. Ctrl + Alt +: To zoom out inside a workbook.
92. Alt + 1: To turn on Autosave.
93. Alt + 2: To save a workbook.
94. Alt + F + E: To export your workbook.
95. Alt + F + Z: To share your workbook.
96. Alt + F + C: To close and save your workbook.
97. Alt or F11: To turn key tips on or off.
98. Alt + Y + W: To know what's new in Microsoft Excel.
99. F1: To open Microsoft Excel help.
100. Ctrl + F4: To close Microsoft Excel.
Free Excel Resources: https://whatsapp.com/channel/0029VaifY548qIzv0u1AHz3i
Double Tap ♥️ For More
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✅ Top Python Libraries for Data Analytics 📊🐍
1. Pandas – Data Handling & Analysis
- Work with tabular data using DataFrames
- Clean, filter, group, and aggregate data
- Read/write from CSV, Excel, JSON
2. NumPy – Numerical Operations
- Efficient array and matrix operations
- Used for data transformation and statistical tasks
3. Matplotlib – Basic Visualization
- Create line, bar, scatter, and pie charts
- Customize noscripts, legends, and styles
4. Seaborn – Statistical Visualization
- Heatmaps, box plots, histograms, and more
- Easy integration with Pandas
5. Plotly – Interactive Graphs
- Zoom, hover, and export visuals
- Great for dashboards and presentations
6. Scikit-learn – Machine Learning for Analysis
- Feature selection, classification, regression
- Data preprocessing & model evaluation
7. Statsmodels – Statistical Analysis
- Perform regression, ANOVA, time series analysis
- Great for data exploration and insight extraction
8. OpenPyXL / xlrd – Excel File Handling
- Read/write Excel files with formulas, formatting, etc.
💡 Pro Tip: Combine Pandas, Seaborn, and Scikit-learn to build complete analytics pipelines.
Tap ❤️ for more!
1. Pandas – Data Handling & Analysis
- Work with tabular data using DataFrames
- Clean, filter, group, and aggregate data
- Read/write from CSV, Excel, JSON
import pandas as pd
df = pd.read_csv("sales.csv")
print(df.head())
2. NumPy – Numerical Operations
- Efficient array and matrix operations
- Used for data transformation and statistical tasks
import numpy as np
arr = np.array([10, 20, 30])
print(arr.mean()) # 20.0
3. Matplotlib – Basic Visualization
- Create line, bar, scatter, and pie charts
- Customize noscripts, legends, and styles
import matplotlib.pyplot as plt
plt.bar(["A", "B", "C"], [10, 20, 15])
plt.show()
4. Seaborn – Statistical Visualization
- Heatmaps, box plots, histograms, and more
- Easy integration with Pandas
import seaborn as sns
sns.boxplot(data=df, x="Region", y="Revenue")
5. Plotly – Interactive Graphs
- Zoom, hover, and export visuals
- Great for dashboards and presentations
import plotly.express as px
fig = px.line(df, x="Month", y="Sales")
fig.show()
6. Scikit-learn – Machine Learning for Analysis
- Feature selection, classification, regression
- Data preprocessing & model evaluation
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
7. Statsmodels – Statistical Analysis
- Perform regression, ANOVA, time series analysis
- Great for data exploration and insight extraction
8. OpenPyXL / xlrd – Excel File Handling
- Read/write Excel files with formulas, formatting, etc.
💡 Pro Tip: Combine Pandas, Seaborn, and Scikit-learn to build complete analytics pipelines.
Tap ❤️ for more!
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50 interview SQL questions, including both technical and non-technical questions, along with their answers PART-1
1. What is SQL?
- Answer: SQL (Structured Query Language) is a standard programming language specifically designed for managing and manipulating relational databases.
2. What are the different types of SQL statements?
- Answer: SQL statements can be classified into DDL (Data Definition Language), DML (Data Manipulation Language), DCL (Data Control Language), and TCL (Transaction Control Language).
3. What is a primary key?
- Answer: A primary key is a field (or combination of fields) in a table that uniquely identifies each row/record in that table.
4. What is a foreign key?
- Answer: A foreign key is a field (or collection of fields) in one table that uniquely identifies a row of another table or the same table. It establishes a link between the data in two tables.
5. What are joins? Explain different types of joins.
- Answer: A join is an SQL operation for combining records from two or more tables. Types of joins include INNER JOIN, LEFT JOIN (or LEFT OUTER JOIN), RIGHT JOIN (or RIGHT OUTER JOIN), and FULL JOIN (or FULL OUTER JOIN).
6. What is normalization?
- Answer: Normalization is the process of organizing data to reduce redundancy and improve data integrity. This typically involves dividing a database into two or more tables and defining relationships between them.
7. What is denormalization?
- Answer: Denormalization is the process of combining normalized tables into fewer tables to improve database read performance, sometimes at the expense of write performance and data integrity.
8. What is stored procedure?
- Answer: A stored procedure is a prepared SQL code that you can save and reuse. So, if you have an SQL query that you write frequently, you can save it as a stored procedure and then call it to execute it.
9. What is an index?
- Answer: An index is a database object that improves the speed of data retrieval operations on a table at the cost of additional storage and maintenance overhead.
10. What is a view in SQL?
- Answer: A view is a virtual table based on the result set of an SQL query. It contains rows and columns, just like a real table, but does not physically store the data.
11. What is a subquery?
- Answer: A subquery is an SQL query nested inside a larger query. It is used to return data that will be used in the main query as a condition to further restrict the data to be retrieved.
12. What are aggregate functions in SQL?
- Answer: Aggregate functions perform a calculation on a set of values and return a single value. Examples include COUNT, SUM, AVG (average), MIN (minimum), and MAX (maximum).
13. Difference between DELETE and TRUNCATE?
- Answer: DELETE removes rows one at a time and logs each delete, while TRUNCATE removes all rows in a table without logging individual row deletions. TRUNCATE is faster but cannot be rolled back.
14. What is a UNION in SQL?
- Answer: UNION is an operator used to combine the result sets of two or more SELECT statements. It removes duplicate rows between the various SELECT statements.
15. What is a cursor in SQL?
- Answer: A cursor is a database object used to retrieve, manipulate, and navigate through a result set one row at a time.
16. What is trigger in SQL?
- Answer: A trigger is a set of SQL statements that automatically execute or "trigger" when certain events occur in a database, such as INSERT, UPDATE, or DELETE.
17. Difference between clustered and non-clustered indexes?
- Answer: A clustered index determines the physical order of data in a table and can only be one per table. A non-clustered index, on the other hand, creates a logical order and can be many per table.
18. Explain the term ACID.
- Answer: ACID stands for Atomicity, Consistency, Isolation, and Durability.
Hope it helps :)
1. What is SQL?
- Answer: SQL (Structured Query Language) is a standard programming language specifically designed for managing and manipulating relational databases.
2. What are the different types of SQL statements?
- Answer: SQL statements can be classified into DDL (Data Definition Language), DML (Data Manipulation Language), DCL (Data Control Language), and TCL (Transaction Control Language).
3. What is a primary key?
- Answer: A primary key is a field (or combination of fields) in a table that uniquely identifies each row/record in that table.
4. What is a foreign key?
- Answer: A foreign key is a field (or collection of fields) in one table that uniquely identifies a row of another table or the same table. It establishes a link between the data in two tables.
5. What are joins? Explain different types of joins.
- Answer: A join is an SQL operation for combining records from two or more tables. Types of joins include INNER JOIN, LEFT JOIN (or LEFT OUTER JOIN), RIGHT JOIN (or RIGHT OUTER JOIN), and FULL JOIN (or FULL OUTER JOIN).
6. What is normalization?
- Answer: Normalization is the process of organizing data to reduce redundancy and improve data integrity. This typically involves dividing a database into two or more tables and defining relationships between them.
7. What is denormalization?
- Answer: Denormalization is the process of combining normalized tables into fewer tables to improve database read performance, sometimes at the expense of write performance and data integrity.
8. What is stored procedure?
- Answer: A stored procedure is a prepared SQL code that you can save and reuse. So, if you have an SQL query that you write frequently, you can save it as a stored procedure and then call it to execute it.
9. What is an index?
- Answer: An index is a database object that improves the speed of data retrieval operations on a table at the cost of additional storage and maintenance overhead.
10. What is a view in SQL?
- Answer: A view is a virtual table based on the result set of an SQL query. It contains rows and columns, just like a real table, but does not physically store the data.
11. What is a subquery?
- Answer: A subquery is an SQL query nested inside a larger query. It is used to return data that will be used in the main query as a condition to further restrict the data to be retrieved.
12. What are aggregate functions in SQL?
- Answer: Aggregate functions perform a calculation on a set of values and return a single value. Examples include COUNT, SUM, AVG (average), MIN (minimum), and MAX (maximum).
13. Difference between DELETE and TRUNCATE?
- Answer: DELETE removes rows one at a time and logs each delete, while TRUNCATE removes all rows in a table without logging individual row deletions. TRUNCATE is faster but cannot be rolled back.
14. What is a UNION in SQL?
- Answer: UNION is an operator used to combine the result sets of two or more SELECT statements. It removes duplicate rows between the various SELECT statements.
15. What is a cursor in SQL?
- Answer: A cursor is a database object used to retrieve, manipulate, and navigate through a result set one row at a time.
16. What is trigger in SQL?
- Answer: A trigger is a set of SQL statements that automatically execute or "trigger" when certain events occur in a database, such as INSERT, UPDATE, or DELETE.
17. Difference between clustered and non-clustered indexes?
- Answer: A clustered index determines the physical order of data in a table and can only be one per table. A non-clustered index, on the other hand, creates a logical order and can be many per table.
18. Explain the term ACID.
- Answer: ACID stands for Atomicity, Consistency, Isolation, and Durability.
Hope it helps :)
❤21👍2
SQL best practices:
✔ Use EXISTS in place of IN wherever possible
✔ Use table aliases with columns when you are joining multiple tables
✔ Use GROUP BY instead of DISTINCT.
✔ Add useful comments wherever you write complex logic and avoid too many comments.
✔ Use joins instead of subqueries when possible for better performance.
✔ Use WHERE instead of HAVING to define filters on non-aggregate fields
✔ Avoid wildcards at beginning of predicates (something like '%abc' will cause full table scan to get the results)
✔ Considering cardinality within GROUP BY can make it faster (try to consider unique column first in group by list)
✔ Write SQL keywords in capital letters.
✔ Never use select *, always mention list of columns in select clause.
✔ Create CTEs instead of multiple sub queries , it will make your query easy to read.
✔ Join tables using JOIN keywords instead of writing join condition in where clause for better readability.
✔ Never use order by in sub queries , It will unnecessary increase runtime.
✔ If you know there are no duplicates in 2 tables, use UNION ALL instead of UNION for better performance
✔ Always start WHERE clause with 1 = 1.This has the advantage of easily commenting out conditions during debugging a query.
✔ Taking care of NULL values before using equality or comparisons operators. Applying window functions. Filtering the query before joining and having clause.
✔ Make sure the JOIN conditions among two table Join are either keys or Indexed attribute.
Hope it helps :)
✔ Use EXISTS in place of IN wherever possible
✔ Use table aliases with columns when you are joining multiple tables
✔ Use GROUP BY instead of DISTINCT.
✔ Add useful comments wherever you write complex logic and avoid too many comments.
✔ Use joins instead of subqueries when possible for better performance.
✔ Use WHERE instead of HAVING to define filters on non-aggregate fields
✔ Avoid wildcards at beginning of predicates (something like '%abc' will cause full table scan to get the results)
✔ Considering cardinality within GROUP BY can make it faster (try to consider unique column first in group by list)
✔ Write SQL keywords in capital letters.
✔ Never use select *, always mention list of columns in select clause.
✔ Create CTEs instead of multiple sub queries , it will make your query easy to read.
✔ Join tables using JOIN keywords instead of writing join condition in where clause for better readability.
✔ Never use order by in sub queries , It will unnecessary increase runtime.
✔ If you know there are no duplicates in 2 tables, use UNION ALL instead of UNION for better performance
✔ Always start WHERE clause with 1 = 1.This has the advantage of easily commenting out conditions during debugging a query.
✔ Taking care of NULL values before using equality or comparisons operators. Applying window functions. Filtering the query before joining and having clause.
✔ Make sure the JOIN conditions among two table Join are either keys or Indexed attribute.
Hope it helps :)
❤18🔥1
📚 Foundations
- [ ] Excel / Google Sheets
- [ ] Basic Statistics & Probability
- [ ] Python (or R) for Data Analysis
- [ ] SQL for Data Querying
📊 Data Handling & Manipulation
- [ ] NumPy & Pandas
- [ ] Data Cleaning & Wrangling
- [ ] Handling Missing Data & Outliers
- [ ] Merging, Grouping & Aggregating Data
📈 Data Visualization
- [ ] Matplotlib & Seaborn (Python)
- [ ] Power BI / Tableau
- [ ] Creating Dashboards
- [ ] Storytelling with Data
🧠 Analytical Thinking
- [ ] Exploratory Data Analysis (EDA)
- [ ] Trend & Pattern Detection
- [ ] Correlation & Causation
- [ ] A/B Testing & Hypothesis Testing
🛠️ Tools & Platforms
- [ ] Jupyter Notebook / Google Colab
- [ ] SQL IDEs (e.g., MySQL Workbench)
- [ ] Git & GitHub
- [ ] Google Data Studio / Looker
📂 Projects to Build
- [ ] Sales Data Dashboard
- [ ] Customer Segmentation
- [ ] Marketing Campaign Analysis
- [ ] Product Usage Trend Report
- [ ] HR Attrition Analysis
🚀 Practice & Growth
- [ ] Kaggle Notebooks & Datasets
- [ ] DataCamp / LeetCode (SQL)
- [ ] Real-world Data Challenges
- [ ] Create a Portfolio on GitHub
Tap ❤️ for more!
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❤19👍2🔥1🥰1👏1
🎯 The Only SQL You Actually Need For Your First Data Analytics Job
🚫 Avoid the Learning Trap:
Watching 100+ tutorials but no hands-on practice.
✅ Reality:
75% of real SQL work boils down to these essentials:
1️⃣ SELECT, FROM, WHERE
⦁ Pick columns, tables, and filter rows
2️⃣ JOINs
⦁ Combine related tables (INNER JOIN, LEFT JOIN)
3️⃣ GROUP BY
⦁ Aggregate data by groups
4️⃣ ORDER BY
⦁ Sort results ascending or descending
5️⃣ Aggregation Functions
⦁ COUNT(), SUM(), AVG(), MIN(), MAX()
6️⃣ ROW_NUMBER()
⦁ Rank rows within partitions
💡 Final Tip:
Master these basics well, practice hands-on, and build up confidence!
Double Tap ♥️ For More
🚫 Avoid the Learning Trap:
Watching 100+ tutorials but no hands-on practice.
✅ Reality:
75% of real SQL work boils down to these essentials:
1️⃣ SELECT, FROM, WHERE
⦁ Pick columns, tables, and filter rows
SELECT name, age FROM customers WHERE age > 30;
2️⃣ JOINs
⦁ Combine related tables (INNER JOIN, LEFT JOIN)
SELECT o.id, c.name FROM orders o JOIN customers c ON o.customer_id = c.id;
3️⃣ GROUP BY
⦁ Aggregate data by groups
SELECT country, COUNT(*) FROM users GROUP BY country;
4️⃣ ORDER BY
⦁ Sort results ascending or descending
SELECT name, score FROM students ORDER BY score DESC;
5️⃣ Aggregation Functions
⦁ COUNT(), SUM(), AVG(), MIN(), MAX()
SELECT AVG(salary) FROM employees;
6️⃣ ROW_NUMBER()
⦁ Rank rows within partitions
SELECT name,
ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS rank
FROM employees;
💡 Final Tip:
Master these basics well, practice hands-on, and build up confidence!
Double Tap ♥️ For More
❤24👏2🥰1
✅ Power BI Scenario-Based Questions 📊⚡
🧮 Scenario 1: Measure vs. Calculated Column
Question: You need to create a new column to categorize sales as “High” or “Low” based on a threshold. Would you use a calculated column or a measure? Why?
Answer: I would use a calculated column because the categorization is row-level logic and needs to be stored in the data model for filtering and visual grouping. Measures are better suited for aggregations and calculations on summarized data.
🔁 Scenario 2: Handling Data from Multiple Sources
Question: How would you combine data from Excel, SQL Server, and a web API into a single Power BI report?
Answer: I’d use Power Query to connect to each data source and perform necessary transformations. Then, I’d establish relationships in the data model using the Manage Relationships pane. I’d ensure consistent data types and structure before building visuals that integrate insights across all sources.
🔐 Scenario 3: Row-Level Security
Question: How would you ensure that different departments only see data relevant to them in a Power BI report?
×Answer:× I’d implement ×Row-Level Security (RLS)× by defining roles in Power BI Desktop using DAX filters (e.g., [Department] = USERNAME()), then publish the report to the Power BI Service and assign users to the appropriate roles.
📉 Scenario 4: Reducing Dataset Size
Question: Your Power BI model is too large and hitting performance limits. What would you do?
Answer: I’d remove unused columns, reduce granularity where possible, and switch to star schema modeling. I might also aggregate large tables, optimize DAX, and disable auto date/time features to save space.
📌 Tap ❤️ for more!
🧮 Scenario 1: Measure vs. Calculated Column
Question: You need to create a new column to categorize sales as “High” or “Low” based on a threshold. Would you use a calculated column or a measure? Why?
Answer: I would use a calculated column because the categorization is row-level logic and needs to be stored in the data model for filtering and visual grouping. Measures are better suited for aggregations and calculations on summarized data.
🔁 Scenario 2: Handling Data from Multiple Sources
Question: How would you combine data from Excel, SQL Server, and a web API into a single Power BI report?
Answer: I’d use Power Query to connect to each data source and perform necessary transformations. Then, I’d establish relationships in the data model using the Manage Relationships pane. I’d ensure consistent data types and structure before building visuals that integrate insights across all sources.
🔐 Scenario 3: Row-Level Security
Question: How would you ensure that different departments only see data relevant to them in a Power BI report?
×Answer:× I’d implement ×Row-Level Security (RLS)× by defining roles in Power BI Desktop using DAX filters (e.g., [Department] = USERNAME()), then publish the report to the Power BI Service and assign users to the appropriate roles.
📉 Scenario 4: Reducing Dataset Size
Question: Your Power BI model is too large and hitting performance limits. What would you do?
Answer: I’d remove unused columns, reduce granularity where possible, and switch to star schema modeling. I might also aggregate large tables, optimize DAX, and disable auto date/time features to save space.
📌 Tap ❤️ for more!
❤34👍5🤩3🥰1👏1
✅ Data Analysts in Your 20s – Avoid This Career Trap 🚫📊
Don't fall for the passive learning illusion!
🎯 The Trap? → Passive Learning
It feels like you're making progress… but you’re not.
🔍 Example:
You spend hours:
👉 Watching SQL tutorials on YouTube
👉 Saving Excel shortcut threads
👉 Browsing dashboards on LinkedIn
👉 Enrolling in 3 new courses
At day’s end — you feel productive.
But 2 weeks later?
❌ No SQL written from scratch
❌ No real dashboard built
❌ No insights extracted from raw data
That’s passive learning — absorbing, but not applying.
It creates false confidence and delays actual growth.
🛠️ How to Fix It:
1️⃣ Learn by doing: Pick real datasets (Kaggle, public APIs)
2️⃣ Build projects: Sales dashboard, churn analysis, etc.
3️⃣ Write insights: Explain findings like you're presenting to a manager
4️⃣ Get feedback: Share work on GitHub or LinkedIn
5️⃣ Fail fast: Debug bad queries, wrong charts, messy data
📌 In your 20s, focus on building data instincts — not collecting certificates.
Stop binge-learning.
Start project-building.
Start explaining insights.
That’s how analysts grow fast in the real world. 📈
💬 Tap ❤️ if you agree!
Don't fall for the passive learning illusion!
🎯 The Trap? → Passive Learning
It feels like you're making progress… but you’re not.
🔍 Example:
You spend hours:
👉 Watching SQL tutorials on YouTube
👉 Saving Excel shortcut threads
👉 Browsing dashboards on LinkedIn
👉 Enrolling in 3 new courses
At day’s end — you feel productive.
But 2 weeks later?
❌ No SQL written from scratch
❌ No real dashboard built
❌ No insights extracted from raw data
That’s passive learning — absorbing, but not applying.
It creates false confidence and delays actual growth.
🛠️ How to Fix It:
1️⃣ Learn by doing: Pick real datasets (Kaggle, public APIs)
2️⃣ Build projects: Sales dashboard, churn analysis, etc.
3️⃣ Write insights: Explain findings like you're presenting to a manager
4️⃣ Get feedback: Share work on GitHub or LinkedIn
5️⃣ Fail fast: Debug bad queries, wrong charts, messy data
📌 In your 20s, focus on building data instincts — not collecting certificates.
Stop binge-learning.
Start project-building.
Start explaining insights.
That’s how analysts grow fast in the real world. 📈
💬 Tap ❤️ if you agree!
❤40👍1
You’re not a failure as a data analyst if:
• It takes you more than two months to land a job (remove the time expectation!)
• Complex concepts don’t immediately sink in
• You use Google/YouTube daily on the job (this is a sign you’re successful, actually)
• You don’t make as much money as others in the field
• You don’t code in 12 different languages (SQL is all you need. Add Python later if you want.)
• It takes you more than two months to land a job (remove the time expectation!)
• Complex concepts don’t immediately sink in
• You use Google/YouTube daily on the job (this is a sign you’re successful, actually)
• You don’t make as much money as others in the field
• You don’t code in 12 different languages (SQL is all you need. Add Python later if you want.)
❤8
Interviewer: Show me top 3 highest-paid employees per department.
Me: Sure, let’s use ROW_NUMBER() for this!
✅ I used a window function to rank employees by salary within each department.
Then filtered the top 3 using a subquery.
🧠 Key Concepts:
- ROW_NUMBER()
- PARTITION BY → resets ranking per department
- ORDER BY → sorts by salary (highest first)
📝 Real-World Tip:
These kinds of queries help answer questions like:
– Who are the top earners by team?
– Which stores have the best sales staff?
– What are the top-performing products per category?
💬 Tap ❤️ for more!
Me: Sure, let’s use ROW_NUMBER() for this!
SELECT name, salary, department
FROM (
SELECT name, salary, department,
ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS rn
FROM employees
) sub
WHERE rn <= 3;
✅ I used a window function to rank employees by salary within each department.
Then filtered the top 3 using a subquery.
🧠 Key Concepts:
- ROW_NUMBER()
- PARTITION BY → resets ranking per department
- ORDER BY → sorts by salary (highest first)
📝 Real-World Tip:
These kinds of queries help answer questions like:
– Who are the top earners by team?
– Which stores have the best sales staff?
– What are the top-performing products per category?
💬 Tap ❤️ for more!
❤17
✅ Data Analytics A–Z 📊🚀
🅰️ A – Analytics
Understanding, interpreting, and presenting data-driven insights.
🅱️ B – BI Tools (Power BI, Tableau)
For dashboards and data visualization.
©️ C – Cleaning Data
Remove nulls, duplicates, fix types, handle outliers.
🅳 D – Data Wrangling
Transform raw data into a usable format.
🅴 E – EDA (Exploratory Data Analysis)
Analyze distributions, trends, and patterns.
🅵 F – Feature Engineering
Create new variables from existing data to enhance analysis or modeling.
🅶 G – Graphs & Charts
Visuals like histograms, scatter plots, bar charts to make sense of data.
🅷 H – Hypothesis Testing
A/B testing, t-tests, chi-square for validating assumptions.
🅸 I – Insights
Meaningful takeaways that influence decisions.
🅹 J – Joins
Combine data from multiple tables (SQL/Pandas).
🅺 K – KPIs
Key metrics tracked over time to evaluate success.
🅻 L – Linear Regression
A basic predictive model used frequently in analytics.
🅼 M – Metrics
Quantifiable measures of performance.
🅽 N – Normalization
Scale features for consistency or comparison.
🅾️ O – Outlier Detection
Spot and handle anomalies that can skew results.
🅿️ P – Python
Go-to programming language for data manipulation and analysis.
🆀 Q – Queries (SQL)
Use SQL to retrieve and analyze structured data.
🆁 R – Reports
Present insights via dashboards, PPTs, or tools.
🆂 S – SQL
Fundamental querying language for relational databases.
🆃 T – Tableau
Popular BI tool for data visualization.
🆄 U – Univariate Analysis
Analyzing a single variable's distribution or properties.
🆅 V – Visualization
Transform data into understandable visuals.
🆆 W – Web Scraping
Extract public data from websites using tools like BeautifulSoup.
🆇 X – XGBoost (Advanced)
A powerful algorithm used in machine learning-based analytics.
🆈 Y – Year-over-Year (YoY)
Common time-based metric comparison.
🆉 Z – Zero-based Analysis
Analyzing from a baseline or zero point to measure true change.
💬 Tap ❤️ for more!
🅰️ A – Analytics
Understanding, interpreting, and presenting data-driven insights.
🅱️ B – BI Tools (Power BI, Tableau)
For dashboards and data visualization.
©️ C – Cleaning Data
Remove nulls, duplicates, fix types, handle outliers.
🅳 D – Data Wrangling
Transform raw data into a usable format.
🅴 E – EDA (Exploratory Data Analysis)
Analyze distributions, trends, and patterns.
🅵 F – Feature Engineering
Create new variables from existing data to enhance analysis or modeling.
🅶 G – Graphs & Charts
Visuals like histograms, scatter plots, bar charts to make sense of data.
🅷 H – Hypothesis Testing
A/B testing, t-tests, chi-square for validating assumptions.
🅸 I – Insights
Meaningful takeaways that influence decisions.
🅹 J – Joins
Combine data from multiple tables (SQL/Pandas).
🅺 K – KPIs
Key metrics tracked over time to evaluate success.
🅻 L – Linear Regression
A basic predictive model used frequently in analytics.
🅼 M – Metrics
Quantifiable measures of performance.
🅽 N – Normalization
Scale features for consistency or comparison.
🅾️ O – Outlier Detection
Spot and handle anomalies that can skew results.
🅿️ P – Python
Go-to programming language for data manipulation and analysis.
🆀 Q – Queries (SQL)
Use SQL to retrieve and analyze structured data.
🆁 R – Reports
Present insights via dashboards, PPTs, or tools.
🆂 S – SQL
Fundamental querying language for relational databases.
🆃 T – Tableau
Popular BI tool for data visualization.
🆄 U – Univariate Analysis
Analyzing a single variable's distribution or properties.
🆅 V – Visualization
Transform data into understandable visuals.
🆆 W – Web Scraping
Extract public data from websites using tools like BeautifulSoup.
🆇 X – XGBoost (Advanced)
A powerful algorithm used in machine learning-based analytics.
🆈 Y – Year-over-Year (YoY)
Common time-based metric comparison.
🆉 Z – Zero-based Analysis
Analyzing from a baseline or zero point to measure true change.
💬 Tap ❤️ for more!
❤24👏1
The key to starting your data analysis career:
❌It's not your education
❌It's not your experience
It's how you apply these principles:
1. Learn the job through "doing"
2. Build a portfolio
3. Make yourself known
No one starts an expert, but everyone can become one.
If you're looking for a career in data analysis, start by:
⟶ Watching videos
⟶ Reading experts advice
⟶ Doing internships
⟶ Building a portfolio
⟶ Learning from seniors
You'll be amazed at how fast you'll learn and how quickly you'll become an expert.
So, start today and let the data analysis career begin
React ❤️ for more helpful tips
❌It's not your education
❌It's not your experience
It's how you apply these principles:
1. Learn the job through "doing"
2. Build a portfolio
3. Make yourself known
No one starts an expert, but everyone can become one.
If you're looking for a career in data analysis, start by:
⟶ Watching videos
⟶ Reading experts advice
⟶ Doing internships
⟶ Building a portfolio
⟶ Learning from seniors
You'll be amazed at how fast you'll learn and how quickly you'll become an expert.
So, start today and let the data analysis career begin
React ❤️ for more helpful tips
❤29👍4🔥1
📊 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿: How do you find the Third Highest Salary in SQL?
🙋♂️ 𝗠𝗲: Just tweak the offset:
🧠 Logic Breakdown:
-
-
-
✅ Use Case: Top 3 performers, tiered bonus calculations
💡 Pro Tip: For ties, use
💬 Tap ❤️ for more!
🙋♂️ 𝗠𝗲: Just tweak the offset:
SELECT DISTINCT salary
FROM employees
ORDER BY salary DESC
LIMIT 1 OFFSET 2;
🧠 Logic Breakdown:
-
OFFSET 2 skips the top 2 salaries -
LIMIT 1 fetches the 3rd highest -
DISTINCT ensures no duplicates interfere✅ Use Case: Top 3 performers, tiered bonus calculations
💡 Pro Tip: For ties, use
DENSE_RANK() or ROW_NUMBER() in a subquery.💬 Tap ❤️ for more!
❤7👍2
📊 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿: How do you find Employees Earning More Than the Average Salary in SQL?
🙋♂️ 𝗠𝗲: Use a subquery to calculate average salary first:
🧠 Logic Breakdown:
- Inner query gets overall average salary
- Outer query filters employees earning more than that
✅ Use Case: Performance reviews, salary benchmarking, raise eligibility
💡 Pro Tip: Use
💬 Tap ❤️ for more!
🙋♂️ 𝗠𝗲: Use a subquery to calculate average salary first:
SELECT *
FROM employees
WHERE salary > (
SELECT AVG(salary)
FROM employees
);
🧠 Logic Breakdown:
- Inner query gets overall average salary
- Outer query filters employees earning more than that
✅ Use Case: Performance reviews, salary benchmarking, raise eligibility
💡 Pro Tip: Use
ROUND(AVG(salary), 2) if you want clean decimal output.💬 Tap ❤️ for more!
❤8