Data Science Interview Questions Part 4:

31. What is reinforcement learning?
A type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize cumulative rewards through trial and error.

32. What tools and libraries do you use?
Commonly used tools: Python, R, Jupyter Notebooks, SQL, Excel. Libraries: Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, Matplotlib, Seaborn.

33. How do you interpret model results for non-technical audiences?
Use simple language, visualize key insights (charts, dashboards), focus on business impact, avoid jargon, and use analogies or stories.

34. What is dimensionality reduction?
Techniques like PCA or t-SNE to reduce the number of features while preserving essential information, improving model efficiency and visualization.

35. Handling categorical variables in machine learning.
Use encoding methods like one-hot encoding, label encoding, target encoding depending on model requirements and feature cardinality.

36. What is exploratory data analysis (EDA)?
The process of summarizing main characteristics of data often using visual methods to understand patterns, spot anomalies, and test hypotheses.

37. Explain t-test and chi-square test.
⦁ t-test compares means between two groups to see if they are statistically different.
⦁ Chi-square test assesses relationships between categorical variables.

38. How do you ensure fairness and avoid bias in models?
Audit data for bias, use balanced training datasets, apply fairness-aware algorithms, monitor model outcomes, and include diverse perspectives in evaluation.

39. Describe a complex data problem you solved.
(Your personal story here, describing the problem, approach, tools used, and impact.)

40. How do you stay updated with new data science trends?
Follow blogs, research papers, online courses, attend webinars, participate in communities (Kaggle, Stack Overflow), and read newsletters.

Data science interview questions: https://t.me/datasciencefun/3668

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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 (or LEFT OUTER JOIN): Returns all records from the left table and 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 (or RIGHT OUTER JOIN): Returns all records from the right table and 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 (or FULL OUTER 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 and 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 count the number of records in a table?

SELECT COUNT(*) FROM table_name;

This query counts all the records in the specified table.

6. How do you calculate average, sum, minimum, and 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;


7. What is a subquery, and how do you use it?

Subquery: A query nested inside another query

SELECT * FROM table_name
WHERE column_name = (SELECT column_name FROM another_table WHERE condition);




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⦁ Use crisp bullet points, no long paragraphs.
⦁ Focus on relevant data science experience.

2. Strong Summary at the Top 
Example: 
“Aspiring Data Scientist with hands-on experience in Python, Pandas, and Machine Learning. Built 5+ real-world projects including house price prediction and sentiment analysis.”

3. Highlight Technical Skills 
Separate Skills section:
⦁ Languages: Python, SQL
⦁ Libraries: Pandas, NumPy, Matplotlib, Scikit-learn
⦁ Tools: Jupyter, VS Code, Git, Tableau
⦁ Concepts: EDA, Regression, Classification, Data Cleaning

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Each project: 2–3 bullet points
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⦁ “Cleaned & visualized 10K+ rows of sales data with Pandas & Seaborn.” 
  Include GitHub links.

5. Education & Certifications 
Include:
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⦁ Online certifications (Coursera, Kaggle, etc.)
⦁ Mention course projects or capstones

6. Quantify Everything 
Instead of “Analyzed data”, write: 
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