1. What is Data Science?

Data Science is the process of collecting, cleaning, analyzing, and interpreting data to discover meaningful insights. It combines programming, statistics, machine learning, and visualization to solve real-world business problems.

2. Who is a Data Scientist?

A Data Scientist is a professional who gathers, analyzes, and interprets large datasets to help organizations make data-driven decisions using statistical methods, machine learning, and programming.

3. What is Artificial Intelligence (AI)?

Artificial Intelligence (AI) is the branch of computer science that enables machines to simulate human intelligence, such as learning, reasoning, decision-making, and problem-solving.

4. What is Machine Learning?

Machine Learning (ML) is a subset of Artificial Intelligence that allows computers to learn from historical data and improve their performance without being explicitly programmed.

5. What is Deep Learning?

Deep Learning is an advanced branch of Machine Learning that uses artificial neural networks with multiple layers to solve complex tasks such as image recognition, speech processing, and natural language understanding.

6. Differentiate AI, Machine Learning, and Deep Learning.

• Artificial Intelligence (AI): Enables machines to mimic human intelligence.
• Machine Learning (ML): A subset of AI where systems learn from data.
• Deep Learning (DL): A specialized branch of ML that uses deep neural networks for advanced predictions.

7. What are the Prerequisites for Learning Data Science?

A strong foundation in the following areas is recommended:

• Python Programming
• Mathematics & Statistics
• SQL & Databases
• Data Analysis
• Machine Learning Basics
• Data Visualization

8. Why is Python Preferred for Data Science?

Python is the most popular language for Data Science because it is easy to learn, has a simple syntax, and offers powerful libraries such as NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, and PyTorch.

9. What is NumPy?

NumPy is a Python library used for numerical computing. It provides high-performance multidimensional arrays, mathematical operations, and matrix manipulation.

10. What is Pandas?

Pandas is a Python library used for data analysis and manipulation. It provides powerful data structures like Series and DataFrames to clean, transform, and analyze datasets efficiently.

11. What is Data Preprocessing?

Data preprocessing is the process of preparing raw data before applying machine learning algorithms. It includes:

• Handling missing values
• Removing duplicates
• Feature scaling
• Encoding categorical variables
• Data cleaning

12. What is Data Visualization?

Data Visualization is the graphical representation of data using charts and graphs, making it easier to understand trends, patterns, and relationships.

Popular visualization tools include:

• Matplotlib
• Seaborn
• Power BI
• Tableau

13. What is SQL and Why is it Important in Data Science?

SQL (Structured Query Language) is used to store, retrieve, update, and manage data in relational databases. Data Scientists use SQL to extract and analyze large datasets efficiently.

14. What are the Types of Machine Learning?

There are three major types:

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning