Introduction to Data Science Part 1

Question 1

What is Data Science?

Answer 1

A process of using data to understand different things and uncover insights using scientific tools like programming and statistics.

Question 2

What are the key objectives of Data Science?

Answer 2

Extract knowledge from data, uncover insights, and make informed decisions.

Question 3

What are common terms associated with Data Science?

Answer 3

Big Data, Machine Learning, Artificial Intelligence, Data Mining, Predictive Analytics.

Question 4

What are the types of data in Data Science?

Answer 4

Qualitative (descriptive data) and Quantitative (measurable values).

Question 5

What are the three main data formats?

Answer 5

Structured, Unstructured, and Semi-structured data.

Question 6

What are examples of Structured Data?

Answer 6

Relational databases, spreadsheets, and data tables.

Question 7

What are examples of Unstructured Data?

Answer 7

Images, videos, social media posts, and PDFs.

Question 8

What are examples of Semi-structured Data?

Answer 8

JSON, XML, and HTML documents.

Question 9

What are the major sources of data?

Answer 9

Web data, financial transactions, online trading, social networks, business records.

Question 10

What is Big Data?

Answer 10

Data that is expensive to manage and difficult to extract value from.

Question 11

What are the 5 V’s of Big Data?

Answer 11

Volume, Velocity, Variety, Veracity, and Value.

Question 12

What does ‘Volume’ refer to in Big Data?

Answer 12

The size of data being generated.

Question 13

What does ‘Velocity’ refer to in Big Data?

Answer 13

The speed at which data is processed and analyzed.

Question 14

What does ‘Variety’ refer to in Big Data?

Answer 14

Different types of data sources and formats.

Question 15

What does ‘Veracity’ refer to in Big Data?

Answer 15

Data quality and reliability.

Question 16

What does ‘Value’ refer to in Big Data?

Answer 16

The potential business benefits derived from analyzing data.

Question 17

What is Machine Learning?

Answer 17

A field of AI that enables systems to learn and improve from experience without explicit programming.

Question 18

What are the three main types of Machine Learning?

Answer 18

Supervised Learning, Unsupervised Learning, and Reinforcement Learning.

Question 19

What is the goal of Supervised Learning?

Answer 19

To learn a mapping from inputs to outputs using labeled data.

Question 20

What is the goal of Unsupervised Learning?

Answer 20

To find patterns or structure in data without labeled responses.

Question 21

What is Reinforcement Learning?

Answer 21

A type of learning where an agent interacts with an environment to maximize cumulative reward.

Question 22

What is AI (Artificial Intelligence)?

Answer 22

The simulation of human intelligence processes by machines, including learning, reasoning, and self-correction.

Question 23

What is the difference between Data Science and Machine Learning?

Answer 23

Data Science produces insights, while Machine Learning produces predictions.

Question 24

What are the main application areas of Data Science?

Answer 24

Industrial processes, business, text data, image data, and medical data applications.

Question 25

What are some industrial applications of Data Science?

Answer 25

Fault prediction, preventive maintenance, demand forecasting, inventory management, price optimization.

Question 26

What are some business applications of Data Science?

Answer 26

Market trend analysis, churn analysis, credit risk modeling.

Question 27

What are some text data applications of Data Science?

Answer 27

Sentiment Analysis, Topic Modeling, Conversational AI.

Question 28

What are some image data applications of Data Science?

Answer 28

Computer Vision, Machine Vision.

Question 29

What are some medical applications of Data Science?

Answer 29

Disease diagnosis, patient data analysis, medical imaging analysis.

Question 30

What is the CRISP-DM process?

Answer 30

A standard for data mining with phases: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, Deployment.

Question 31

What are the six phases of CRISP-DM?

Answer 31

Business understanding, Data understanding, Data preparation, Modeling, Evaluation, Deployment.

Question 32

What is the TDSP (Team Data Science Process)?

Answer 32

A methodology developed by Microsoft for structuring data science projects.

Question 33

What are the key steps in a Data Science project?

Answer 33

Problem definition, Data Collection, Data Processing, Model Building, Model Evaluation, Deployment.

Question 34

What key questions does Data Science aim to answer?

Answer 34

What is the problem? What data is needed? Where does data come from? How should data be processed? How should models be evaluated?

Question 35

What are common ways to visualize data for insights?

Answer 35

Charts, graphs, heatmaps, scatter plots, histograms.

Question 36

What are key qualities of a good Data Scientist?

Answer 36

Inquisitive, knowledgeable, proficient in machine learning, statistics, and probability, skilled in coding, and strong in domain knowledge.

Question 37

What coding skills should a Data Scientist have?

Answer 37

Python, R, SQL, and tools for data processing like Pandas, NumPy, and Scikit-Learn.

Question 38

Why is domain knowledge important for a Data Scientist?

Answer 38

It helps in interpreting data correctly and making meaningful insights relevant to the industry.

Question 39

What are some emerging research topics in Data Science?

Answer 39

Big Data Modeling, AI Ethics, Fairness in Machine Learning, Explainable AI, Edge Computing.

Question 40

What are some challenges in Data Science?

Answer 40

Data privacy concerns, data bias, computational complexity, data storage and management.

Question 41

What is the impact of Data Science in healthcare?

Answer 41

Predicting diseases, personalizing treatments, improving patient care, optimizing hospital operations.