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Question 1

What is Data Mining?

Answer 1

The process of sorting through large data sets to identify patterns and relationships that can help solve business problems.

Involves methods at the intersection of machine learning, statistics, and database systems.

Aims to extract information from a data set and transform it into a comprehensible structure for further use.

Question 2

Data Analytics vs Data Mining

Answer 2

Data analytics is the process of interpreting data to find trends and patterns.
Data mining is the process of extracting valuable information from a large dataset.

Question 3

Machine Learning in Data Mining

Answer 3

Helps in identifying patterns, predicting outcomes, and extracting meaningful insights from large datasets, which are essential steps in the data mining process.

Question 4

Data Mining Process Pyramid

Answer 4

Data Sources
Data Preprocessing
Data Exploration
Data Mining
Data Presentation
Decision Making

Question 5

Data Preprocessing Tools and Methods

Answer 5

Sampling
Transformation
Cleaning
Feature Extraction

Question 6

Sampling

Answer 6

Selects a representative subset from a large population of data.

Question 7

Transformation

Answer 7

Manipulates raw data to produce a single input.

Question 8

Cleaning

Answer 8

Imputation: Synthesizes statistically relevant data for missing values.
Normalization: Organizes data for more efficient access.

Question 9

Feature Extraction

Answer 9

Pulls out a relevant feature subset that is significant in a particular context.

Question 10

Data Preprocessing Steps (PCR-TEV)

Answer 10

Data Profiling
Data Cleansing
Data Reduction
Data Transformation
Data Enrichment
Data Validation

Question 11

Data Mining Techniques

Answer 11

Classification
Regression
Clustering
Anomaly Detection
Time Series Analysis
Neural Networks
Decision Trees

Question 12

Classification

Answer 12

Categorizes data into predefined classes based on features or attributes.

Question 13

Regression

Answer 13

Predicts numeric or continuous values based on the relationship between input variables and a target variable.

Question 14

Clustering

Answer 14

Groups similar data instances together based on intrinsic characteristics.

Question 15

Anomaly Detection

Answer 15

Identifies rare or unusual data instances that deviate significantly from expected patterns.

Question 16

Time Series Analysis

Answer 16

Analyzes and predicts data points collected over time.

Question 17

Neural Networks

Answer 17

AI models inspired by the human brain, composed of interconnected nodes (neurons) and layers that learn from data.

Question 18

Decision Trees

Answer 18

Graphical models that represent decisions and their possible consequences.

Question 19

Data Mining Tools

Answer 19

Python
R
Rapid Miner
SQL
Knime
Weka

Question 20

CrISP-DM

Answer 20

Cross Industry Standard Process for Data Mining

Question 21

CrISP-DM

Answer 21

A process model with six phases that describes the data science life cycle.

Ensures that business goals remain at the center of the project.

Provides an iterative approach with frequent opportunities to evaluate progress.

Question 22

Phases of CrISP-DM

Answer 22

Business Understanding
Data Understanding
Data Preparation
Modelling
Entity-Relationship Data Model
Relational Data Model
Object-Oriented Data Model
Evaluation
Deployment

Question 23

Business Understanding

Answer 23

Understand project objectives and requirements from a business perspective.

Question 24

Data Understanding

Answer 24

Data Collection
Data Quality Problems
Initial Insights of Data

Question 25

Data Preparation

Answer 25

Covers all activities to construct the final dataset from the initial raw data.

Question 26

Modelling

Answer 26

Evaluates, selects, and applies appropriate modeling techniques. Hierarchical Data Model: Composed of links between records. Entity-Relationship Data Model: Widely adopted for relational databases. Relational Data Model: Data stored in columns and tables. Object-Oriented Data Model: Represents data and relationships in a single structure.

Question 27

Evaluation

Answer 27

Builds and chooses models based on selected loss functions, ensuring they generalize against unseen data, and cover all key business issues.

Question 28

Deployment

Answer 28

Deploys a code representation of the model into an operating system, including mechanisms to score or categorize new data.

Question 29

(GDPR)

Answer 29

General Data Protection Regulation

Question 30

Key Principles

Answer 30

Lawfulness, Fairness, and Transparency: Data must be processed legally, fairly, and transparently. Purpose Limitation: Data should be collected for a specific purpose and not used for anything else. Data Minimization: Organizations should only collect the data they actually need. Accuracy: Personal data must be accurate and kept up to date. Storage Limitation: Data should not be kept longer than necessary. Integrity and Confidentiality: Data must be kept secure and protected from unauthorized access. Accountability: Organizations must take responsibility for complying with GDPR.

Question 31

Key Rights of Individuals

Answer 31

Right to Access: Individuals can request a copy of their data. Right to Rectification: They can ask for corrections to inaccurate data. Right to Erasure (Right to be Forgotten): They can request data deletion. Right to Restrict Processing: They can limit how their data is used. Right to Data Portability: They can receive their data in a usable format. Right to Object: They can refuse certain types of data processing, like direct marketing. Rights Related to Automated Decision-Making: Individuals can challenge automated decisions affecting them.

Question 32

Why does GDPR Matter?

Answer 32

Strengthens consumer privacy rights. Ensures transparency and accountability in data handling. Influences global data protection laws, including in the U.S., UK, and other regions.