DAHI

Question 1

– Uses statistical treatment (mathematical methods) to analyze data. Helps identify main characteristics, patterns, and distribution before deeper analysis.

Answer 1

Exploratory Data Analysis

Question 2

– Reshaping cleaned data and reprocessing missing values to make the dataset ready for modeling or assessment.

Answer 2

Data Preparation for Modelling/Assessment

Question 3

– Testing different models and strategies to solve the business problem and achieve objectives.

Answer 3

Modeling

Question 4

– Setting up a validation scheme while the data product is working to monitor performance and ensure reliable results.

Answer 4

Implementation

Question 5

– The practice of analyzing large datasets to discover useful patterns and hidden relationships. Uses machine learning, statistics, and AI for tasks like marketing, fraud detection, and scientific discovery. Also known as KDD (Knowledge Discovery in Data).

Answer 5

Data Mining

Question 6

– Process with stages: Business Understanding, Gathering of Data, Data Preparation, Conceptualization, and Evaluation of Model.

Answer 6

Traditional Data Mining Life Cycle

Question 7

– A five-stage process designed to guide data mining projects for developing predictive models:

Answer 7

SEMMA Methodology

Question 8

– selecting the dataset for modeling

Answer 8

Sample

Question 9

– understanding the data by discovering both expected and unexpected relationships, including abnormalities, often with visualization

Answer 9

Explore

Question 10

– selecting, creating, and transforming variables in preparation for modeling

Answer 10

Modify

Question 11

Building the strategy to solve the problem

Answer 11

Model

Question 12

– evaluating the modeling results to test reliability and usefulness

Answer 12

Assess

Question 13

What are the five stages of the SEMMA methodology in data mining?

Answer 13

Sample, Explore, Modify, Model, Assess

Question 14

–refers to a collection of data that is extremely large in volume grows exponentially over time, and is too complex for traditional data management tools to store or process efficiently. It is still data, but much larger and harder to handle.

Answer 14

Big Data

Question 15

Characteristics of Big Data

Answer 15

Volume, Velocity, Variety, Veracity

Question 16

– the size of data is massive

Answer 16

Volume

Question 17

– the speed of data generation and processing

Answer 17

Velocity

Question 18

– data comes in multiple forms (Structured, Unstructured, Semi- structured)

Answer 18

Variety

Question 19

What form of data in Big Data is available in spreadsheets and databases?

Answer 19

Structured

Question 20

What form of data in Big Data includes text, images, audio, and video?

Answer 20

Unstructured

Question 21

What form of data in Big Data is a combination of structured and unstructured?

Answer 21

Semi-structured

Question 22

– ensures completeness and quality of information

Answer 22

Veracity

Question 23

– identifying the problem and determining the main cause

Answer 23

Business Problem Definition

Question 24

– analyzing what other companies have done in similar cases

Answer 24

Research

Question 25

– checking if staff is capable of completing the project

Answer 25

Human Resource Assessment

Question 26

– key step; data is collected according to policies and guidelines

Answer 26

Data Acquisition

Question 27

– reviewing, cleaning, filtering, and transforming data for analysis, visualization, or modeling

Answer 27

Data Munging

Question 28

saving the prepared data in databases for further use

Answer 28

Data Storage

Question 29

What are the steps in the Big Data Cycle?

Answer 29

Business Problem Definition Research Human Resource Assessment Data Acquisition Data Munging Data Storage

Question 30

– A process of extracting useful information or knowledge from previously collected data.

Answer 30

Data Mining

Question 31

Types of Database

Answer 31

Relational Database, Data Warehouse, Transactional Database, Multimedia/Streaming Database, Text Database

Question 32

– uses tables, for example Microsoft SQL Server

Answer 32

Relational Database

Question 33

– centralized data storage, for example Oracle

Answer 33

Data Warehouse

Question 34

– records of purchases, deposits, and withdrawals

Answer 34

Transactional Database

Question 35

– stores text, images, audio, video

Answer 35

Multimedia/Streaming Database

Question 36

– collection of documents, articles, and emails

Answer 36

Text Database

Question 37

What are the steps in the Data Mining Implementation Process?

Answer 37

Business Understanding, Data Understanding, Data Preparation, Data Transformation, Evaluation, Deployment

Question 38

– defining business and client objectives

Answer 38

Business Understanding

Question 39

– collecting data from multiple databases and using metadata to minimize errors

Answer 39

Data Understanding

Question 40

– cleansing, selecting, and integrating data into a consistent format

Answer 40

Data Preparation

Question 41

– changing data into usable format through: Smoothing, Aggregation, Generalization, Normalization, Attribute Constructive

Answer 41

Data Transformation

Question 42

What method of data transformation removes noise or irregularities from data to make it clearer?

Answer 42

Smoothing

Question 43

What method of data transformation summarizes data by combining it into more compact forms (e.g., totals, averages)?

Answer 43

Aggregation

Question 44

What method of data transformation replaces detailed, low-level data with higher-level concepts for easier analysis?

Answer 44

Generalization

Question 45

What method of data transformation adjusts values to a common scale, such as converting numbers to a range like -2.0 to 2.0?

Answer 45

Normalization

Question 46

What method of data transformation creates new attributes from the existing data that may be more useful for analysis?

Answer 46

Attribute Constructive

Question 47

– testing if identified patterns match business objectives

Answer 47

Evaluation

Question 48

– applying findings in business operations and creating reports

Answer 48

Deployment

Question 49

What are the different Data Mining Techniques?

Answer 49

Classification, Clustering, Regression, Association Rules, Outlier Detection, Sequential Patterns, Prediction

Question 50

– classifying data into different groups or categories

Answer 50

Classification

Question 51

– grouping data that are similar and identifying differences

Answer 51

Clustering

Question 52

– determining relationships among variables. Example: sales forecasting based on seasonality or economic indicators

Answer 52

Regression

Question 53

– finding associations between two or more items to predict customer behavior

Answer 53

Association Rules

Question 54

– identifying unusual or anomalies data that do not match patterns

Answer 54

Outer Detection

Question 55

– discovering repeated trends in transaction data over time

Answer 55

Sequential Patterns

Question 56

– forecasting future customer behavior by analyzing past events in sequence

Answer 56

Prediction

Question 57

– Methods used with a specific reason or purpose in mind.

Answer 57

Techniques for Data Analytics/Analysis

Question 58

– discovering patterns in large datasets using statistics, databases, or mining tools; involves extracting and interpreting meaning from text

Answer 58

Text Analytics

Question 59

– answering the question “Why did it happen?” by finding causes from statistical insights

Answer 59

Diagnostic Analytic

Question 60

– answering “What is likely to happen?” by using historical data to predict future outcomes

Answer 60

Predictive Analytic

Question 61

– recommending the best actions to take in solving current problems

Answer 61

Prescriptive Analytic

Question 62

5. – collecting, organizing, exploring, and interpreting data systematically

Answer 62

Statistical Analysis

Question 63

– summarizing data into a complete overview, whether sample or whole population

Answer 63

Descriptive Analytic

Question 64

What are the Statistical Analysis Tools?

Answer 64

Mean and Deviation Percentage and Frequency Inferential tools

Question 65

What statistical tool is used to analyze continuous data by measuring central tendency and spread?

Answer 65

Mean and Deviation

Question 66

What statistical tool is used to analyze categorical data by showing proportions and counts?

Answer 66

Percentage and Frequency

Question 67

What statistical tool is used to test for significant differences and relationships between variables?

Answer 67

Inferential tools

Question 68

What are the methods under Inferential Analysis?

Answer 68

ANOVA and ANCOVA, T-test, Pearson-r

Question 69

What inferential analysis tool is used to test for differences among three (3) or more variables?

Answer 69

ANOVA and ANCOVA

Question 70

What inferential analysis tool is used to compare the means of only two (2) variables or groups?

Answer 70

T-test

Question 71

What inferential analysis tool measures if there is a significant correlation or linear relationship between two variables?

Answer 71

Pearson-r