Week 1

Question 1

What are the types of data?

Answer 1

Structured Data, Semi-Structured Data, and Unstructured Data.

Question 2

What are the differences of data types in term of format?

Answer 2

Structured Data has predefined schema. Semi-Structured Data has some structure, often with tags. Unstructured Data has no fixed format.

Question 3

What are the differences of data types in term of analysis?

Answer 3

SD is easy. SSD is moderate. UD is difficult.

Question 4

What are the differences of data types in terms of tools?

Answer 4

In SD, sql, traditional databases. IN SSD, Specialized Tools like JSON parsers. In UD, Natural Language Processing, Computer Vision.

Question 5

What are the differences of data types in term of examples?

Answer 5

In SD, databases and spreadsheets. In SSD, JSON, HTML, and XML. In UD, text, images, and videos.

Question 6

What is ‘Structured Data’

Answer 6

organized in a predefined format with a fixed schema and is typically stored in rows and columns, similar to a spreadsheet or database table. Examples include customer information, spreadsheet sales data, and machine sensor readings.

Question 7

What is ‘Unstructured Data’

Answer 7

lacks a predefined structure or format. It’s often text-heavy or multimedia-based. Social media posts, emails, images, videos, and audio files are typical examples of unstructured data.

Question 8

What is ‘Semi-Structured Data’

Answer 8

has some structure but lacks the rigid format of structured data. It often includes tags or markers to indicate the meaning of different parts of the data. Examples of semi-structured data include JSON data, XML data, and HTML documents.

Question 9

What is Data?

Answer 9

the raw material that fuels insights and informed decision-making, originating from various data sources

Question 10

What is Data Source?

Answer 10

is where data is stored or generated, such as sensors, social media platforms, customer interactions, databases, and public records.

Question 11

What is ‘Data Collection’?

Answer 11

involves systematically gathering data from these diverse sources

Question 12

What are the two categories of Data Sources?

Answer 12

Primary Data Source and Secondary Data Source

Question 13

What is Primary Data Source?

Answer 13

is data collected firsthand by the researcher for a specific purpose or project. Data is collected from primary data sources through surveys, experiments, and observations.

Question 14

What is Secondary Data Source?

Answer 14

is data that has already been collected by someone else for another purpose but is being repurposed for a new analysis. Secondary data sources include public databases, published research, and third-party sources.

Question 15

What is Databases?

Answer 15

a database is an organized collection of data that allows for efficient storage, retrieval, and manipulation. It is designed for transactional processing and day-to-day operations like creating, reading, updating, and deleting data (CRUD). Examples of databases are Microsoft SQL Server, MySQL, and MongoDB.

Question 16

What is Data Warehouse?

Answer 16

a data warehouse is a large, centralized repository of data that aggregates information from various sources. It’s designed for analytical processing, historical data storage, and decision-making.

Question 17

What is ‘Knowledge Discovery in Databases(KDD)’

Answer 17

is a method that offers a structured framework for extracting valuable insights from data.

Question 18

What are the KDD Steps?

Answer 18

Data Selection, Data Preprocessing, Data Transformation, Data Mining, Pattern Evaluation, and Knowledge Representation.

Question 19

What is ‘Data Selection’

Answer 19

identifying the relevant data sources for analysis by selecting the target dataset(s) or focusing on a subset of variables or data samples.

Question 20

What is ‘Data Preprocessing’

Answer 20

cleaning and preparing the data for analysis by removing outliers and handling missing values to correct errors and inconsistencies.

Question 21

What is ‘Data Transformation’

Answer 21

preparing the data for analysis by transforming it into suitable formats for mining. This data transformation includes reduction, normalization, discretization, and feature engineering.

Question 22

What is ‘Data Mining’

Answer 22

apply algorithms such as classification, clustering, and association rules to extract patterns or models from the processed data.

Question 23

How does ‘Data Mining’ work?

Answer 23

Collect Data: Start with a lot of information (like sales records, customer reviews, or website activity).

Analyze: Use tools and algorithms (special formulas) to look for patterns.

Find Insights: Spot trends, such as which products sell best during certain months or what type of customers are most loyal.

Question 24

What is ‘Pattern Evaluation’

Answer 24

identify interesting patterns in the dataset by assessing their relevance, validity, novelty, and potential usefulness for action.

Question 25

What is ‘Knowledge Representation’

Answer 25

share the discovered knowledge using reports, visualizations, or decision support systems to communicate the findings to stakeholders.

Question 26

How does ‘Data Transformation’ works?

Answer 26

Collect Data: Gather a lot of small details (e.g., daily sales from multiple stores).

Group It: Organize the data (e.g., by month, region, or product type).

Summarize: Use calculations like totals, averages, or counts to create a summary (e.g., “Total sales for January = $10,000”).

Question 27

What is ‘Data Dredging’

Answer 27

Data dredging (also called data fishing or data snooping) is when someone looks through a lot of data to find patterns or results, but they do it without a clear plan or hypothesis.

Question 28

What is ‘Data Discrepancy’

Answer 28

is when there’s a mismatch or inconsistency in data that should be the same or aligned.

Question 29

What is ‘Data Regression’

Answer 29

A system bug or error that causes old, incorrect data to reappear.

Question 30

What is ‘Regression Analysis’

Answer 30

A statistical tool to predict and explain data relationships.

Question 31

What is the difference between ‘Data Analysis’ and ‘Data Mining’?

Answer 31

data analysis answers predefined questions using statistical techniques. data mining involves discovering hidden patterns in large datasets without a specific question using clustering or association rule mining methods.

Question 32

What is ‘Feature Engineering’

Answer 32

is the process of creating, selecting, or transforming data into meaningful inputs (features) that can improve the performance of a machine learning model.is the process of creating, selecting, or transforming data into meaningful inputs (features) that can improve the performance of a machine learning model.

Question 33

How does ‘Feature Engineering’ works?

Answer 33

Identify Raw Data: Start with the original dataset (e.g., sales data, customer details, sensor readings).

Create New Features: Derive useful information from the raw data.
Example: Combine “date of birth” to calculate “age.”

Transform Features: Apply techniques like scaling or encoding to make the data usable for models.

Select Features: Pick the most relevant features and remove unnecessary ones to avoid overcomplicating the model.

Question 34

What is ‘Classification Technique’

Answer 34

a technique used to categorize data into predefined classes or categories based on the features or attributes of the data instances. It involves training a model on labeled data and using it to predict the class labels of new, unseen data instances.

Question 35

What is ‘Regression Technique’

Answer 35

employed to predict numeric or continuous values based on the relationship between input variables and a target variable. It aims to find a mathematical function or model that best fits the data to make accurate predictions.

Question 36

What is ‘Clustering Technique’

Answer 36

a technique used to group similar data instances together based on their intrinsic characteristics or similarities. It aims to discover natural patterns or structures in the data without any predefined classes or labels.

Question 37

What is ‘Association Rule’

Answer 37

focuses on discovering interesting relationships or patterns among a set of items in transactional or market basket data. It helps identify frequently co-occurring items and generates rules such as “if X, then Y” to reveal associations between items.

Question 38

What is ‘Anomaly Detection’

Answer 38

aims to identify rare or unusual data instances that deviate significantly from the expected patterns. It is useful in detecting fraudulent transactions, network intrusions, manufacturing defects, or any other abnormal behavior.

Question 39

What is ‘Time Series Analysis Technique’

Answer 39

focuses on analyzing and predicting data points collected over time. It involves techniques such as forecasting, trend analysis, seasonality detection, and anomaly detection in time-dependent datasets.

Question 40

What is ‘Neural Networks Technique’

Answer 40

a type of machine learning or AI model inspired by the human brain’s structure and function. They are composed of interconnected nodes (neurons) and layers that can learn from data to recognize patterns, perform classification, regression, or other tasks.

Question 41

What is ‘Decision Trees Technique’

Answer 41

graphical models that use a tree-like structure to represent decisions and their possible consequences. They recursively split the data based on different attribute values to form a hierarchical decision-making process.

Question 42

What is ‘Ensemble Methods Technique’

Answer 42

combine multiple models to improve prediction accuracy and generalization. Techniques like Random Forests and Gradient Boosting utilize a combination of weak learners to create a stronger, more accurate model.