Data science is an interdisciplinary field that applies scientific methods and algorithms to extract knowledge from both structured and unstructured data. It transforms raw data into meaningful information, which can then be used to generate insights and guide decision-making across various sectors, including healthcare, finance, and education. The document outlines the data-information-knowledge triangle and highlights applications of data science in different domains.

1. What is Data Science?
Applications of Data Science

2. What is Data?
Data is defined as raw facts and figures collected and stored in database. Data are
records which are collected by various ways, large number of resources generates
data, and this data is in different formats. It is typically unprocessed and lacks
context. Data can exist in various forms such as text, numbers, images, audio, or
video.
Examples of data include individual words in a text document, numbers in a
spreadsheet, pixels in an image, or individual sound waves in an audio file.
• Student A: Math score - 85, Science score - 78, English score - 90
• Student B: Math score - 75, Science score - 82, English score - 88
• Student C: Math score - 90, Science score - 85, English score - 92

3. Information
Information is derived from data through the process of organizing, analyzing, and
interpreting it to make it meaningful and useful. Information provides context,
relevance, and understanding. It answers questions such as who, what, when,
where, and how.
• Average math score for the class: 83.3
• Average science score for the class: 81.7
• Average English score for the class: 90
• Student with the highest overall score: Student C.

4. Knowledge
Knowledge involves the ability to apply information effectively to achieve a
specific purpose or to solve problems. Knowledge is about knowing why
something is the way it is, understanding relationships between different pieces of
information, and being able to make judgments or predictions based on that
understanding. It encompasses insights, expertise, skills, and intuition.
• Recognizing that Student C consistently performs well across all subjects and
may benefit from more advanced coursework or enrichment activities.
• Noting that Student B's science score is lower than their scores in other subjects,
indicating a potential area for targeted intervention or additional support.
• Understanding that the overall class performance can be influenced by various
factors such as teaching methods, curriculum effectiveness, and individual
learning styles.

5. Data Information Knowledge Triangle
Knowledge
Information
Data
Processed data provides
information
Raw facts and figures
Application of information
effectively

6. Data Science
Data science is an interdisciplinary field that uses scientific methods, processes,
algorithms, and systems to extract knowledge and insights from structured and
unstructured data. It encompasses a variety of techniques, including data mining,
machine learning, statistical analysis, and predictive modeling, to analyze large
volumes of data and uncover hidden patterns, correlations, and trends.
The goal of data science is to generate actionable insights and solutions that can
inform decision-making, solve complex problems, optimize processes, and drive
innovation across various domains and industries.

7. Key components of data science
Data collection and cleaning
Exploratory Data Analysis (EDA)
Statistical Analysis
Machine Learning
Data Visualization
Big Data Technologies

8. Applications of data science
Healthcare
• Drug discovery
and
development.
• Predictive
analysis for
disease
diagnosis.
Finance
• Algorithm
trading
• Credit risk
assessment.
• Fraud detection
Marketing
• Customer
segmentation
• Personalized
recommendation

9. Applications of data science
Education
• Adaptive
learning.
• Predictive
student success.
Manufacturing
• Predictive
maintenance
• Quality control
Social Media
• Sentiment
analysis
• Content
recommendation

10. Applications of data science
Government
• Crime prediction
Healthcare
management
• Resource
allocation
• Patient
management
Transportation
and Logistics
• Route
optimization
• Demand
forecasting
• Vehicle tracking

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