Data Science & AI Literacy
Lecture 01: Foundations of Data Science and
Artificial Intelligence
Welcome to the comprehensive study notes on Data
Science and Artificial Intelligence (AI). In the modern
digital era, data has become the new oil. Organizations
across the globe are leveraging data-driven strategies to
automate processes, predict future trends, and optimize
decision-making environments.
1. What is 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 combines core concepts from
computer science, mathematics, statistics, and domain
expertise.
● Data Extraction: The process of gathering raw
data from various secure sources.
, ● Data Analysis: Inspecting, transforming, and
modeling data to discover useful information.
● Predictive Analytics: Using historical statistical
patterns to forecast future outcomes.
Core Component Checkpoint
Data Science is not just about writing code; it is a
lifecycle that involves understanding business problems,
preparing data, building statistical models, and
communicating visualizations to stakeholders.
2. Introduction to Artificial Intelligence (AI)
Artificial Intelligence refers to the simulation of human
intelligence processes by machines, especially
computer systems. These processes include learning
(the acquisition of information and rules for using the
information), reasoning (using rules to reach
approximate or definite conclusions), and self-correction.
3. The Intersection of Data Science and AI
While Data Science focuses on extracting insights and
patterns, AI focuses on building systems that act
,intelligently based on those insights. Data Science
serves as the foundational fuel for training AI models.
Without high-quality data, modern artificial intelligence
algorithms cannot function effectively.
Lecture 02: Understanding Data and Its
Classifications
Before building any Artificial Intelligence model or
performing advanced data analytics, a data scientist
must understand the nature of the data they are working
with. Data comes in various shapes, sizes, and formats,
which heavily influences how it is stored and processed.
1. Structured Data
Structured data is highly organized information that fits
predefined fields or models perfectly. It is highly
searchable and easy to analyze using traditional
relational databases.
, ● Format: Typically stored in rows and columns
(tables).
● Examples: SQL databases, Excel spreadsheets,
dates, financial transactions, and phone numbers.
2. Unstructured Data
Unstructured data is information that does not have a
predefined conceptual framework or data model. It
makes up the vast majority of the data available in the
digital world today and requires advanced AI techniques
to parse effectively.
● Format: Raw, text-heavy, or binary files with no
rigid organization.
● Examples: Audio files, video recordings, satellite
images, PDF documents, and social media posts.
Technical Note: Semi-Structured Data
There is also a middle ground known as
Semi-Structured data. It does not look like a traditional
table but contains tags or markers to separate data
Lecture 01: Foundations of Data Science and
Artificial Intelligence
Welcome to the comprehensive study notes on Data
Science and Artificial Intelligence (AI). In the modern
digital era, data has become the new oil. Organizations
across the globe are leveraging data-driven strategies to
automate processes, predict future trends, and optimize
decision-making environments.
1. What is 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 combines core concepts from
computer science, mathematics, statistics, and domain
expertise.
● Data Extraction: The process of gathering raw
data from various secure sources.
, ● Data Analysis: Inspecting, transforming, and
modeling data to discover useful information.
● Predictive Analytics: Using historical statistical
patterns to forecast future outcomes.
Core Component Checkpoint
Data Science is not just about writing code; it is a
lifecycle that involves understanding business problems,
preparing data, building statistical models, and
communicating visualizations to stakeholders.
2. Introduction to Artificial Intelligence (AI)
Artificial Intelligence refers to the simulation of human
intelligence processes by machines, especially
computer systems. These processes include learning
(the acquisition of information and rules for using the
information), reasoning (using rules to reach
approximate or definite conclusions), and self-correction.
3. The Intersection of Data Science and AI
While Data Science focuses on extracting insights and
patterns, AI focuses on building systems that act
,intelligently based on those insights. Data Science
serves as the foundational fuel for training AI models.
Without high-quality data, modern artificial intelligence
algorithms cannot function effectively.
Lecture 02: Understanding Data and Its
Classifications
Before building any Artificial Intelligence model or
performing advanced data analytics, a data scientist
must understand the nature of the data they are working
with. Data comes in various shapes, sizes, and formats,
which heavily influences how it is stored and processed.
1. Structured Data
Structured data is highly organized information that fits
predefined fields or models perfectly. It is highly
searchable and easy to analyze using traditional
relational databases.
, ● Format: Typically stored in rows and columns
(tables).
● Examples: SQL databases, Excel spreadsheets,
dates, financial transactions, and phone numbers.
2. Unstructured Data
Unstructured data is information that does not have a
predefined conceptual framework or data model. It
makes up the vast majority of the data available in the
digital world today and requires advanced AI techniques
to parse effectively.
● Format: Raw, text-heavy, or binary files with no
rigid organization.
● Examples: Audio files, video recordings, satellite
images, PDF documents, and social media posts.
Technical Note: Semi-Structured Data
There is also a middle ground known as
Semi-Structured data. It does not look like a traditional
table but contains tags or markers to separate data
