Page 254 - AI Ver 3.0 class 10_Flipbook
P. 254
UNIT 4
STATISTICAL DATA
(PRACTICAL)
Learning Outcomes
• What is Data Science? • Introduction to Low/No-Code AI Approach for Statistical Data
• Important Concepts in Statistics • Orange Data Mining
• Applying the AI Project Cycle in Orange Data Mining
Artificial Intelligence (AI) is a powerful technology that drives many of today’s innovations, and it depends heavily
on data. AI systems use large amounts of data to “learn” through algorithms, allowing machines to do tasks that
usually need human intelligence. The success of AI depends on the type, quality, and amount of data it receives.
Depending on the data and the tasks the AI is made to do, AI can be divided into three main domains: Data
Science, Computer Vision, and Natural Language Processing (NLP).
What is Data Science?
Data Science is a continuous process of exploring and discovering new things by analysing data. It helps us find
patterns, trends, and insights that allow us to better understand the world. At its core, Data Science takes raw data
and turns it into valuable knowledge using a mix of statistics, computer techniques, and specialised knowledge
from different fields.
Data Science itself is an interdisciplinary field that integrates various aspects of statistics, data analysis, machine
learning, and other related methodologies. Its purpose is to extract meaningful insights from data and apply them
to solve real-world problems. To achieve this, data scientists use a diverse range of techniques drawn from fields
like Mathematics, Statistics, Computer Science, and Information Science, all of which contribute to the development
of models, algorithms, and tools that enhance data-driven decision-making.
In essence, Data Science serves as the backbone of AI, bridging the gap between raw data and actionable
intelligence, while providing the foundation for AI systems to learn, adapt, and improve over time.
For example, imagine a company that sells online clothing. The company collects data about customers’ purchasing
habits, such as what items they buy, when they buy them, and what discounts they use. A data scientist can analyse
this data to find patterns, such as which items are most popular during certain seasons or how certain discounts
affect sales. Using these insights, the company can make data-driven decisions like adjusting their marketing
strategy, predicting demand, or creating personalised offers for customers.
252 Touchpad Artificial Intelligence (Ver. 3.0)-X

