Introduction to Data Analysis

Course Overview

• The course consists of assignments, quizzes, midterm exam, practical exam, final project, and final exam, totaling 100 grades.

Importance of Data

• Large amounts of data are being collected and warehoused from various sources such as: • Web data and e-commerce • Financial transactions, bank and credit transactions • Online trading and purchasing • Social Network
• Data is considered as valuable as oil in today's economy.

Data, Information, and Knowledge

• Data: raw, unorganized facts, numbers, or text that can be processed by a computer, including: • Operational/transactional data (e.g., sales, cost, inventory, payroll, accounting) • Non-operational data (e.g., industry sales, forecast data, macroeconomic data) • Meta data (e.g., data about the data itself, such as logical database design or data dictionary definitions)
• Information: the patterns, associations, or relationships among data, providing insights, e.g.: • Analysis of retail point of sale transaction data yields information on which products are selling and when
• Knowledge: information converted into understanding of historical patterns and future trends, e.g.: • Summary information on retail supermarket sales analyzed in light of promotional efforts provides knowledge of consumer buying behavior.

Course Overview

• The course consists of assignments, quizzes, midterm exam, practical exam, final project, and final exam, totaling 100 grades.

Importance of Data

• Large amounts of data are being collected and warehoused from various sources such as: • Web data and e-commerce • Financial transactions, bank and credit transactions • Online trading and purchasing • Social Network
• Data is considered as valuable as oil in today's economy.

Data, Information, and Knowledge

• Data: raw, unorganized facts, numbers, or text that can be processed by a computer, including: • Operational/transactional data (e.g., sales, cost, inventory, payroll, accounting) • Non-operational data (e.g., industry sales, forecast data, macroeconomic data) • Meta data (e.g., data about the data itself, such as logical database design or data dictionary definitions)
• Information: the patterns, associations, or relationships among data, providing insights, e.g.: • Analysis of retail point of sale transaction data yields information on which products are selling and when
• Knowledge: information converted into understanding of historical patterns and future trends, e.g.: • Summary information on retail supermarket sales analyzed in light of promotional efforts provides knowledge of consumer buying behavior.

This quiz covers the basics of data analysis, including the importance of data collection and warehousing. It provides an overview of the field and its applications.