Data Analysis and Database Design Quiz

Big Data

• Data is crucial for decision-making in all areas of business
• In 2025, global data generation is projected at 175 zettabytes (ZB)
• 1 ZB = 1 billion gigabytes
• Daily internet user data generation exceeds 2.5 million gigabytes
• 90% of current data was generated within the past two years.

Five Vs of Big Data

• Velocity: Includes batch, near real-time, real-time, and streaming data.
• Variety: Includes structured, unstructured, and semi-structured data formats.
• Volume: Includes large datasets measured in terabytes, records, transactions, tables, and files
• Veracity: Concerns trustworthiness, authenticity, origin, reputation, accountability
• Value: Represents the insights generated from data analysis (statistical, events, correlations).

Data Sources

• Facebook
• Twitter (500,000 tweets per minute)
• Instagram (347,222 posts per minute)
• Internet of Things (IoT) sensors (75 million connected devices generate data)

Big Data Storage

• Less than 20% of globally generated data is stored in relational databases.
• 80% is unstructured (text, images, videos)
• Big Data stored in big data architectures, clouds, and NoSQL databases.
• Traditional databases can't handle today's massive datasets.

Data Analysis Types

• Descriptive analysis: Summarizes and describes data (e.g., unemployment by age)
• Trend analysis: Analyzes how data changes over time (e.g., unemployment trends monthly)
• Comparative analysis: Compares data across different groups or variables (e.g., unemployment trends by region).

Economic and Financial Data Sources

• INE (National Statistics Institute of Spain): Provides statistical data on the Spanish economy, demographics, and social aspects.
• Ministry of Economy, Trade and Enterprise: Provides financial data and statistics on the Spanish economy (macroeconomic, public finances, labor market, and foreign trade).
• Other sources include Madrid Stock Market, Spanish Bank, Eurostat. world bank, and the IMF.

Introduction to Databases

• Databases manage data across various industries (ecommerce, social media, healthcare, and others).
• Traditional methods like paper, tapes, and filing cabinets are now obsolete. Limitations include search difficulty, lack of integrity, and inability to handle large volumes.
• Databases evolved from simple models to become the primary tool for data storage and retrieval.
• Database management systems (DBMS) provide an interface between users and data.

Relational Databases

• Relational databases organize data into tables with rows and columns.
• Tables relate to each other through relationships.
• Primary keys uniquely identify rows in each table.
• Foreign keys relate rows in different tables.
• Relationships help eliminate data redundancy and ensure integrity.

Excel as a Flat File Database

• Excel can function as a simple database accommodating smaller datasets.
• It lacks the sophisticated structures and features of relational databases.

NoSQL Databases

• NoSQL databases are flexible, schema-less, and horizontally scalable.
• They handle diverse data types: structured, semi-structured (JSON), and unstructured (images, text, etc.).
• NoSQL databases are commonly used in environments where scalability and flexibility are important factors such real-time applications or big data analysis.

SQL

• SQL (Structured Query Language) is a standard language for managing and manipulating relational databases.
• SQL commands are used to create, read, update, and delete data in a database.
• SQL is very useful for data analysis.

Key Concepts in Databases

• Tables: Data is stored in structured tables with rows and columns.
• Rows: Individual records within a table.
• Columns: Fields or attributes describing data in a row.
• Primary Keys: Uniquely identify rows within a table.
• Foreign Keys: Establish relationships between tables.
• Relationships: Connect data from different tables using primary and foreign keys.
• Data Types: Specify the nature of data held within a column or field.

Operators in Databases

• Arithmetic Operators: Used for mathematical calculations (e.g., +, -, *, /)
• Comparison Operators: Used for comparisons between data values (e.g., <,>,=,!=)
• Logical Operators: Used to combine conditional statements (e.g., AND, OR, NOT)

Data Integrity in Databases

• Data integrity ensures accuracy, reliability, and consistency of data.
• It is crucial for preventing errors and reliable operations with data; especially crucial and unavoidable in financial, and business settings.
• Good normalization minimizes storage redundancy and improves integrity.

Test your knowledge on data analysis techniques and database design principles. This quiz covers topics such as summarizing datasets, normalization, ER diagrams, and financial statistics. Suitable for students learning about statistics and database management.