MIS_Week_1 - Tagged (1)

Week 1: Introduction to Databases

  • Course: Management Information Systems (Spring 2025)

  • Instructor: Yuyang Ye (yuyang.ye@rutgers.edu)

  • Date: January 22, 2025

Database Basics

  • Types of Data

    • Numeric Data: Information represented by numbers.

      • Discrete Data: Finite values (e.g., number of products sold).

      • Continuous Data: Infinite values (e.g., temperature readings).

    • Textual Data: Written or typed information (e.g., customer reviews, social media posts).

      • Requires analysis using text mining and natural language processing.

    • Visual Data: Images, videos, graphs (e.g., photographs, charts).

      • Analyzed using image recognition software and video analysis algorithms.

    • Audio Data: Sound recordings and speech (e.g., recorded conversations, music).

      • Requires analysis through speech recognition and audio signal processing.

Common MySQL Data Types

  • INTEGER: Whole numbers, stored in 4 bytes, range from -2,147,483,648 to 2,147,483,647.

  • DOUBLE PRECISION: Base and exponent format number, stored in 8 bytes. Range of the exponent is -1022 to 1023.

    • Example: 1/3 produces a repeating fraction.

  • DECIMAL(N,D): Fixed-point number, suitable for monetary values. N is the total number of digits, D is the number after the decimal point.

    • Example: DECIMAL(4,2) can store 65.99, but not 165.1.

  • DATETIME: Date and time in 'YYYY-MM-DD hh:mm:ss' format.

    • Example: "2024-05-15 09:39:22"

  • DATE: Date in 'YYYY-MM-DD' format.

    • Example: "2024-05-15"

  • VARCHAR(N): String of variable length, max N characters.

    • Example: VARCHAR(5) can hold "hello", but not "goodbye".

  • TEXT: String of varying lengths, cannot be used for keys.

    • Example: "hello and goodbye"

  • BOOLEAN: Logical values TRUE or FALSE, stored in 1 byte.

    • Example: TRUE is stored as 1, FALSE as 0.

Uses of Data

  • Describes Real-World Systems: Data is used for forecasting, financial analysis, tracking pandemics, etc.

    • Forecasting Weather: Uses data from various sources to predict weather patterns.

    • Analyzing Financial Investments: Financial data aids in investment decisions and risk management.

    • Tracking Pandemics: Health data to monitor disease spread and mobility.

  • Public Data Sources: Data can be obtained from various public databases like data.gov, cancer.gov, kaggle.com, etc.

Characteristics of Relational Databases

  • Relational Database Management System (RDBMS): Stores data in tables (relations) consisting of rows (records) and columns (attributes).

    • Examples: MySQL, PostgreSQL, Oracle.

  • Normalization: Reduces redundancy and improves integrity.

  • Data Integrity: Enforced through primary keys and foreign keys.

  • Data Type Consistency: Fixed data types for columns.

  • Difference from Spreadsheets:

    • Relational databases have fixed roles for rows and columns, unlike spreadsheets.

    • Easier modification of rows in databases compared to columns.

Extracting Useful Information from Data

  • Data Visualization: Helps present numerical data more intuitively.

    • Example: NYC Open Data on fire hydrants generating revenue from parking tickets.

Course Focus

  • Developing MIS: Learn to create Management Information Systems to support decision-making.

  • Data Flow Understanding: Learn to model data flow through organizations.

  • SQL Proficiency: Writing SQL queries to derive information from RDBMS.

  • Spreadsheet Skills & Visualization: Using Excel and Tableau for data analysis and visualization.

  • Data Science Pipeline: Understanding Extract-Load-Transform (ELT) processes for data handling.