BUS 391 Midterm #1

BUS 391 WINTER 2026 MIDTERM Comprehensive Study Guide

1. Week One

a. The Difference Between Data and Information

• Data:

  • Definition: Raw, unprocessed facts and figures without context.

  • Examples: Numbers, text, observations.

• Information:

  • Definition: Processed, organized, and contextualized data that has meaning and value for decision-making.

  • Example: '25' is data; '25 degrees Celsius in San Francisco' is information.

• Transformation of Data to Information:

  • Data becomes information when it is processed to answer questions such as who, what, when, where, and why.

b. How Do Current Computers 'Think'

• Computers process information using binary code (0s and 1s).

• The Central Processing Unit (CPU) executes instructions through a fetch-decode-execute cycle.

• Logic gates perform Boolean operations (AND, OR, NOT) on electrical signals.

• Transistors act as electronic switches that control the flow of electricity.

• Memory stores data and instructions temporarily (RAM) or permanently (storage).

• Algorithms provide step-by-step instructions for solving problems.

2. Business Management Systems

a. Moore's Law

• Definition: States that the number of transistors on a computer chip doubles approximately every 18 months.

• Associated Effects:

  • The price of transistors correspondingly decreases over time.

  • Results in exponential growth in computing power and performance.

  • Enables smaller, faster, and more efficient computers.

• Historical Context: Has been a reliable predictor of technological advancement since 1965.

b. DBMS (Database Management System)

• Definition: Software that creates, manages, and controls access to databases.

• Key Functions:

  • Provides data security, integrity, and consistency.

  • Allows multiple users to access data simultaneously.

• Examples: MySQL, Oracle, Microsoft SQL Server, PostgreSQL.

• DBMS Functions include data definition, manipulation, querying, and administration.

c. Decision-making Information Systems

• Definition: Systems designed to support business decision-making activities.

• Types include:

  • Management Information Systems (MIS).

  • Decision Support Systems (DSS).

  • Executive Information Systems (EIS).

• Purpose: Provide reports, analytics, and insights from organizational data, helping managers make informed decisions.

d. Implementing Change Brings Short-term Stress and Reduced Productivity

• Definition: Change management is challenging for organizations and employees.

• Issues:

  • Learning curve associated with new systems temporarily decreases efficiency.

  • Employees may resist change due to comfort with existing processes.

• Solutions:

  • Proper training and support can minimize disruption.

• Long-term benefits typically outweigh short-term productivity losses.

e. On-premises Solutions

• Definition: Software and hardware located physically within an organization's facilities.

• Characteristics:

  • Organization has full control over infrastructure and data.

  • Requires significant capital investment in servers, networking, and maintenance.

  • Organization responsible for security, updates, and disaster recovery.

  • Often necessary for regulatory compliance or data sensitivity requirements.

f. Cloud-based Options

• Definition: Services hosted on remote servers accessed via the internet.

• Key Benefits:

  • Lower upfront costs with subscription-based pricing models.

  • Scalability: easily adjust resources based on demand.

  • Provider handles maintenance, updates, and security.

  • Accessible from anywhere with internet connection.

• Types include:

  • Public cloud.

  • Private cloud.

  • Hybrid cloud.

g. SaaS (Software as a Service)

• Definition: Cloud-based software delivery model where applications are hosted by a vendor.

• Characteristics:

  • Users access software through web browsers, no local installation required.

  • Examples: Salesforce, Microsoft 365, Google Workspace, Dropbox.

  • Subscription-based pricing (monthly or annual fees).

  • Automatic updates and maintenance handled by provider.

  • Reduces IT overhead and infrastructure costs.

h. Business Intelligence

• Definition: Technologies and strategies for analyzing business data.

• Purpose: Transforms raw data into meaningful insights for decision-making.

• Key Components include data mining, reporting, querying, and visualization.

• Tools: Tableau, Power BI, QlikView, SAP BusinessObjects.

• Benefits: Helps identify trends, patterns, and opportunities; supports strategic planning and competitive advantage.

i. Big Data

• Definition: Extremely large and complex datasets that traditional processing cannot handle.

• Characterization: Described by the Four V's:

  • Volume: Massive amounts of data (terabytes to petabytes).

  • Velocity: High speed of data generation and processing (real-time or near real-time).

  • Variety: Different types and formats of data (structured, semi-structured, unstructured).

  • Veracity: Uncertainty and quality of data (accuracy, trustworthiness).

• Sources: Include social media, sensors, transactions, and IoT devices.

• Requires specialized technologies like Hadoop and Spark.

• Enables predictive analytics and data-driven decision-making.

j. Decision Support Systems (DSS)

• Definition: Interactive computer-based systems that help decision-makers use data and models.

• Applications: Support semi-structured and unstructured decision-making tasks.

• Components:

  • Database.

  • Model base.

  • User interface.

• Features: Provide what-if analysis and scenario planning capabilities, used for strategic planning, resource allocation, and risk assessment.

k. Learning Management Systems (LMS)

• Definition: Software applications for administration, documentation, and delivery of educational content.

• Examples: Moodle, Canvas, Blackboard, Google Classroom.

• Features: Include course management, assessment tools, and progress tracking.

• Applications: Support both academic institutions and corporate training programs, enabling remote and self-paced learning.

3. Databases

a. Relational Databases

• Definition: Organize data into tables (relations) with rows and columns.

• Structure: Tables linked through primary and foreign keys.

• Basis: Based on relational model developed by E.F. Codd.

• Integrity: Ensures data integrity through normalization.

• ACID properties:

  • Atomicity: Transactions are all-or-nothing.

  • Consistency: Transactions bring the database from one valid state to another.

  • Isolation: Transactions occur independently without interference.

  • Durability: Once a transaction is committed, it remains so even in the event of a failure.

• Examples: MySQL, PostgreSQL, Oracle, Microsoft SQL Server.

b. SQL (Structured Query Language)

• Definition: Standard language for managing and manipulating relational databases.

• Main Operations:

  • SELECT (query).

  • INSERT (add data).

  • UPDATE (modify data).

  • DELETE (remove data).

• Subcategories:

  • DDL (Data Definition Language): CREATE, ALTER, DROP.

  • DML (Data Manipulation Language): INSERT, UPDATE, DELETE.

• Features: Supports joins, aggregations, and complex queries.

c. NoSQL

• Definition: Non-relational databases designed for large-scale data and flexibility.

• Types:

  • Document stores: MongoDB.

  • Key-value stores: Redis.

  • Column-family stores: Cassandra.

  • Graph databases: Neo4j.

• Characteristics: Schema-less or flexible schema design; horizontal scalability for handling big data.

• Usage: Optimized for specific use cases and performance.

d. Cloud Database

• Definition: Database running on cloud computing platform.

• Types: Can be SQL or NoSQL.

• Examples: Amazon RDS, Google Cloud SQL, Azure SQL Database.

• Benefits: Include scalability, automated backups, high availability; operate on a pay-per-use pricing model.

e. Data Warehouse

• Definition: Centralized repository that stores integrated data from multiple sources.

• Characteristics: Optimized for query and analysis rather than transaction processing; subject-oriented, integrated, time-variant, and non-volatile.

• Purpose: Supports business intelligence and reporting; utilizes ETL (Extract, Transform, Load) processes.

f. Data Mart

• Definition: Subset of a data warehouse focused on a specific business area or department.

• Comparisons: Smaller and more focused than an enterprise data warehouse.

• Examples: Sales data mart, Marketing data mart, Finance data mart.

• Benefits: Faster implementation and lower cost than a full data warehouse; provides tailored data access for specific user groups.

g. Big Data

• Reference: See section 2.i for detailed explanation.

• In Database Context: Data sets too large for traditional DBMS require distributed processing frameworks.

h. Four V's of Big Data

• Volume: Massive amounts of data (measured in terabytes to petabytes).

• Velocity: High speed of data generation and processing (real-time or near real-time).

• Variety: Different types and formats of data (structured, semi-structured, unstructured).

• Veracity: Uncertainty and quality of data (accuracy, trustworthiness).

• Additional Context: Some models add Value as the fifth V.

i. Online Analytical Processing (OLAP)

• Definition: Technology for performing multidimensional analysis on large data volumes.

• Functions: Enables complex queries and calculations, allows operations such as slice, dice, drill-down, roll-up, pivot.

• Organization: Data organized in cubes with dimensions and measures.

• Optimization: Optimized for read-heavy operations and analysis; contrasts with OLTP (Online Transaction Processing).

j. Data Mining

• Definition: Process of discovering patterns, correlations, and insights from large datasets.

• Techniques:

  • Classification.

  • Clustering.

  • Regression.

  • Association rules.

• Methodology: Uses statistical and machine learning algorithms.

• Applications: Market basket analysis, customer segmentation, fraud detection.

• Outcome: Transforms raw data into actionable business intelligence.

4. Computers and Software

a. Four Basic Computing Functions

• Input: Receiving data and instructions (e.g., keyboard, mouse, sensors).

• Processing: Manipulating and calculating data (CPU operations).

• Storage: Saving data for later use (e.g., RAM, hard drives, SSD).

• Output: Presenting processed information (e.g., monitor, printer, speakers).

b. Examples of Embedded Computers

• Automotive:

  • Engine control units

  • Anti-lock braking systems

  • Infotainment systems

• Home Appliances:

  • Smart refrigerators

  • Washing machines

  • Thermostats

• Medical Devices:

  • Pacemakers

  • Insulin pumps

  • Diagnostic equipment

• Consumer Electronics:

  • Digital cameras

  • Smartwatches

  • Fitness trackers

• Industrial:

  • Manufacturing robots

  • ATMs

  • Point-of-sale systems.

c. What is a Server

• Definition: Computer or software that provides services to other computers (clients).

• Types:

  • Web servers.

  • File servers.

  • Database servers.

  • Email servers.

• Characteristics:

  • High processing power, large storage, redundancy.

  • Designed for 24/7 operation and handling multiple requests.

  • Can be physical hardware or virtual machines.

d. Wi-Fi

• Definition: Wireless networking technology using radio waves.

• Standards: Based on IEEE 802.11 standards.

• Frequencies: Common frequencies include 2.4 GHz and 5 GHz.

• Versions: 802.11n, 802.11ac, 802.11ax (Wi-Fi 6).

• Purpose: Enables wireless internet access within limited range.

e. Bluetooth

• Definition: Short-range wireless technology for data exchange.

• Operation: Operates in the 2.4 GHz frequency band.

• Range: Typically between 10-100 meters depending on class.

• Uses: Common applications include headphones, keyboards, mice, and file transfers.

• Advantages: Lower power consumption compared to Wi-Fi.

f. Ethernet

• Definition: Wired networking technology for local area networks.

• Media: Uses twisted-pair or fiber optic cables.

• Speeds: Range from 10 Mbps (original) to 100 Gbps (modern).

• Reliability: More reliable and faster than wireless connections.

• Standard: Based on IEEE 802.3 standard.

g. Operating Systems

• Definition: System software that manages hardware and software resources.

• Functions:

  • Process management

  • Memory management

  • File systems

  • Security

• User Interface: Acts as an intermediary between user and hardware.

• Examples: Windows, macOS, Linux, iOS, Android.

h. Utility Software

• Definition: Programs that perform maintenance and optimization tasks.

• Examples: Antivirus, disk cleanup, backup software, file compression.

• Purpose: Helps keep computer running efficiently and securely; often included with operating systems or available separately.

i. Productivity Software

• Definition: Applications that help users complete tasks and create content.

• Types:

  • Word processors: Microsoft Word, Google Docs.

  • Spreadsheets: Excel, Google Sheets.

  • Presentation software: PowerPoint, Google Slides.

  • Other Tools: Email clients, calendars, project management tools.

5. Computer Networks and the Internet

a. Computer Networks

• Definition: Interconnected computing devices that can exchange data and share resources.

• Components: Computers, routers, switches, cables, wireless access points.

• Purposes: Enable communication, resource sharing, and collaboration.

• Types by Size: PAN, LAN, MAN, WAN.

b. Local Area Networks (LAN)

• Definition: Network covering small geographic area (e.g., building, campus).

• Characteristics: High data transfer rates and low latency.

• Ownership: Owned and managed by a single organization.

• Technologies: Ethernet, Wi-Fi.

• Functions: Enables file sharing, printer sharing, and internal communication.

c. Wide Area Networks (WAN)

• Definition: Network covering a large geographic area (e.g., countries, continents).

• Purpose: Connects multiple LANs together.

• Example: The Internet is the largest WAN.

• Infrastructure: Uses leased telecommunication lines, satellites, or cellular networks.

• Characteristics: Generally slower than LANs but enables global connectivity.

d. Internet Service Provider (ISP)

• Definition: Company that provides internet access to customers.

• Types: Cable, DSL, fiber optic, satellite, mobile broadband.

• Examples: Comcast, AT&T, Verizon, Spectrum.

• Functions: Provides infrastructure and connectivity to the internet backbone.

e. Internet Protocol Address (IP)

• Definition: Unique numerical identifier assigned to each device on a network.

• Format: IPv4 format: Four octets (e.g., 192.168.1.1).

• Purpose: Enables routing of data packets across networks.

• Types: Can be static (permanent) or dynamic (changes periodically).

f. IPv6

• Definition: Next generation Internet Protocol addressing system.

• Address Length: 128-bit addresses (vs. 32-bit for IPv4).

• Format: Eight groups of hexadecimal digits (e.g., 2001:0db8:85a3:0000:0000:8a2e:0370:7334).

• Advantage: Provides virtually unlimited address space developed to address IPv4 address exhaustion.

g. Transmission Control Protocol (TCP)

• Definition: Core protocol that ensures reliable, ordered delivery of data.

• Characteristics: Connection-oriented protocol (establishes connection before transmission).

• Features: Includes error checking and correction mechanisms.

• Reliability: Guarantees packet delivery in correct order.

• Usage: Employed in applications requiring reliability (e.g., web, email, file transfer).

h. TCP/IP

• Definition: Suite of communication protocols used for the internet and similar networks.

• Combination: Combines TCP (reliability) with IP (routing).

• Structure: Four layers: Application, Transport, Internet, Network Access.

• Importance: The foundation of internet communication; platform-independent and scalable.

i. Resources Provided by the Internet

• World Wide Web (WWW): Websites and web applications.

• Email: Electronic messaging system.

• File Transfer Protocol (FTP): Method for file sharing.

• Cloud Storage and Services: Remote data storage.

• Communication: Voice and video communication (VoIP, video conferencing).

• Media: Streaming media, social networks, online gaming.

j. Tier 1 Providers

• Definition: Top-level ISPs that form the backbone of the internet.

• Characteristics: Can reach every other network on the internet without purchasing transit.

• Interconnection: Peer with each other through settlement-free interconnection.

• Examples: AT&T, Verizon, NTT, Level 3.

• Operations: Operate high-capacity fiber optic networks globally.

k. Undersea Cables

• Definition: Fiber optic cables laid on the ocean floor connecting continents.

• Importance: Carry 99% of international data traffic.

• Characteristics: Provide high bandwidth and low latency for global communications.

• Role: Critical infrastructure for internet, telecommunications, and finance.

• Coverage: Hundreds of cables spanning millions of kilometers worldwide.

l. World Wide Web (WWW)

• Definition: Information system of interlinked hypertext documents accessed via the internet.

• Inventor: Created by Tim Berners-Lee in 1989.

• Protocols: Uses HTTP/HTTPS protocol and URLs for addressing.

• Content: Created with HTML, CSS, JavaScript.

• Access: Accessed through web browsers such as Chrome, Firefox, Safari, Edge.

m. Web 2.0

• Definition: Second generation of web characterized by user-generated content.

• Characteristics: Interactive and collaborative platforms.

• Examples: Social media (Facebook, Twitter), wikis, blogs, video sharing (YouTube).

• Evolution: Shift from static web pages to dynamic web applications.

• Technologies: Involves AJAX, APIs, cloud computing.

n. Web 3.0

• Definition: Emerging vision of semantic and decentralized web.

• Features: Key aspects include Blockchain, decentralization, AI integration.

• Purpose: Semantic web where machines can understand and interpret content.

• Technologies: Cryptocurrencies, NFTs, decentralized apps (dApps).

• Focus: On user ownership and privacy.

o. Deep Web

• Definition: Content not indexed by standard search engines.

• Content Types: Includes password-protected sites, databases, private networks.

• Size: Much larger than the surface web (visible web).

• Examples: Email inboxes, online banking, medical records, academic databases.

• Characteristics: Not inherently illegal or malicious.

p. Dark Web

• Definition: Small portion of deep web intentionally hidden and anonymous.

• Accessibility: Requires special software to access (e.g., Tor browser).

• Purpose: Provides anonymity for users and website operators.

• Usage: Used for both legitimate (privacy, whistleblowing) and illegal activities.

• Access Characteristics: Not indexed by search engines and uses .onion domains.

6. Understanding Database Concepts

a. Hierarchy of Data

• Bit: Smallest unit of data (0 or 1).

• Byte: 8 bits, represents a single character.

• Field: Single piece of information (e.g., First Name, Age).

• Record: Collection of related fields (e.g., one customer's information).

• Table/File: Collection of related records.

• Database: Collection of related tables.

b. Major Access Objects

• Tables: Store data in rows and columns.

• Queries: Retrieve and manipulate data from tables.

• Forms: User-friendly interface for data entry and viewing.

• Reports: Formatted presentation of data for printing or viewing.

• Macros: Automated actions and processes.

• Modules: VBA code for advanced functionality.

7. Requirements Gathering Processes

a. Importance of Planning

• Objectives: Prevents scope creep and project delays.

• Stakeholder Alignment: Ensures alignment among stakeholders and clear expectations.

• Cost Reduction: Reduces costs by identifying issues early.

• Development Roadmap: Provides roadmap for development and implementation.

• Success Metrics: Establishes success criteria and measurable goals.

b. Requirements

• Functional requirements: What the system must do (features, capabilities).

• Business requirements: High-level organizational needs and objectives.

• User requirements: Specific needs of end users.

• Characteristics: Must be clear, specific, measurable, and testable.

• Methods: Gathered through interviews, surveys, observation, and documentation review.

c. Non-functional Requirements

• Definition: Define how the system should perform (quality attributes).

• Key Areas:

  • Performance: Speed, response time, throughput.

  • Security: Authentication, authorization, data protection.

  • Usability: User-friendliness, accessibility, learnability.

  • Reliability: Uptime, fault tolerance, recoverability.

  • Scalability: Ability to handle growth.

8. Artificial Intelligence

a. What is AI?

• Definition: Computer systems capable of performing tasks requiring human intelligence.

• Key Areas: Learning, reasoning, problem-solving, perception, language understanding.

• Types: Narrow AI (designed for specific tasks) vs. General AI (human-level intelligence across domains).

• Applications: Include virtual assistants, recommendation systems, autonomous vehicles, and medical diagnosis.

b. Machine Learning

• Definition: Subset of AI where systems learn from data without explicit programming.

• Functionality: Algorithms identify patterns and make predictions.

• Improvements: Performance improves with more data and experience.

• Main Types:

  • Supervised learning.

  • Unsupervised learning.

  • Reinforcement learning.

c. Deep Learning

• Definition: Subset of machine learning using artificial neural networks with multiple layers.

• Inspiration: Models inspired by human brain structure and function.

• Strengths: Excels at processing complex, unstructured data (e.g., images, speech, text).

• Requirements: Requires large datasets and significant computational power.

• Applications: Include image recognition, natural language processing, and game playing.

d. Cognitive Computing

• Definition: AI systems simulating human thought processes.

• Components: Combines machine learning, natural language processing, and reasoning.

• Functionality: Can understand context, interpret meaning, and learn continuously.

• Example: IBM Watson used for complex decision support and human-computer interactions.

e. Machine Learning Methods

i. Supervised Learning

• Definition: Learns from labeled training data (input-output pairs).

• Function: Algorithm learns to map inputs to correct outputs.

• Types:

  • Classification: Produces discrete outputs.

  • Regression: Produces continuous outputs.

• Examples: Spam detection, house price prediction, image classification.

ii. Unsupervised Learning

• Definition: Learns from unlabeled data without predefined outputs.

• Purpose: Discovers hidden patterns and structures in data.

• Types:

  • Clustering: Groups similar items.

  • Dimensionality reduction.

• Examples: Customer segmentation, anomaly detection, recommendation systems.

iii. Reinforcement Learning

• Definition: Agent learns through trial and error by interacting with an environment.

• Feedback: Receives rewards or penalties for actions taken.

• Objective: Goal is to maximize cumulative rewards over time.

• Examples: Game playing (e.g., AlphaGo), robotics, autonomous vehicles.

f. Neural Networks

• Definition: Computing systems inspired by biological neural networks.

• Structure: Consist of interconnected nodes (neurons) organized in layers.

• Learning: Learn by adjusting connection weights during training.

i. Input Layer

• Function: Receives initial data/features.

• Structure: Each node represents one feature or attribute; no computation occurs, simply passes data forward.

ii. Hidden Layer(s)

• Definition: Intermediate layers between input and output.

• Functionality: Performs transformations and extracts features.

• Characteristics: Deep networks have multiple hidden layers; applies activation functions to introduce non-linearity.

iii. Output Layer

• Function: Produces final prediction or classification.

• Nodes: The number of nodes depends on task (e.g., 1 for regression, multiple for classification).

• Probability: May use softmax for generating probability distributions.

g. Types of Data Used in AI

i. Structured Data

• Definition: Organized in a defined format (e.g., tables, databases).

• Searchability: Easily searchable and analyzable.

• Examples: Spreadsheets, relational databases, CSV files.

• Data Types: Includes numerical and categorical data.

ii. Unstructured Data

• Definition: Lacks a predefined format or organization.

• Processing: Requires processing before analysis.

• Examples: Text documents, images, videos, audio, social media posts.

• Significance: Comprises the majority of data generated today.

iii. Historical Data

• Definition: Past data used to train AI models.

• Importance: Enables pattern recognition and trend analysis.

• Quality: The quality and quantity affect model performance; used for predictions and forecasting.

iv. Internal vs. External Data

• Internal: Data generated within an organization (e.g., sales, operations, HR).

• External: Data from outside sources (e.g., market data, social media, government statistics).

• Combination: Combining both types provides comprehensive insights; internal data is more controlled, whereas external data offers broader context.

h. Generative AI

• Definition: AI that creates new content (e.g., text, images, music, code).

• Mechanism: Learns patterns from training data to generate similar outputs.

• Technologies: Includes Large Language Models (LLMs), Generative Adversarial Networks (GANs), and Diffusion models.

• Examples: ChatGPT, DALL-E, Midjourney, GitHub Copilot.

• Applications: Content creation, design, coding assistance, and other creative work.

i. Current Problems with AI

• Bias and Fairness: Models can perpetuate or amplify societal biases.

• Explainability: Difficulty understanding how AI makes decisions (black box problem).

• Data Quality and Privacy: Models require large amounts of data, raising privacy concerns.

• Hallucinations: AI generating false or nonsensical information.

• Job Displacement and Ethical Concerns.

• Environmental Impact: High energy consumption for training.

j. Bias

• Definition: Systematic errors in AI predictions favoring certain outcomes.

• Sources: Biased training data, algorithm design, feature selection.

• Types:

  • Selection bias.

  • Confirmation bias.

  • Historical bias.

• Consequences: Unfair treatment, discrimination, reinforced stereotypes.

• Mitigation: Diverse training data, fairness metrics, regular audits.

k. Prompt Engineering

• Definition: Crafting effective inputs (prompts) to get desired outputs from AI models.

• Importance: Particularly important for Large Language Models (LLMs).

• Techniques: Clear instructions, providing context, examples (few-shot learning), role assignment.

• Methodology: An iterative process of testing and refining prompts for better results; it's an emerging skill for maximizing AI tool effectiveness.

Good luck on your midterm!