chap 11

• The Vast of Data


  • Patterns & Structures
  • Algorithms & Models
• Big Data


  Extremely large datasets that cannot be analyzed, processed, or stored with traditional data processing. Big Data is characterized by (5 Vs): Volume, Velocity, Variety, Veracity, Value
• The goal of Big Data analytics is to uncover hidden patterns, correlations, and insights that can help in decision-making
• The Importance of Big Data

  • Informed Decision Making
  • Understanding Market Trends
  • Improving Customer Experiences
  • Operational Efficiency
  • Innovation
  • Risk Management
  • Enhancing Public Services
• The Sources of Big Data

  • Social Media
  • Internet of Things (IoT) Devices
  • Transaction Records
  • Web Logs and Browsing Behaviors
  • Multimedia Content
  • Public Databases and Government Records
  • Business Applications
  • Scientific and Research Data
• Data Mining


  The process of discovering patterns, correlations, and anomalies within large sets of data (Big Data) to predict outcomes. Data Mining includes: Classification, Clustering, Association Rule Learning, Regression
• Data Mining techniques are used in a variety of domains like marketing, fraud detection, healthcare, and beyond
• Data Mining Processes


  1. Define the business or research problem to understand the goal

  2. Gather and explore the data to see what you have

  3. Clean and organize your data to make it usable

  4. Choose and apply algorithms to find patterns

  5. Check if your models effectively meet your goals

  6. Implement the solution and make decisions based on insights

  7. Keep an eye on the solution's performance and update as needed
• Key Techniques in Data Mining 1/2

  • Classification
  • Clustering
  • Association Rule Learning
  • Regression
• Key Techniques in Data Mining 2/2

  • Anomaly Detection
  • Dimensionality Reduction
  • Neural Networks and Deep Learning
  • Group Methods
• What is Data Mining?
• Machine Learning


  A subset of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. The core of Machine Learning revolves around: Supervised Learning, Unsupervised Learning, Reinforcement Learning
• Machine Learning is widely used for recommendation systems, speech recognition, predictive analytics, and more
• What is Machine Learning?
• The Importance of Machine Learning

  • Automates Decisions
  • Enhances Personalization
  • Improves Efficiency
  • Advances Healthcare
  • Boosts Security
  • Accelerates Research
  • Develops Autonomous Systems
  • Improves Communication
  • Drives Economic Growth
  • Combats Climate Change
• The combination of Big Data, Data Mining, and Machine Learning allows organizations to not only understand the current state of their operations but also to predict future trends. This capability is critical for strategic planning and staying ahead of market changes
• Beyond analysis, the toolkit can automate decision-making processes and routine tasks. For example, Machine Learning models can automate customer service responses or optimize supply chain logistics without human intervention
• With deeper insights and the ability to predict future trends, businesses and researchers can innovate more effectively. This might involve developing new products, entering new markets, or creating more efficient processes
• Cross-Industry Applications


  • Healthcare
  • Finance
  • Retail
  • Manufacturing