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# Introduction to IoT, Data, and Analytics Concepts

Internet of Things (IoT)

The Internet of Things (IoT) refers to a network of physical devices—such as home appliances, vehicles, and mobile phones—that are embedded with software, sensors, and connectivity features. These capabilities enable the devices to collect and share data across networks [1].

IoT Devices

IoT devices are the physical objects that make up this network. For example:

  • A smartphone can be used to record tutorial videos (data collection).
  • These videos can be transferred to a computer (data sharing).
  • Finally, the content can be uploaded to YouTube or other platforms (further data collection and distribution).

Big Data

Big Data refers to datasets that are too large, too fast, or too diverse to be handled effectively by traditional methods [2]. It is not only about size, but also about how we store, process, and analyze these massive volumes of structured and unstructured data to reveal valuable business insights [3].

Raw Data

Raw data is unprocessed information—rows and rows of values that hold immense potential. However, without transformation and analysis, raw data has very limited usefulness.

Data Architecture

Data Architecture is the structured approach to managing data. It defines how data is collected, processed or transformed, distributed, and stored to support organizational needs [4].

Data Engineering

Data Engineering involves designing and building data pipelines that aggregate, transform, and store data for analysis and decision-making [5].

Data Modeling

Data Modeling focuses on defining relationships between raw data through visual representations. It helps create structured frameworks for databases and improves how data is organized and understood [6].

Data Mining

Data Mining is the process of analyzing large datasets to discover patterns, trends, and insights. These insights can be used to predict future outcomes, recommend solutions, or identify risks [7].

Machine Learning

Machine Learning involves training machines using data and algorithms so they can perform tasks such as prediction, classification, or identification without explicit human programming [8].

Data Visualization

Data Visualization transforms raw data into charts, graphs, and dashboards. This makes patterns and insights visible that would otherwise remain hidden in unprocessed data [9].

References

  1. IBM. What is the Internet of Things (IoT)? Available at: https://www.ibm.com/think/topics/internet-of-things (Accessed: 16 September 2025).
  2. Google Cloud. What is Big Data? Available at: https://cloud.google.com/learn/what-is-big-data?hl=en (Accessed: 16 September 2025).
  3. Cloud Advocate. Big Data Explained [Video]. YouTube. Available at: https://youtu.be/K3pXnbniUcM?t=662 (Accessed: 16 September 2025).
  4. Instaclustr. Data Architecture: Key Components, Tools, Frameworks, and Strategies. Available at: https://www.instaclustr.com/education/data-architecture/data-architecture-key-components-tools-frameworks-and-strategies/ (Accessed: 16 September 2025).
  5. IBM. What is Data Engineering? Available at: https://www.ibm.com/think/topics/data-engineering (Accessed: 16 September 2025).
  6. Future Processing. Data Modelling: Why It Matters. Available at: https://www.future-processing.com/blog/data-modelling/ (Accessed: 16 September 2025).
  7. Investopedia. Data Mining. Available at: https://www.investopedia.com/terms/d/datamining.asp (Accessed: 16 September 2025).
  8. MIT Sloan. Machine Learning Explained. Available at: https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained (Accessed: 16 September 2025).
  9. Johns Hopkins University. Data Visualization Guide. Available at: https://guides.library.jhu.edu/datavisualization (Accessed: 16 September 2025).

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