Enabling Technologies for Data Science and Analytics: The Internet of Things


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Course Overview: The Internet of Things (IoT) is experiencing rapid growth, with a prediction that over 25 billion devices will be connected by 2020. In this data science course, you will delve into the major components of the IoT and explore how data is acquired from sensors. Additionally, you will examine various methods for analyzing event data, such as sentiment analysis and facial recognition software. The course will also cover how data generated from IoT devices can be effectively utilized to make informed decisions.

Course Content:

  1. Introduction to the Internet of Things: An overview of the IoT, its major components, and the significance of connected devices.
  2. Data Acquisition from Sensors: Understanding the process of data acquisition from sensors within IoT devices and exploring different types of sensors used in various applications.
  3. Event Data Analysis: Techniques and approaches for analyzing event data generated by IoT devices, including patterns, anomalies, and correlations.
  4. Sentiment Analysis: Examining sentiment analysis methods to understand the emotional tone and opinions expressed in data collected from IoT devices.
  5. Facial Recognition: Exploring facial recognition software and its applications in IoT, including face detection, identification, and emotion recognition.
  6. Data-Driven Decision Making: Utilizing data generated by IoT devices to inform decision-making processes and enhance operational efficiency.

By completing this course, you will gain a solid understanding of the key aspects of the Internet of Things and how data science techniques can be applied to analyze and utilize data generated by IoT devices. This knowledge will enable you to contribute to the growing field of IoT and leverage the vast amount of data available to make informed decisions and drive innovation.

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