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Applied Information Science

This multidisciplinary project-based laboratory course provides instruction on extracting information from data through hands-on activities that complement traditional classroom experience. Topics include predictive modeling, regression and classification; data cleaning and preprocessing; feature engineering and selection; entropy, information theory, and learning. This course focuses on applications and involves working with real data. It leverages an array of tools including Git, SQLite, Python, NumPy, Pandas, and Tensorflow. The focus is on modular projects, algorithms and implementation, data management, and visualization. In addition, emphasis is put on team work, presentation skills, time management, creativity, and innovation.

Learning Outcomes

  • Enhance engineering education by facilitating learning through applied projects in information science.
  • Review basics of project development, programming concepts, and fundamentals of data analysis.
  • Foster leadership and team work, with division of labor, complementary tasks, discussion and integration.
  • Develop the ability to bridge theoretical concepts and practical tasks while dealing with information extraction.
  • Master elements of experiential learning: abstract conceptualization, active experimentation, concrete experience, reflective observation.
  • Improve transferable engineering skills and the ability to integrate different concepts.
  • Promote creativity and critical thinking.
  • Refine presentation skills and the ability to conduct and manage projects.

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  • Jupyter Notebook 97.7%
  • Python 2.3%