Data engineering is the process of collecting and refining data. Clean and accessible data is an important preparatory step for many use cases that extract information and value from data. Data engineering is foundational to visualization, clustering, classification, regression, and other machine learning methods. This introductory course is on science and engineering applications of data science with particular emphasis on practical applications and specific examples.
The course combines mathematical details with several case studies that provide an intuition for data engineering solutions. A second phase of the course is a hands-on group project. The engineering problems and theory guide the student towards a working fluency in state-of-the-art methods implemented in Python.
- Collect data from sensors and store values
- Filter data, identifying and removing noise or bad values
- Create edge-computing solutions with time-series data streaming
- Assess data information content and predictive capability
- Communicate data quality with uncertainty quantification
- Visualize data to understand relationships and assess data quality
- Understand engineering and business objectives to plan applications
- Implement data engineering techniques to successfully complete a group project
- Engineering-specific programming with treatment of numerical methods.
- Machine Learning for Engineers: building mathematical models for classification and regression based on training data to make empirical predictions or decisions.
- Cybersecurity for Engineers: assessing and mitigating risks from computer-based adversarial attacks on engineered systems.
- Data Science: using scientific methods, processes, algorithms and systems to extract knowledge and insights from data.
- Data Visualization: creating graphical representations of data to extract insights.
- Internet of Things: building cyber-physical systems that connect microcontrollers, sensors, actuators, and other embedded devices. Includes mechatronics, embedded systems, distributed systems, and networking.
- High Performance Computing: programming high-performance computers (e.g., supercomputers, cloud computing) to tackle computationally-intensive engineering problems.
See Course Overview at https://apmonitor.com/dde