Introductory and advanced participants will be provided with essential mathematical concepts and Python programming frameworks critical for modeling, analyzing, and optimizing real-world aerospace and engineering scenarios. This course explores predictive analytics, system optimization, and data-driven decision-making through state machines. Students will directly apply these concepts to scenarios relevant to aerospace roles, such as autonomous drone navigation using LiDAR, radar, and object recognition algorithms, as well as advanced Identification Friend or Foe (IFF) transponder systems.
Participants will utilize Python and various open source data analytics tools to produce visualizations such as performance charts, reliability heat maps, and predictive trend analyses, handling large-scale datasets of up to one million data points (100 x 10,000). The course is structured to accommodate varied skill levels, with content suitable for both beginners and experienced students, ensuring practical applicability through case studies and hands-on projects relevant to diverse engineering roles across a myriad of fields. Because of the complex nature of this class, there will be two co-instructors and instructors will offer one hour of “office hours” per lesson.