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BWSI Python Core 2020

Enrollment is Closed

About This Course

The BWSI Python Core course is as a required component of several of the BWSI-EdX courses:

  • BWSI122: Autonomous Cognitive Assistant 2019
  • BWSI: Autonomous UAV 2019
  • BWSI:171: Build A CubeSat2019
  • BWSI:191: Embedded Security and Hardware Hacking 2019
  • BWSI151: Medlytics 2019
  • BWSI: Racecar 2019
  • BWSI181: Remote Sensing for Crisis Response 2019
  • BWSI161: Unmanned Air Systen-Synthetic Aperture Radar 2019

It must be completed in order to apply to the summer sessions for these courses.


The focus of this online section is the essentials of Python and NumPy. The outline can be broken down into the following components:

  • Learning the essentials of Python and NumPy, via Python Like You Mean It
    • Module 1: Introduction to Python
    • Module 2: Essentials of Python
    • Module 3: Essentials of NumPy


It is beneficial for students to have some previous experience programming. While a relatively comprehensive introduction to Python is presented here, this course is not designed to serve as an introduction to programming - it is a bit faster-paced than is ideal for a first-introduction to programming.

Course Staff

Course Staff Image #1

Ryan Soklaski

Ryan Soklaski is a technical staff member of Lincoln Laboratory’s Intelligence & Decision Technologies group. There, he researches machine learning techniques that are performant under data-restricted circumstances, and works as a core developer for a lab-internal machine learning library. Prior to joining the laboratory, Ryan earned his PhD in theoretical condensed matter physics at Washington University in St. Louis. His doctoral thesis involved conducting physics simulations on high-performance computing clusters to study the physical mechanisms that drive the glass formation process in metallic liquids. Ryan’s background in education includes working as a lead-instructor for an undergraduate physics course at Washington University, and as a graduate-level teaching assistant. His interests include methods of numerical analysis, developing software in Python, and quantum mechanics.

David Mascharka

David Mascharka

David Mascharka is a computer vision researcher in Lincoln Laboratory's Intelligence & Decision Technologies group. His work is aimed at enabling machines to reason about the visual world, while ensuring humans can understand the behavior of these models. Prior to joining the lab, David completed a B.S. in computer science and math and a B.A. in philosophy. His academic interests include trust and interpretability in machine systems, vision and language, and the philosophical intersections of technology and everyday life.

Zac Ravichandran

Zachary Ravichandran

Zachary Ravichandran conducts computer vision research at MIT Lincoln Laboratory's Intelligence and Decision Technologies group. His work includes developing algorithms enabling real-time perception on mobile robotic platforms. He has also contributed to simulations designed for prototyping and evaluating complex, multi-disciplinary systems, with a focus on improving computer vision capabilities. Before joining the lab, Zac completed a B.S. in electrical engineering with a minor in computer science from Rensselaer Polytechnic Institute where he focused on machine learning, signal processing, and robotics.