Aviation Data Science Lab (DSL): Short Course

 

Short Course Curriculum

 

Aviation Data Science Lab short course teaches the fundamentals of reproducible data science and analytic, probabilistic reasoning & statistical inference, and machine learning to leverage data generated within the large scope of aviation and aeronautics. Data sets such as Sherlock, ATD-2, BTS, and FOQA data will be used to build case studies throughout the course around applications of interest in predictive models for air traffic management, airport surface operations, and flight safety.

 

Instructors: Dr. Milad Memarzadeh & Dr. Ata Akbari Asanjan, USRA

Duration: 12 weeks

Dates: October 6th – December 15th, 2020

 

Course Flyer
Course Registration Form

 

Course will be taught in two phases:

  • Lecture and discussion on the topic of interest
  • Lab, with implementation of the methods learned on the real-world data using Python in Jupyter Notebook

 

Pre-Requisites

 

1. Linear Algebra

You can learn the basics of this topic here.

2. Basics of Python Programming

Many online short-courses are available to learn the basics.

 


 

Previous Courses

 
Short Course Curriculum

This course teaches you fundamentals of reproducible data science and analytics, probabilistic reasoning & statistical inference, and machine learning to leverage data generated within the large scope of aviation and aeronautics.

 

Instructors: Dr. Milad Memarzadeh, Senior Scientist, Data Sciences, USRA

Duration: 12 weeks

Dates: March 10th – May 26th, 2020

 

Course Flyer

 

Course will be taught in four modules:

(1) Data science basics

(2) Supervised reasoning

(3) Unsupervised reasoning

(4) Dimensionality reduction and data visualization

 

We will cover three main areas of aviation data:

(1) Airspace Operations

(2) Surface Operations 

(3) Flight Safety

 

We will be using ATD-2, Sherlock, and FOQA data to build the case studies. Course is designed in two phases: (i) lecture and discussion: on the important topics in each module, and (ii) lab: with implementation of the methods learned on the real-world data using Python in Jupyter Hub. Evaluation will be based on a few individual assignments and a group project.

 

 

Pre-Requisites

1. Linear Algebra

You can learn the basics of this topic here.

2. Basics of Python Programming

Many online short-courses are available to learn the basics.