Demystifying Machine Learning: A Python-Based Introduction to Data Science

Course Description

The field of data science will continue to impact every industry in the 21st century: health, government, transportation, energy, technology, weather, politics, and many more. A hands-on, gentle introduction to data science is critical for today's students to impact tomorrow's workforce. While data science is an important skill, it's complexity will soon lead to the modification of the phrase "Well, it's not rocket science" to "Well, at least it's not data science".

This course will be hosted at the nation's capitol, Washington DC, to highlight the importance of data science skills as they relate to every sector in the government: weather and environment, energy, defense, transportation, health, economy, and others. The course will immerse students in a Python-based data science experience to learn how to use state of the art techniques in data science to derive data-driven insights to the world's most important problems. The skills learned from this course will provide students with a strong foundation to enter the field of data science, with practical hands on experience with open source data across multiple fields of study.

This course is intended to teach data science at the college level, as the students will progress to understand the high level topics involved in data science like supervised learning, unsupervised learning, neural networks for object detection, and natural language processing. Each of these units will be explored in detail and taught with examples on open source datasets in a variety of sectors. However as it is an introductory course, no prior programming or math experience is required - as the course will teach an introduction to the Python programming language in the data science context. The course will leverage Google's Colaboratory platform for access to a simple, easy to use developer experience built for learning data science.

A typical day in this course will include lectures on the topics in data science followed by code-alongs to demonstrate and fortify the techniques learned in class. The course will also include opportunities for group discussions on important ethical topics in data science: for instance, GDPR and the right to your own data, machine learning biases, and data science in health privacy. This course will also feature a field trip to the NSA's National Cryptologic Museum to gain insight into the history of cryptology and its foundation to data science.

The students will work in teams for their final project of the course where they will apply the methods learned to pitch an idea for a novel data science product. The students will present their findings on the last day of class to grow their background in public communication on technical domains.

Each student will leave this course with the ability to use Python to derive insights from data. To achieve this, students will become familiar with Python and the core data science libraries (pandas, numpy, scikit-learn), get a hands-on introduction to open source data (the extract, transform, and loading skills required), and learn to use state of the art data science tools to begin to perform regression, classification, clustering and computer vision.

Prerequisites: This course is an introductory course for interested high school students to explore the topics and methods involved in data science. No prior programming or math experience is required.