apps.precollege.brown.edu

Bird's Eye View of Machine Learning, Geospatial Data Analysis, and Python

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 capital, Washington. D.C., 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 you 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 you 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 you 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 Steven F. Udvar-Hazy Center (a Smithsonian National Air and Space Museum).

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

You will leave this course with the ability to use Python to derive insights from data. To achieve this, you 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.

For more information, please see the program website.

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. The course will feature content that is typically taught at the collegiate level, as well as content that is often what a software engineer would 'learn on the job'.

Sections

This course is no longer being offered.