Open Educational Resources | Pao Yue-kong Library, The Hong Kong Polytechnic University

MOOC

Energy Within Environmental Constraints

Humanity faces an immense challenge: providing abundant energy to everyone without wrecking the planet. If we want a high-energy future while protecting the natural world for our children, we must consider the environmental consequences of energy production and use. But money matters too: energy solutions that ignore economic costs are not realistic, particularly in a world where billions of people currently can’t afford access to basic energy services. How can we proceed? Energy Within Environmental Constraints won’t give you the answer. Instead, we will teach you how to ask the right questions and estimate the consequences of different choices. This course is rich in details of real devices and light on theory. You won’t find any electrodynamics here, but you will find enough about modern commercial solar panels to estimate if they would be profitable to install in a given location. We emphasizes costs: the cascade of capital and operating costs from energy extraction all the way through end uses. We also emphasize quantitative comparisons and tradeoffs: how much more expensive is electricity from solar panels than from coal plants, and how much pollution does it prevent? Is solar power as cost-effective an environmental investment as nuclear power or energy efficiency? And how do we include considerations other than cost? This course is intended for a diverse audience. Whether you are a student, an activist, a policymaker, a business owner, or a concerned citizen, this course will help you start to think carefully about our current energy system and how we can improve its environmental performance.

Subjects:: Environmental Engineering, Building Services Engineering, and Building and Real Estate
Keywords:: Environmental protection Environmental management Renewable energy sources Power resources
Resource Type:: MOOC

MOOC

Statistics and R

This course teaches the R programming language in the context of statistical data and statistical analysis in the life sciences. We will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R code. We provide R programming examples in a way that will help make the connection between concepts and implementation. Problem sets requiring R programming will be used to test understanding and ability to implement basic data analyses. We will use visualization techniques to explore new data sets and determine the most appropriate approach. We will describe robust statistical techniques as alternatives when data do not fit assumptions required by the standard approaches. By using R scripts to analyze data, you will learn the basics of conducting reproducible research. Given the diversity in educational background of our students we have divided the course materials into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. We start with simple calculations and descriptive statistics. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.