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Harvard University
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MOOC
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MOOC
All of us carry explicit or implicit theories of learning. They manifest themselves in the ways we learn, the ways we teach, and the ways we think about leadership and learning. In Leaders of Learning, you will identify and develop your personal theory of learning, and explore how it fits into the shifting landscape of learning. This isn’t just about schools, it’s about the broader and bigger world of learning. The education sector is undergoing great transformation, and in the coming decades will continue to change. How we learn, what we learn, where we learn, and why we learn; all these questions will be reexamined. In Leaders of Learning, we will explore learning, leadership, organizational structure, and physical design.
- Course related:
- APSS1L01 Tomorrow's Leaders and APSS2A01 Service Leadership
- Subjects:
- Psychology
- Keywords:
- Learning Psychology of Learning
- Resource Type:
- MOOC
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MOOC
This is CS50x , Harvard University's introduction to the intellectual enterprises of computer science and the art of programming for majors and non-majors alike, with or without prior programming experience. An entry-level course taught by David J. Malan, CS50x teaches students how to think algorithmically and solve problems efficiently. Topics include abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development. Languages include C, Python, SQL, and JavaScript plus CSS and HTML. Problem sets inspired by real-world domains of biology, cryptography, finance, forensics, and gaming. The on-campus version of CS50x , CS50, is Harvard's largest course.
- Course related:
- COMP1011 Programming Fundamentals
- Subjects:
- Computing
- Keywords:
- Computer programming Computer science
- Resource Type:
- MOOC
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MOOC
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
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MOOC
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.
- Subjects:
- Statistics and Research Methods and Mathematics and Statistics
- Keywords:
- Life sciences -- Statistical methods Mathematical statistics -- Data processing R (Computer program language)
- Resource Type:
- MOOC
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MOOC
Learn fundamental principles of architecture — as an academic subject or a professional career — from a study of history’s important buildings.
- Keywords:
- Architectural design Architecture
- Resource Type:
- MOOC