Lecture

Air Pollution Data Analysis

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In course

ENV-400: Air pollution and climate change

A survey course describing the origins of air pollution and climate change ...

Description

This lecture covers the analysis of air pollution data, focusing on R basics, visualizing time series, and creating summaries of pollutant concentrations. Students will learn to import data, manage directories, aggregate data, and plot time series. The course emphasizes programming with data, troubleshooting, and seeking help. Assignments involve analyzing pollutant concentrations, contrasting seasonal averages, and detecting exceedances of regulatory limits.

Instructors (2)

Satoshi Takahama

Curriculum Vitae (pdf)

Julia Schmale

Expertise atmospheric science, aerosol-cloud interactions, climate change in polar regions Mission Extreme environments are particularly sensitive to climate change and are transforming at accelerated rates. This can have important global repercussions. EERL aims to create integrated process understanding by investigating interactions between the atmosphere, cryosphere, biosphere, ocean, land and anthroposphere. The goal is to characterize climate-change relevant and vertically-resolved processes that are d

Official source

Ontological neighbourhood

Theoretical computer science

Programming language theory: High-level programming languages

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Data analysis

Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.

Data and information visualization

Data and information visualization (data viz or info viz) is the practice of designing and creating easy-to-communicate and easy-to-understand graphic or visual representations of a large amount of complex quantitative and qualitative data and information with the help of static, dynamic or interactive visual items.

Data science

Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data. Data science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine). Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession.

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Visualization (graphics)

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