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Modeling Climate Anomalies with Statistical Analysis

Modeling Climate Anomalies with Statistical Analysis

本课程是 Modeling and Predicting Climate Anomalies 专项课程的一部分

位教师：Osita Onyejekwe

包含在中

了解更多

3个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

7 小时完成

灵活的计划

自行安排学习进度

攻读学位

了解更多

3个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

7 小时完成

灵活的计划

自行安排学习进度

攻读学位

了解更多

您将学到什么

Visualize and interpret climate anomalies using statistical analysis.
Use APIs to import climate data from government portals.
Visualize data in Python with matplotlib.

您将获得的技能

您将学习的工具

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

3 项作业

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Modeling and Predicting Climate Anomalies 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有3个模块

This course introduces the use of statistical analysis in Python programming to study and model climate data, specifically with the SciPy and NumPy package. Topics include data visualization, predictive model development, simple linear regression, multivariate linear regression, multivariate linear regression with interaction, and logistic regression. Strong emphasis will be placed on gathering and analyzing climate data with the Python programming language.

This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. The degree offers targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://hua.dididi.sbs/degrees/master-of-science-data-science-boulder

单元详情

In this module, we'll start with an introduction to the Python library, Pandas. You'll also learn the fundamentals of data visualization using Matplotlib, a powerful library for creating insightful plots and graphs. At the end of the module you will practice manipulating data with Pandas and visualizing your findings using Matplotlib.

涵盖的内容

4个视频5篇阅读材料1个作业1个编程作业

4个视频总计55分钟

Introduction to the Course3分钟
Meet the Instructor1分钟
Introduction to Pandas for Data Loading and Exploration26分钟
Introduction to Matplotlib and Seaborn for Data Visualization25分钟

5篇阅读材料总计46分钟

Course Updates and Accessibility Support1分钟
Earn Academic Credit for your Work!10分钟
Course Support10分钟
Pandas15分钟
Course Assignments with Jupyter10分钟

1个作业总计15分钟

Pandas and Matplotlib15分钟

1个编程作业总计30分钟

Introduction to Pandas and Matplotlib30分钟

In this module, you will be introduced to APIs and the Python requests library, enabling you to connect and interact with web-based data services. You'll explore climate data sources from NOAA, USGS, and NWIS, and practice accessing data using the dataretrieval library.

涵盖的内容

4个视频6篇阅读材料2个作业

4个视频总计32分钟

Introduction to APIs9分钟
Introduction to NOAA Website for Data Collection9分钟
Introduction to USGS Website and Available Data6分钟
Collecting GWL Data FROM USGS via NWIS8分钟

6篇阅读材料总计105分钟

APIs, JSON, and Python Requests15分钟
API Hands-On (Lab Activity)30分钟
NOAA API and Available Data10分钟
Datasets by USGS10分钟
Methods and Datasets via NWIS10分钟
Access Streamflow Data via NWIS (Lab Activity)30分钟

2个作业总计16分钟

API Hands-On15分钟
Access Streamflow Data via NWIS1分钟

In this module, you will delve into visualizing and analyzing various climate data sets, including air temperature, precipitation, groundwater level (GWL), and soil temperature and moisture. You will learn to create informative visualizations to identify patterns, trends, and anomalies in the data.