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Ryan Ahmed
In this hands-on project, we will analyze the change in temperatures across globe from the 17th century till now and build a multivariate deep learning based time series model to forecast the U.S. Average temperature. Predictive models attempt at forecasting...
Read more
In this hands-on project, we will analyze the change in temperatures across globe from the 17th century till now and build a multivariate deep learning based time series model to forecast the U.S. Average temperature. Predictive models attempt at forecasting...
Read more
In this hands-on project, we will analyze the change in temperatures across globe from the 17th century till now and build a multivariate deep learning based time series model to forecast the U.S. Average temperature. Predictive models attempt at forecasting future value based on historical data.
Register for this course and see more details by visiting:
OpenCourser.com/course/kx7fac/climate
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Teaches skills and knowledge highly relevant to industry, such as time series analysis and deep learning for forecasting
Taught by Ryan Ahmed, who has practical expertise in data science and forecasting
Engages learners in hands-on labs, which is valuable for solidifying concepts and developing practical skills
Suitable for students who have a baseline understanding of data analysis and time series concepts
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Reviews summary
Climate change forecasting with mixed reviews
This course on climate change forecasting using deep learning has mixed reviews. Some students found the content to be helpful and engaging, while others found it to be too basic or lacking in depth. Overall, the course seems to be a good starting point for those interested in learning more about this topic, but students may need to supplement their learning with additional resources.
Helpful content for beginners
"From the scratch"
Basic content
"The project is just a compilation of very basic Jupyter notebooks freely accessible on Kaggle"
Requires further study
"The explanations of the theories and some of the coding sections are too short, therefore it requires a further self study to understand those."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Climate Change Forecasting Using Deep Learning with these
activities:
Practice time series analysis techniques
Show steps
Build a foundation in time series analysis techniques to facilitate understanding of the course material.
Browse courses on
Time Series Analysis
Show steps
-
Review time series basics
-
Complete practice problems on time series decomposition
-
Analyze real-world time series data
Read 'Hands-On Time Series Analysis with Python'
Show steps
Supplement the course material with a practical guide to time series analysis using Python.
Show steps
-
Read chapters on time series basics and forecasting techniques
-
Complete exercises and projects related to the course material
Form a study group with classmates
Show steps
Collaborate with peers to reinforce concepts, discuss course material, and prepare for assessments.
Browse courses on
Time Series Analysis
Show steps
-
Identify classmates with similar interests
-
Set up regular study sessions
-
Discuss course topics, solve problems, and quiz each other
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow tutorials on deep learning for time series
Show steps
Expand knowledge of deep learning techniques for time series analysis, enhancing understanding of the course material.
Browse courses on
Deep Learning
Show steps
-
Identify relevant tutorials on deep learning for time series
-
Follow the tutorials and complete exercises
-
Apply the learned techniques to the course project
Participate in a time series analysis workshop
Show steps
Enhance practical skills in time series analysis through hands-on training and expert guidance.
Browse courses on
Time Series Analysis
Show steps
-
Identify suitable workshops
-
Register and attend the workshop
-
Participate actively in exercises and discussions
Build a multivariate time series model
Show steps
Apply the concepts learned in the course to a practical project, deepening understanding of time series modeling.
Browse courses on
Time Series Modeling
Show steps
-
Select a suitable dataset
-
Preprocess and prepare the data
-
Build and evaluate a multivariate time series model
-
Present findings and insights
Attend industry conferences on time series analysis
Show steps
Connect with professionals in the field and learn about the latest trends and applications of time series analysis.
Browse courses on
Time Series Analysis
Show steps
-
Research upcoming industry conferences
-
Register and attend relevant sessions
-
Network with attendees and explore potential collaborations
Participate in a time series forecasting competition
Show steps
Challenge knowledge and skills by applying time series analysis techniques to real-world problems.
Browse courses on
Time Series Forecasting
Show steps
-
Identify relevant competitions
-
Build and submit time series models
-
Evaluate results and compare approaches with others
Practice time series analysis techniques
Show steps
Build a foundation in time series analysis techniques to facilitate understanding of the course material.
Browse courses on
Time Series Analysis
Show steps
Show steps
Show steps
- Review time series basics
- Complete practice problems on time series decomposition
- Analyze real-world time series data
Read 'Hands-On Time Series Analysis with Python'
Show steps
Supplement the course material with a practical guide to time series analysis using Python.
Show steps
Show steps
Show steps
- Read chapters on time series basics and forecasting techniques
- Complete exercises and projects related to the course material
Form a study group with classmates
Show steps
Collaborate with peers to reinforce concepts, discuss course material, and prepare for assessments.
Browse courses on
Time Series Analysis
Show steps
Show steps
Show steps
- Identify classmates with similar interests
- Set up regular study sessions
- Discuss course topics, solve problems, and quiz each other
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow tutorials on deep learning for time series
Show steps
Expand knowledge of deep learning techniques for time series analysis, enhancing understanding of the course material.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Identify relevant tutorials on deep learning for time series
- Follow the tutorials and complete exercises
- Apply the learned techniques to the course project
Participate in a time series analysis workshop
Show steps
Enhance practical skills in time series analysis through hands-on training and expert guidance.
Browse courses on
Time Series Analysis
Show steps
Show steps
Show steps
- Identify suitable workshops
- Register and attend the workshop
- Participate actively in exercises and discussions
Build a multivariate time series model
Show steps
Apply the concepts learned in the course to a practical project, deepening understanding of time series modeling.
Browse courses on
Time Series Modeling
Show steps
Show steps
Show steps
- Select a suitable dataset
- Preprocess and prepare the data
- Build and evaluate a multivariate time series model
- Present findings and insights
Attend industry conferences on time series analysis
Show steps
Connect with professionals in the field and learn about the latest trends and applications of time series analysis.
Browse courses on
Time Series Analysis
Show steps
Show steps
Show steps
- Research upcoming industry conferences
- Register and attend relevant sessions
- Network with attendees and explore potential collaborations
Participate in a time series forecasting competition
Show steps
Challenge knowledge and skills by applying time series analysis techniques to real-world problems.
Browse courses on
Time Series Forecasting
Show steps
Show steps
Show steps
- Identify relevant competitions
- Build and submit time series models
- Evaluate results and compare approaches with others
Career center
Learners who complete Climate Change Forecasting Using Deep Learning will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts combine programming skills with industry knowledge to transform raw data into useful insights for businesses. This course lays the groundwork for this role by teaching you data analysis and interpretation skills.
Data Scientist
Data Scientist
Data Scientists use coding to drive quantitative solutions, which can include building machine learning models. This course provides a solid base for this job.
Climate Data Scientist
Climate Data Scientist
Climate Data Scientists analyze climate data to build models, which can be used to develop strategies for mitigating the effects of climate change. This course offers a path to success in this career by providing foundational knowledge in deep learning and data analysis.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data systems. This course may be useful for this role by providing a strong foundation in data analysis and management.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. This course could help prepare for this role by providing a foundation in deep learning, a subfield of machine learning.
Renewable Energy Project Manager
Renewable Energy Project Manager
Renewable Energy Project Managers oversee the development and implementation of renewable energy projects, such as solar and wind farms. This course can be helpful for this role by providing knowledge of deep learning and data analysis, which allows the understanding of large amounts of data on renewable energy production and optimization.
Energy Analyst
Energy Analyst
Energy Analysts provide insights into energy markets, policies, and technologies. This course can assist in pursuing this career by providing a background in deep learning and data analysis, which can be applied to analyzing energy data.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. This course may be useful for this role by providing a solid foundation in statistical modeling and data analysis.
Climate Policy Analyst
Climate Policy Analyst
Climate Policy Analysts research and analyze climate change policies. This course may be useful for this role by providing knowledge of deep learning and data analysis, which are useful for evaluating climate policies.
Research Scientist
Research Scientist
Research Scientists conduct research on a variety of topics, including climate change. This course could provide a foundation in deep learning and data analysis, which are valuable skills for this role.
Sustainability Consultant
Sustainability Consultant
Sustainability Consultants help organizations improve their sustainability performance. This course may be useful for this role by providing knowledge of deep learning and data analysis, which can be used to analyze and interpret sustainability data.
Climate Modeler
Climate Modeler
Climate Modelers create mathematical models of the Earth's climate system to study past, present, and future climates. This course may be helpful for this role by providing a foundation in data analysis and deep learning.
Environmental Scientist
Environmental Scientist
Environmental Scientists use scientific research to develop solutions for environmental problems. This course may be useful for those interested in this role by providing skills in deep learning and data analysis.
Software Engineer
Software Engineer
Software Engineers develop and maintain software systems. This course may be useful for those interested in this role by providing skills in data analysis, which can be applied to software development.
Environmental Economist
Environmental Economist
Environmental Economists analyze the economic effects of environmental policies. This course may be useful for this role by providing skills in data analysis, which can be used to quantify the economic impacts of environmental issues.
For more career information including salaries, visit:
OpenCourser.com/course/kx7fac/climate
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Climate Change Forecasting Using Deep Learning.
This report provides a comprehensive assessment of the interactions between climate change and the global energy system, including the impacts of climate change on energy production and consumption, and the potential of energy technologies to mitigate climate change.
Save
Provides a collection of essays from leading climate change experts, covering the science, impacts, and policy responses to climate change.
Provides an overview of the impacts of climate change on indigenous peoples, including the social, economic, and cultural consequences.
Provides a powerful and thought-provoking account of the potential consequences of climate change, including the impacts on human society and the natural world.
Provides a classic account of the environmental impacts of human activity, including the potential consequences of climate change.
Provides a detailed account of the potential impacts of climate change at different levels of global warming, including the impacts on human society and the natural world.
Provides a concise and accessible overview of the science of climate change, including the physical basis, impacts, and mitigation strategies.
Save
Provides a critical analysis of the political and economic challenges of climate change mitigation, and argues that the failure of democracy major obstacle to effective action.
Provides an overview of the social justice implications of climate change, including the disproportionate impacts on marginalized communities.
Save
Provides a broad overview of the challenges facing life on Earth, including climate change, biodiversity loss, and pollution.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/kx7fac/climate
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Time
1 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Ryan Ahmed
Good to know
Teaches skills and knowledge highly relevant to industry, such as time series analysis and deep learning for forecasting
Taught by Ryan Ahmed, who has practical expertise in data science and forecasting
Engages learners in hands-on labs, which is valuable for solidifying concepts and developing practical skills
Suitable for students who have a baseline understanding of data analysis and time series concepts
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