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Nestor Nicolas Campos Rojas
En este curso basado en un proyecto y de 1 hora de duración, aprenderás sobre Dask y la importancia de usarlo en proyectos de Big Data para grandes análisis de datos en procesamiento paralelo.
Register for this course and see more details by visiting:
OpenCourser.com/course/4vvsjh/trabajando
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Learners wanting to strengthen their foundation in big data may find this course to be useful
This course's one-hour length makes it well-suited as an introduction to Dask and its use in data analysis for big data
Save this course
Save Trabajando con Dask to your list so you can find it easily later:
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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 Trabajando con Dask with these
activities:
Read the Dask documentation
Show steps
Review the different concepts and functionalities of Dask to reinforce your understanding of the course material.
Browse courses on
Dask
Show steps
-
Visit the official Dask website.
-
Read through the documentation.
-
Follow along with the provided tutorials.
Work through Dask exercises
Show steps
Apply your knowledge of Dask by working through a series of exercises to solidify your understanding.
Browse courses on
Dask
Show steps
-
Find exercises online or in the course materials.
-
Work through the exercises, referring to the Dask documentation when needed.
-
Debug any errors you encounter.
Join a Dask study group
Show steps
Engage with other students and collaborate on Dask-related projects to enhance your learning experience.
Browse courses on
Dask
Show steps
-
Find a Dask study group or create your own.
-
Attend regular meetings.
-
Participate in discussions.
-
Work on projects together.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Attend a Dask workshop
Show steps
Enhance your Dask skills by attending a workshop led by experienced practitioners.
Browse courses on
Dask
Show steps
-
Find a Dask workshop.
-
Register for the workshop.
-
Attend the workshop.
-
Participate in discussions.
-
Complete any assignments or exercises.
Practice Dask's parallel processing functions
Show steps
Practice using Dask's parallel processing functions to improve your understanding of how Dask works.
Browse courses on
Dask
Show steps
-
Create a Dask client
-
Create a Dask dataframe
-
Apply a function to the dataframe in parallel
-
Visualize the results
Write a blog post about Dask
Show steps
Demonstrate your understanding of Dask by writing a blog post explaining the concepts and sharing your experiences with others.
Browse courses on
Dask
Show steps
-
Choose a specific topic related to Dask.
-
Research the topic thoroughly.
-
Write a draft of your blog post.
-
Edit and refine your post.
-
Publish your blog post.
Develop a Dask application
Show steps
Apply your Dask knowledge by building a real-world application that addresses a specific data analysis problem.
Browse courses on
Dask
Show steps
-
Identify a problem that can be solved using Dask.
-
Design and implement your application.
-
Test and debug your application.
-
Deploy your application.
Create a tutorial on using Dask for a specific project
Show steps
Create a tutorial that demonstrates how to use Dask to solve a specific problem. This will help you to solidify your understanding of Dask and its applications.
Browse courses on
Dask
Show steps
-
Choose a project that you want to work on
-
Set up a Dask cluster
-
Write your tutorial
-
Publish your tutorial
Contribute to the Dask project
Show steps
Deepen your understanding of Dask by contributing to its open-source codebase, enhancing your skills and benefiting the community.
Browse courses on
Dask
Show steps
-
Find an area in the Dask project that interests you.
-
Read the Dask contribution guidelines.
-
Make a code contribution.
-
Submit a pull request.
-
Respond to feedback and make revisions.
Read the Dask documentation
Show steps
Review the different concepts and functionalities of Dask to reinforce your understanding of the course material.
Browse courses on
Dask
Show steps
Show steps
Show steps
- Visit the official Dask website.
- Read through the documentation.
- Follow along with the provided tutorials.
Work through Dask exercises
Show steps
Apply your knowledge of Dask by working through a series of exercises to solidify your understanding.
Browse courses on
Dask
Show steps
Show steps
Show steps
- Find exercises online or in the course materials.
- Work through the exercises, referring to the Dask documentation when needed.
- Debug any errors you encounter.
Join a Dask study group
Show steps
Engage with other students and collaborate on Dask-related projects to enhance your learning experience.
Browse courses on
Dask
Show steps
Show steps
Show steps
- Find a Dask study group or create your own.
- Attend regular meetings.
- Participate in discussions.
- Work on projects together.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Attend a Dask workshop
Show steps
Enhance your Dask skills by attending a workshop led by experienced practitioners.
Browse courses on
Dask
Show steps
Show steps
Show steps
- Find a Dask workshop.
- Register for the workshop.
- Attend the workshop.
- Participate in discussions.
- Complete any assignments or exercises.
Practice Dask's parallel processing functions
Show steps
Practice using Dask's parallel processing functions to improve your understanding of how Dask works.
Browse courses on
Dask
Show steps
Show steps
Show steps
- Create a Dask client
- Create a Dask dataframe
- Apply a function to the dataframe in parallel
- Visualize the results
Write a blog post about Dask
Show steps
Demonstrate your understanding of Dask by writing a blog post explaining the concepts and sharing your experiences with others.
Browse courses on
Dask
Show steps
Show steps
Show steps
- Choose a specific topic related to Dask.
- Research the topic thoroughly.
- Write a draft of your blog post.
- Edit and refine your post.
- Publish your blog post.
Develop a Dask application
Show steps
Apply your Dask knowledge by building a real-world application that addresses a specific data analysis problem.
Browse courses on
Dask
Show steps
Show steps
Show steps
- Identify a problem that can be solved using Dask.
- Design and implement your application.
- Test and debug your application.
- Deploy your application.
Create a tutorial on using Dask for a specific project
Show steps
Create a tutorial that demonstrates how to use Dask to solve a specific problem. This will help you to solidify your understanding of Dask and its applications.
Browse courses on
Dask
Show steps
Show steps
Show steps
- Choose a project that you want to work on
- Set up a Dask cluster
- Write your tutorial
- Publish your tutorial
Contribute to the Dask project
Show steps
Deepen your understanding of Dask by contributing to its open-source codebase, enhancing your skills and benefiting the community.
Browse courses on
Dask
Show steps
Show steps
Show steps
- Find an area in the Dask project that interests you.
- Read the Dask contribution guidelines.
- Make a code contribution.
- Submit a pull request.
- Respond to feedback and make revisions.
Career center
Learners who complete Trabajando con Dask will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists analyze and interpret large datasets to uncover valuable insights. Knowledge of Dask can significantly enhance your productivity, enabling you to process and analyze massive datasets more efficiently. This course will provide you with the necessary skills to leverage Dask and streamline your data analysis workflow.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models to solve complex problems. Dask is particularly valuable in this field, as it allows you to train and evaluate models on large datasets efficiently. Taking this course can equip you with the skills to leverage Dask and enhance your capabilities as a Machine Learning Engineer.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. Understanding how to use Dask can significantly improve your efficiency when working with large datasets. This course will help you gain proficiency in Dask, enabling you to enhance your data analysis skills and become a more effective Data Analyst.
Data Engineer
Data Engineer
Data Engineers design, construct, and maintain the infrastructure used to store and process large datasets. As datasets grow increasingly larger, working with frameworks like Dask is crucial to ensuring analytical pipelines remain efficient and reliable. This course can help you build a foundation in Dask, providing you with a competitive edge in the field of Data Engineering.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. Dask is becoming increasingly prevalent in software development, particularly for projects involving large-scale data processing. Taking this course can provide you with an edge by equipping you with the skills to leverage Dask and create robust software solutions.
For more career information including salaries, visit:
OpenCourser.com/course/4vvsjh/trabajando
Reading list
We've selected 14 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
Trabajando con Dask.
Save
Provides a comprehensive overview of deep learning concepts and algorithms. It covers various topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. Reading this book would be beneficial for learners who want to gain a deep understanding of deep learning.
Provides a comprehensive overview of statistical learning methods. It covers various topics such as linear regression, logistic regression, and decision trees. Reading this book would be beneficial for learners who want to gain a deep understanding of statistical learning.
Provides a comprehensive overview of pattern recognition and machine learning algorithms. It covers various topics such as supervised learning, unsupervised learning, and Bayesian inference. Reading this book would be beneficial for learners who want to gain a deep understanding of pattern recognition and machine learning.
Covers various machine learning algorithms and techniques using Python. It provides a comprehensive overview of machine learning concepts, tools, and applications. Reading this book would be beneficial for learners who want to gain a solid understanding of machine learning.
Provides a comprehensive introduction to Python for data analysis. It covers essential concepts, tools, and techniques for working with data in Python. Reading this book would be helpful for learners who want to gain a strong foundation in Python for data analysis.
Covers various techniques for writing high-performance Python code. It starts with basics like profiling and optimization and delves into concurrency and parallelism. It also covers Numba, Cython, and Dask, which could help learners to explore further the topics covered in the course.
Provides a comprehensive introduction to data visualization using Python. It covers various visualization techniques and tools for creating effective data visualizations. Reading this book would be beneficial for learners who want to gain practical experience with data visualization.
Provides a hands-on approach to machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It covers various machine learning algorithms and techniques through practical examples. Reading this book would be beneficial for learners who want to gain practical experience with machine learning.
Covers various big data analytics techniques and tools using Python. It provides a comprehensive overview of big data concepts, architectures, and applications. Reading this book would be beneficial for learners who want to gain a solid understanding of big data analytics.
Covers various aspects of data analysis using Pandas. It provides a hands-on approach to data manipulation, cleaning, and analysis. Reading this book would be beneficial for learners who want to gain practical experience with Pandas, which commonly used library for data manipulation and analysis in Python.
Covers various machine learning algorithms and techniques using Python. It provides a practical approach to building and evaluating machine learning models. Reading this book would be beneficial for learners who want to gain hands-on experience with machine learning.
Provides a comprehensive introduction to deep learning using Python. It covers essential concepts, architectures, and applications of deep learning. Reading this book would be beneficial for learners who want to gain a solid understanding of deep learning.
Covers various natural language processing (NLP) techniques and algorithms using Python. It provides a hands-on approach to NLP tasks such as text preprocessing, feature extraction, and classification. Reading this book would be beneficial for learners who want to gain practical experience with NLP.
Provides a comprehensive introduction to data science concepts and techniques. It covers data wrangling, analysis, visualization, and modeling. Reading this book would be helpful for learners who want to gain a broader understanding of data science and its applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/4vvsjh/trabajando
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Time
1 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Nestor Nicolas Campos Rojas
Good to know
Learners wanting to strengthen their foundation in big data may find this course to be useful
This course's one-hour length makes it well-suited as an introduction to Dask and its use in data analysis for big data
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