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Leire Ahedo
Este proyecto es un curso práctico y efectivo para aprender Data Science de manera práctica y aplicada. Aprenderemos desde cero todo el proceso y fases del data science, desarrollando un proyecto práctico de cada una de estas fases.
Gracias a ello aprenderás...
Read more
Este proyecto es un curso práctico y efectivo para aprender Data Science de manera práctica y aplicada. Aprenderemos desde cero todo el proceso y fases del data science, desarrollando un proyecto práctico de cada una de estas fases.
Gracias a ello aprenderás...
Read more
Este proyecto es un curso práctico y efectivo para aprender Data Science de manera práctica y aplicada. Aprenderemos desde cero todo el proceso y fases del data science, desarrollando un proyecto práctico de cada una de estas fases.
Gracias a ello aprenderás a desarrollar un modelo completo de Machine learning, desde el pre-procesamiento de datos hasta la validación del modelo.
Register for this course and see more details by visiting:
OpenCourser.com/course/y7ekyx/curso
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what to watch for , and
possible dealbreakers
Learners are expected to have proficiency in Python and R
Taught by Leire Ahedo, who is an instructor and researcher in data science
Suited for intermediate to advanced learners looking to strengthen skills and knowledge
Teaches essential skills for data science, including data preprocessing, model development, and validation
Emphasizes practical application, with learners developing a complete machine learning model
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Reviews summary
Practical data science course
This course is a hands-on introduction to Data Science. You will learn the entire process of data science, including data preprocessing, model development, and model validation. This course received an average rating of 3 out of 5.
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 Curso Completo de Data Science with these
activities:
Read 'Data Science for Business' by Provost and Fawcett
Show steps
Reading 'Data Science for Business' will provide you with a comprehensive overview of the field and its applications in the business world.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
-
Read the book thoroughly and take notes.
-
Identify key concepts and ideas.
-
Apply the concepts to real-world business scenarios.
Review Python programming fundamentals
Show steps
Reviewing Python programming fundamentals will refresh your knowledge and ensure you have a solid foundation before starting this course.
Browse courses on
Python
Show steps
-
Review variables, data types, and operators.
-
Practice writing simple Python scripts.
-
Complete online tutorials or exercises on Python basics.
Participate in study groups or discussion forums
Show steps
Participating in study groups or discussion forums will allow you to connect with other students, share knowledge, and get feedback on your work.
Show steps
-
Find study groups or discussion forums related to this course.
-
Actively participate in discussions and ask questions.
-
Share your own insights and knowledge with others.
Three other activities
Expand to see all activities and additional details
Show all six activities
Follow tutorials on data preprocessing and machine learning
Show steps
Following tutorials on data preprocessing and machine learning will provide you with practical experience and reinforce your understanding of the concepts covered in this course.
Browse courses on
Data Preprocessing
Show steps
-
Find tutorials that cover specific techniques or algorithms relevant to this course.
-
Follow the tutorials step-by-step and implement the techniques in your own code.
-
Experiment with different parameters and datasets to see how they affect the results.
Develop a data science project from scratch
Show steps
Developing a data science project from scratch will allow you to apply the skills and knowledge you learn in this course to a real-world problem.
Browse courses on
Data Science Project
Show steps
-
Identify a problem or challenge that you want to solve using data.
-
Collect and prepare the necessary data.
-
Build and train a model to solve the problem.
-
Evaluate the performance of your model and make improvements.
-
Present your findings and insights.
Contribute to open-source projects in data science
Show steps
Contributing to open-source projects in data science will allow you to gain practical experience and contribute to the community.
Show steps
-
Find open-source projects that are related to your interests.
-
Identify ways to contribute to the project, such as bug fixes, feature enhancements, or documentation improvements.
-
Submit your contributions to the project and get feedback from the community.
Read 'Data Science for Business' by Provost and Fawcett
Show steps
Reading 'Data Science for Business' will provide you with a comprehensive overview of the field and its applications in the business world.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
Show steps
Show steps
- Read the book thoroughly and take notes.
- Identify key concepts and ideas.
- Apply the concepts to real-world business scenarios.
Review Python programming fundamentals
Show steps
Reviewing Python programming fundamentals will refresh your knowledge and ensure you have a solid foundation before starting this course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review variables, data types, and operators.
- Practice writing simple Python scripts.
- Complete online tutorials or exercises on Python basics.
Participate in study groups or discussion forums
Show steps
Participating in study groups or discussion forums will allow you to connect with other students, share knowledge, and get feedback on your work.
Show steps
Show steps
Show steps
- Find study groups or discussion forums related to this course.
- Actively participate in discussions and ask questions.
- Share your own insights and knowledge with others.
Three other activities
Expand to see all activities and additional details
Show all six activities
Follow tutorials on data preprocessing and machine learning
Show steps
Following tutorials on data preprocessing and machine learning will provide you with practical experience and reinforce your understanding of the concepts covered in this course.
Browse courses on
Data Preprocessing
Show steps
Show steps
Show steps
- Find tutorials that cover specific techniques or algorithms relevant to this course.
- Follow the tutorials step-by-step and implement the techniques in your own code.
- Experiment with different parameters and datasets to see how they affect the results.
Develop a data science project from scratch
Show steps
Developing a data science project from scratch will allow you to apply the skills and knowledge you learn in this course to a real-world problem.
Browse courses on
Data Science Project
Show steps
Show steps
Show steps
- Identify a problem or challenge that you want to solve using data.
- Collect and prepare the necessary data.
- Build and train a model to solve the problem.
- Evaluate the performance of your model and make improvements.
- Present your findings and insights.
Contribute to open-source projects in data science
Show steps
Contributing to open-source projects in data science will allow you to gain practical experience and contribute to the community.
Show steps
Show steps
Show steps
- Find open-source projects that are related to your interests.
- Identify ways to contribute to the project, such as bug fixes, feature enhancements, or documentation improvements.
- Submit your contributions to the project and get feedback from the community.
Career center
Learners who complete Curso Completo de Data Science will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
As a Data Scientist, your goal will be to draw actionable insights from organizational data and build innovative AI solutions to business problems. This course will help build a foundation for your journey to become a successful Data Scientist, as it covers the entire data science process and every stage involved in developing a Machine Learning model.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain the Machine Learning models that Data Scientists create. This course may help you develop the skills to become a Machine Learning Engineer as it covers the complete process of developing a Machine Learning model, including data pre-processing and model validation.
Data Analyst
Data Analyst
As a Data Analyst, your role will be to collect, analyze, interpret, and present data. This course can be useful as it covers the entire data science process and will build a strong foundation for your career as a Data Analyst.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. This course may be of use to you, as it covers the entire data science process and will help you develop foundational skills for your career as a Statistician.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make better decisions. This course can be useful, as it provides a comprehensive overview of the data science process and will help you develop foundational skills for your career as a Business Analyst.
Data Engineer
Data Engineer
Data Engineers design and build the infrastructure that allows Data Scientists and Data Analysts to access and process data. This course may be of use to you, as it covers the entire data science process and will help you build foundational skills for your career as a Data Engineer.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course may be of use to you, as it covers the entire data science process and will help you gain an understanding of the data-driven approaches used in modern software development.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve business problems. This course may be useful to you, as it covers the entire data science process and will help you build a foundation for your career as an Operations Research Analyst.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. This course may help build a foundation for your career as a Quantitative Analyst, as it covers the entire data science process and will help you develop the skills needed.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk. This course may build a foundation for your career as an Actuary as it covers the entire data science process and will help you develop the skills needed.
Data Journalist
Data Journalist
Data Journalists use data to tell stories and explain complex issues. This course may be useful to you, as it covers the entire data science process and will help you develop the skills needed.
Market Researcher
Market Researcher
Market Researchers collect, analyze, and interpret data to help businesses understand their customers. This course may build a foundation for your career as a Market Researcher as it covers the entire data science process and will help you develop analytical skills.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment recommendations. This course may be helpful as it covers the entire data science process and will help you develop the skills needed.
Risk Analyst
Risk Analyst
Risk Analysts use data to evaluate and mitigate risk. This course may help build a foundation for your career as a Risk Analyst as it covers the entire data science process and will help you develop analytical skills.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to help businesses make better decisions. This course may be useful as it covers the entire data science process and will help you develop the skills needed.
For more career information including salaries, visit:
OpenCourser.com/course/y7ekyx/curso
Reading list
We've selected 11 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
Curso Completo de Data Science.
Provides a comprehensive overview of the field of data science, including its history, concepts, and applications.
Explores the applications of data science in business, including customer segmentation, fraud detection, and risk assessment.
Save
Covers the use of R for data science tasks, including data manipulation, visualization, and machine learning.
Provides a hands-on introduction to data science using Python, covering data cleaning, feature engineering, and modeling.
Save
Covers the fundamentals of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks.
Covers the use of Python for natural language processing tasks, including text preprocessing, feature extraction, and machine learning.
Save
Provides a practical guide to data visualization, covering the principles of visual perception and the use of data visualization tools.
Provides a gentle introduction to machine learning, covering supervised learning, unsupervised learning, and reinforcement learning.
Provides a beginner-friendly introduction to data science, covering the basics of data science and its applications.
Covers the use of Python for data science tasks, including data manipulation, visualization, and machine learning.
Save
Covers the use of R for data science tasks, including data manipulation, visualization, and machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/y7ekyx/curso
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Leire Ahedo
Good to know
Learners are expected to have proficiency in Python and R
Taught by Leire Ahedo, who is an instructor and researcher in data science
Suited for intermediate to advanced learners looking to strengthen skills and knowledge
Teaches essential skills for data science, including data preprocessing, model development, and validation
Emphasizes practical application, with learners developing a complete machine learning model
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