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Juan Pablo Yepes
En este proyecto guiado obtendrás experiencia práctica trabajando con la librería Pandas y creando tu propio cuaderno de Jupyter Lab. Los conocimientos básicos que obtengas te permitirán trabajar con cualquier base de datos para analizar la información. Al...
Read more
En este proyecto guiado obtendrás experiencia práctica trabajando con la librería Pandas y creando tu propio cuaderno de Jupyter Lab. Los conocimientos básicos que obtengas te permitirán trabajar con cualquier base de datos para analizar la información. Al...
Read more
En este proyecto guiado obtendrás experiencia práctica trabajando con la librería Pandas y creando tu propio cuaderno de Jupyter Lab. Los conocimientos básicos que obtengas te permitirán trabajar con cualquier base de datos para analizar la información. Al final de este proyecto serás capaz de crear tus propios cuadernos con análisis estadísticos de diferentes bases de datos.
Nota: Este curso está dirigido a personas que buscan iniciarse en el mundo de la ciencia de datos o el machine learning.
Register for this course and see more details by visiting:
OpenCourser.com/course/tfjd1o/introduccion
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possible dealbreakers
Introduces fundamental data analysis concepts and tools, which is a core competency for data scientists and machine learning engineers
Taught by Juan Pablo Yepes, who is an experienced data scientist and instructor
Provides hands-on experience with the Pandas library, an essential skill for data manipulation and analysis
Develops a strong foundation in data analysis principles, which can be applied to various industries and fields
Enhances learner's ability to critically analyze data and extract meaningful insights
Requires no prior data analysis experience, making it accessible to beginners
Save this course
Save Introducción a Pandas para Data Science to your list so you can find it easily later:
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Reviews summary
Beginner-friendly pandas intro
This course did not leave a good impression on learners, who found it poorly executed.
Learners who left feedback indicated that the course's poor content and mediocre instruction were its main flaws. Some reviewers mentioned that the content felt incomplete, potentially leaving learners dissatisfied with the overall experience.
With more examples, better explanations, and clearer content, this course could improve significantly.
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 Introducción a Pandas para Data Science with these
activities:
Python Refresher
Show steps
Strengthen your Python foundation to ensure a smooth transition into the Pandas course.
Browse courses on
Python Basics
Show steps
-
Review basic Python syntax and data types.
-
Practice writing simple Python programs.
-
Complete online tutorials or exercises on Python fundamentals.
Jupyter Notebook Experimentation
Show steps
Become familiar with Jupyter Notebooks and develop your data exploration skills by experimenting with different datasets.
Browse courses on
Jupyter Notebooks
Show steps
-
Create a new Jupyter Notebook.
-
Load and explore various datasets using Pandas.
-
Visualize the data using Matplotlib or Seaborn.
-
Save and share your notebook with peers or instructors for feedback.
Pandas Study Group
Show steps
Enhance your understanding and application of Pandas concepts through collaborative learning with peers.
Show steps
-
Form a study group with 2-3 classmates.
-
Meet regularly to discuss course materials, solve problems, and share insights.
-
Work together on a small Pandas project or analysis.
Two other activities
Expand to see all activities and additional details
Show all five activities
Intermediate Pandas Tutorial
Show steps
Enhance your proficiency in Pandas by following an online tutorial that covers advanced techniques and best practices.
Show steps
-
Enroll in an intermediate Pandas tutorial on Coursera, Udemy, or edX.
-
Complete the tutorial videos and exercises thoroughly.
Kaggle Pandas Competition
Show steps
Challenge yourself and apply your Pandas skills in a competitive environment by participating in a Kaggle competition.
Show steps
-
Identify a suitable Kaggle competition that involves Pandas usage.
-
Download the competition dataset and explore it.
-
Develop a Pandas-based solution to the competition problem.
-
Submit your solution and track your progress on the leaderboard.
Python Refresher
Show steps
Strengthen your Python foundation to ensure a smooth transition into the Pandas course.
Browse courses on
Python Basics
Show steps
Show steps
Show steps
- Review basic Python syntax and data types.
- Practice writing simple Python programs.
- Complete online tutorials or exercises on Python fundamentals.
Jupyter Notebook Experimentation
Show steps
Become familiar with Jupyter Notebooks and develop your data exploration skills by experimenting with different datasets.
Browse courses on
Jupyter Notebooks
Show steps
Show steps
Show steps
- Create a new Jupyter Notebook.
- Load and explore various datasets using Pandas.
- Visualize the data using Matplotlib or Seaborn.
- Save and share your notebook with peers or instructors for feedback.
Pandas Study Group
Show steps
Enhance your understanding and application of Pandas concepts through collaborative learning with peers.
Show steps
Show steps
Show steps
- Form a study group with 2-3 classmates.
- Meet regularly to discuss course materials, solve problems, and share insights.
- Work together on a small Pandas project or analysis.
Two other activities
Expand to see all activities and additional details
Show all five activities
Intermediate Pandas Tutorial
Show steps
Enhance your proficiency in Pandas by following an online tutorial that covers advanced techniques and best practices.
Show steps
Show steps
Show steps
- Enroll in an intermediate Pandas tutorial on Coursera, Udemy, or edX.
- Complete the tutorial videos and exercises thoroughly.
Kaggle Pandas Competition
Show steps
Challenge yourself and apply your Pandas skills in a competitive environment by participating in a Kaggle competition.
Show steps
Show steps
Show steps
- Identify a suitable Kaggle competition that involves Pandas usage.
- Download the competition dataset and explore it.
- Develop a Pandas-based solution to the competition problem.
- Submit your solution and track your progress on the leaderboard.
Career center
Learners who complete Introducción a Pandas para Data Science will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data. This course helps build a foundation for working with data, performing analysis, and creating visualizations. These are all skills that are essential for a Data Scientist.
Statistician
Statistician
A Statistician collects, analyzes, interprets, and presents data. This course helps build a foundation for working with data, performing analysis, and creating visualizations. These are all skills that are essential for a Statistician.
Data Visualization Specialist
Data Visualization Specialist
A Data Visualization Specialist creates visual representations of data to help people understand it. This course helps build a foundation for working with data, performing analysis, and creating visualizations. These are all skills that are essential for a Data Visualization Specialist.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and maintains machine learning models. This course helps build a foundation for working with data, performing analysis, and creating visualizations. These are all skills that are essential for a Machine Learning Engineer.
Data Journalist
Data Journalist
A Data Journalist uses data to tell stories and inform the public. This course helps build a foundation for working with data, performing analysis, and creating visualizations. These are all skills that are essential for a Data Journalist.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses data to solve problems and improve efficiency in organizations. This course helps build a foundation for working with data, performing analysis, and creating visualizations. These are all skills that are essential for an Operations Research Analyst.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data pipelines and systems. This course helps build a foundation for working with data, performing analysis, and creating visualizations. These are all skills that are essential for a Data Engineer.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst develops and applies mathematical and statistical models to financial data to identify investment opportunities. This course helps build a foundation for working with data, performing analysis, and creating visualizations. These are all skills that are essential for a Quantitative Analyst.
Market Researcher
Market Researcher
A Market Researcher collects, analyzes, and interprets data to help businesses understand their customers. This course helps build a foundation for working with data, performing analysis, and creating visualizations. These are all skills that are essential for a Market Researcher.
Business Analyst
Business Analyst
A Business Analyst uses data to identify opportunities and solve problems for businesses. This course helps build a foundation for working with data, performing analysis, and creating visualizations. These are all skills that are essential for a Business Analyst.
Epidemiologist
Epidemiologist
An Epidemiologist studies the distribution and patterns of health-related states or events in a population. This course helps build a foundation for working with data, performing analysis, and creating visualizations. These are all skills that are essential for an Epidemiologist.
Financial Analyst
Financial Analyst
A Financial Analyst uses data to evaluate investments and make recommendations to clients. This course helps build a foundation for working with data, performing analysis, and creating visualizations. These are all skills that are essential for a Financial Analyst.
Biostatistician
Biostatistician
A Biostatistician uses statistics to solve problems in the field of biology. This course helps build a foundation for working with data, performing analysis, and creating visualizations. These are all skills that are essential for a Biostatistician.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course helps build a foundation for working with data, performing analysis, and creating visualizations. These are all skills that can be useful for a Software Engineer, particularly those who work on data-intensive applications.
Data Analyst
Data Analyst
A Data Analyst collects, processes, and analyzes data. They then convey the findings to stakeholders in a clear and concise way. This course, "Introduction to Pandas for Data Science", may be useful for this role as it helps build a foundation for working with data, performing analysis, and creating visualizations.
For more career information including salaries, visit:
OpenCourser.com/course/tfjd1o/introduccion
Reading list
We've selected six 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
Introducción a Pandas para Data Science.
Provides a comprehensive introduction to Python for data analysis, covering topics such as data cleaning, data manipulation, and data visualization. It valuable resource for beginners who want to learn the basics of data analysis with Python.
Provides a concise and practical introduction to Pandas, covering topics such as data structures, data manipulation, and data analysis. It good choice for beginners who want to learn the basics of Pandas quickly and easily.
Provides a comprehensive introduction to data science, covering topics such as data cleaning, data manipulation, and data analysis. It valuable resource for beginners who want to learn the basics of data science.
Provides a comprehensive introduction to machine learning with Python, covering topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for beginners who want to learn the basics of machine learning with Python.
Provides a comprehensive introduction to machine learning with Python, covering topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for beginners who want to learn the basics of machine learning with Python.
Provides a comprehensive introduction to deep learning with Python, covering topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for beginners who want to learn the basics of deep learning with Python.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/tfjd1o/introduccion
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Time
1 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Juan Pablo Yepes
Good to know
Introduces fundamental data analysis concepts and tools, which is a core competency for data scientists and machine learning engineers
Taught by Juan Pablo Yepes, who is an experienced data scientist and instructor
Provides hands-on experience with the Pandas library, an essential skill for data manipulation and analysis
Develops a strong foundation in data analysis principles, which can be applied to various industries and fields
Enhances learner's ability to critically analyze data and extract meaningful insights
Requires no prior data analysis experience, making it accessible to beginners
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