En este curso adquirirás los fundamentos de la extracción, transformación y carga (ETL) de datos, así como la exploración de datos en la ciencia de datos. Aprenderás a trabajar con Pandas y dataframes, a leer archivos CSV y XLSX, y a comprender los conceptos de series temporales. Además, aprenderás la visualización de datos utilizando Matplotlib, creando gráficas individuales y personalizadas. También utilizarás NumPy y la manipulación de arrays, realizando las operaciones básicas y calculando gradientes. A lo largo del curso, desarrollarás buenas prácticas en el flujo de trabajo en ciencia de datos y en los conceptos avanzados como multi-índices en series temporales. Finalmente, sabrás como usar paquetes de manera local.
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Syllabus
Fundamentos de extracción, transformación y carga de datos y exploración de datos
Introducción a matplotlib
Manejo de arreglos con Numpy
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Activities
Revise Concepts in Data Analysis
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Brushing up on data analysis concepts before the course begins will provide a strong foundation and facilitate a smoother learning experience.
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Data Analysis
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Review basic statistics concepts, such as measures of central tendency and dispersion.
-
Go through examples of data exploration and visualization techniques.
Read 'Introduction to Data Science' by Rafael López
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This book provides a comprehensive overview of data science concepts and will help build a strong foundation for the course.
View
Investigaciones en gestión del riesgo de...
on Amazon
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-
Read the first three chapters to gain an understanding of the fundamental concepts.
-
Work through the exercises at the end of each chapter to reinforce your understanding.
Join a Study Group for Course Discussions
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Engaging in peer discussions will enhance understanding through different perspectives and collaborative learning.
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Find or create a study group with fellow students taking the course.
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Meet regularly to discuss course material, share insights, and work on assignments together.
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Follow Tutorials on Matplotlib for Data Visualization
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Guided tutorials will provide step-by-step instructions to create effective data visualizations.
Browse courses on
Matplotlib
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-
Search for tutorials on creating line charts, bar charts, and scatterplots using Matplotlib.
-
Follow the tutorials and create visualizations based on sample datasets.
Complete Data Manipulation Exercises with Pandas
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Regular practice with data manipulation exercises will enhance your proficiency in using Pandas.
Browse courses on
Pandas
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-
Find online exercises or use the Pandas documentation to practice data manipulation techniques.
-
Set aside specific time each week to work on these exercises.
Develop a Data Exploration Report
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Creating a data exploration report will provide hands-on experience in applying course concepts.
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Data Exploration
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-
Choose a dataset and explore it using techniques learned in the course.
-
Create visualizations and summarize the key insights from your exploration.
Attend a Data Science Workshop or Conference
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Attending industry events will expose you to real-world applications and connect you with professionals in the field.
Show steps
-
Search for upcoming data science workshops or conferences in your area.
-
Register and actively participate in sessions to expand your knowledge and network.
Revise Concepts in Data Analysis
Show steps
Brushing up on data analysis concepts before the course begins will provide a strong foundation and facilitate a smoother learning experience.
Browse courses on
Data Analysis
Show steps
- Review basic statistics concepts, such as measures of central tendency and dispersion.
- Go through examples of data exploration and visualization techniques.
Read 'Introduction to Data Science' by Rafael López
Show steps
This book provides a comprehensive overview of data science concepts and will help build a strong foundation for the course.
View
Investigaciones en gestión del riesgo de...
on Amazon
Show steps
- Read the first three chapters to gain an understanding of the fundamental concepts.
- Work through the exercises at the end of each chapter to reinforce your understanding.
Join a Study Group for Course Discussions
Show steps
Engaging in peer discussions will enhance understanding through different perspectives and collaborative learning.
Show steps
- Find or create a study group with fellow students taking the course.
- Meet regularly to discuss course material, share insights, and work on assignments together.
Follow Tutorials on Matplotlib for Data Visualization
Show steps
Guided tutorials will provide step-by-step instructions to create effective data visualizations.
Browse courses on
Matplotlib
Show steps
- Search for tutorials on creating line charts, bar charts, and scatterplots using Matplotlib.
- Follow the tutorials and create visualizations based on sample datasets.
Complete Data Manipulation Exercises with Pandas
Show steps
Regular practice with data manipulation exercises will enhance your proficiency in using Pandas.
Browse courses on
Pandas
Show steps
- Find online exercises or use the Pandas documentation to practice data manipulation techniques.
- Set aside specific time each week to work on these exercises.
Develop a Data Exploration Report
Show steps
Creating a data exploration report will provide hands-on experience in applying course concepts.
Browse courses on
Data Exploration
Show steps
- Choose a dataset and explore it using techniques learned in the course.
- Create visualizations and summarize the key insights from your exploration.
Attend a Data Science Workshop or Conference
Show steps
Attending industry events will expose you to real-world applications and connect you with professionals in the field.
Show steps
- Search for upcoming data science workshops or conferences in your area.
- Register and actively participate in sessions to expand your knowledge and network.
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