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Guillermo Barrios del Valle

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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What's inside

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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Proceso ETL y EDA
Certificado con honores

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Explora la extracción, transformación y carga de datos, temas esenciales en ciencia de datos
Desarrolla habilidades de exploración de datos utilizando bibliotecas como Pandas y Numpy
Fortalece las bases en la visualización de datos con Matplotlib
Introduce prácticas recomendadas para el flujo de trabajo en ciencia de datos
Brinda un certificado con honores por completar el curso exitosamente

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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 Python: de usuario a explorador de datos with these activities:
Revise Concepts in Data Analysis
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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  • 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
This book provides a comprehensive overview of data science concepts and will help build a strong foundation for the course.
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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
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.
  • Meet regularly to discuss course material, share insights, and work on assignments together.
Four other activities
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Follow Tutorials on Matplotlib for Data Visualization
Guided tutorials will provide step-by-step instructions to create effective data visualizations.
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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
Regular practice with data manipulation exercises will enhance your proficiency in using 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
Creating a data exploration report will provide hands-on experience in applying course concepts.
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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
Attending industry events will expose you to real-world applications and connect you with professionals in the field.
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  • Search for upcoming data science workshops or conferences in your area.
  • Register and actively participate in sessions to expand your knowledge and network.

Career center

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