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Ciclo completo del desarrollo de un proyecto de Data Science

En este curso aprenderemos a desarrollar el ciclo completo de la ciencia de datos, desarrollando en profundidad la parte del despliegue del modelo. Para ello utilizaremos tecnologías como Python, Flask, Postman, heroku, Scikit-learn o anaconda.

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What's inside

Syllabus

Visión general del proyecto

En este proyecto aprenderás a desarrollar el ciclo completo de un proyecto de Data Science, desde el desarrollo del modelo hasta el desarrollo de la aplicación y despliegue en un servidor

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Know what's good

, what to watch for

, and possible dealbreakers

Está dirigido a personas que buscan desarrollar un proyecto de Data Science completo, desde el modelado hasta el despliegue

Requiere conocimientos previos en Python, Flask, Postman, heroku, Scikit-learn y anaconda

Proporciona una visión general del ciclo de vida completo de un proyecto de Ciencia de Datos

Desarrolla habilidades en el despliegue de modelos, lo cual es esencial para aplicaciones prácticas de Ciencia de Datos

Utiliza tecnologías y herramientas actuales de la industria, asegurando la relevancia para el mercado laboral

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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 Ciclo completo del desarrollo de un proyecto de Data Science with these activities:

Repasa los conceptos básicos de Ciencia de Datos

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Repasar los conceptos básicos de Ciencia de Datos, como la limpieza y preparación de datos, el modelado y la evaluación, te ayudará a refrescar tus conocimientos y a prepararte para el curso con mayor facilidad.

Browse courses on Python

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Revisa tus notas de cursos anteriores de Ciencia de Datos o materiales de estudio autodidacta.
Completa ejercicios de práctica o problemas de muestra para poner a prueba tu comprensión.

Repasa tus habilidades de programación en Python

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Repasar tus habilidades de programación en Python te ayudará a refrescar tu comprensión de la sintaxis y las estructuras de datos, lo que te será útil para las tareas de programación del curso.

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Revisa tus notas o materiales de estudio anteriores sobre programación en Python.
Completa ejercicios de codificación o problemas de muestra para practicar la escritura de código en Python.

Explora tutoriales sobre Flask y Postman

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Familiarizarte con Flask y Postman te ayudará a comprender cómo implementar y probar las API que desarrollarás en el curso.

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Busca y completa tutoriales en línea sobre Flask y Postman.
Crea un proyecto de muestra utilizando Flask para construir una API simple.
Utiliza Postman para probar y enviar solicitudes a tu API.

Four other activities

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Participa en discusiones en línea con otros estudiantes

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Participar en discusiones en línea con otros estudiantes te permitirá compartir ideas, hacer preguntas y aprender de las perspectivas de los demás.

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Únete a foros o grupos de estudio en línea relacionados con Ciencia de Datos.
Participa activamente en discusiones, compartiendo tus conocimientos y haciendo preguntas.

Desarrolla un modelo simple de aprendizaje automático usando Scikit-learn

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Crear un modelo simple de aprendizaje automático utilizando Scikit-learn te permitirá aplicar los conceptos que aprenderás en el curso a un proyecto práctico.

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Importa un conjunto de datos y explóralo usando Scikit-learn.
Entrena un modelo de clasificación o regresión utilizando Scikit-learn.
Evalúa el rendimiento de tu modelo utilizando métricas como precisión o F1-score.

Asiste a eventos de networking para conocer profesionales de Ciencia de Datos

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Asistir a eventos de networking te permitirá establecer contactos con profesionales de Ciencia de Datos, aprender sobre oportunidades laborales y obtener información valiosa sobre la industria.

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Busca y asiste a eventos de networking organizados por grupos de Ciencia de Datos o empresas tecnológicas.
Prepárate para presentarte y hablar sobre tus habilidades y experiencia.

Comparte lo que has aprendido creando un blog o una presentación

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Crear un blog o una presentación sobre los conceptos que aprendas te obligará a sintetizar y fortalecer tu comprensión del material del curso.

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Elige un tema específico que hayas aprendido en el curso.
Investiga y recopila información adicional sobre el tema.
Crea un blog o una presentación que explique el tema de forma clara y concisa.

Career center

Learners who complete Ciclo completo del desarrollo de un proyecto de Data Science will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists develop and use statistical models to extract meaningful insights from data. This course perfectly aligns with the role as it provides a comprehensive overview of the full data science lifecycle, including data collection, cleaning, exploration, modeling, and deployment. By taking this course, aspiring Data Scientists can build a solid foundation in the field and enhance their skills in model deployment, an essential aspect of real-world data science projects.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models. This course is highly relevant to this role as it delves into the practical aspects of model deployment, including using Python, Flask, and Heroku. By completing this course, Machine Learning Engineers can expand their knowledge of the full data science pipeline and become more proficient in deploying models into production.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts collect, analyze, and interpret data to help businesses make informed decisions. This course provides a solid foundation for aspiring Data Analysts by introducing them to the data science lifecycle and equipping them with the skills to develop and deploy data analysis solutions. The focus on model deployment in this course is particularly valuable for Data Analysts who want to enhance their ability to communicate insights and recommendations effectively.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts use data to identify and solve business problems. This course can be beneficial for Business Analysts who want to expand their technical skills and gain a deeper understanding of the data science process. By learning about model deployment and using tools like Python and Flask, Business Analysts can become more effective in leveraging data to drive business outcomes.

See salaries and explore the career path for Business Analyst

Software Engineer

Software Engineers design, develop, and maintain software systems. This course may be useful for Software Engineers who want to specialize in data science or machine learning. The focus on model deployment provides valuable insights into the practical aspects of integrating data science models into software applications.

See salaries and explore the career path for Software Engineer

Product Manager

Product Managers are responsible for the development and success of products. This course can provide Product Managers with a foundation in data science principles and practices. By understanding the data science lifecycle and model deployment, Product Managers can make more informed decisions about product development and drive innovation.

See salaries and explore the career path for Product Manager

Data Engineer

Data Engineers build and maintain the infrastructure that supports data science and machine learning projects. This course may be useful for Data Engineers who want to gain a better understanding of the full data science pipeline, including model deployment. By learning about tools like Python and Flask, Data Engineers can improve their ability to integrate data pipelines and models into real-world applications.

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Statistician

Statisticians collect, analyze, and interpret data to answer questions and solve problems. This course provides a solid foundation for aspiring Statisticians by introducing them to the data science lifecycle and equipping them with the skills to develop and deploy statistical models. The focus on model deployment in this course is particularly valuable for Statisticians who want to enhance their ability to communicate insights and recommendations effectively.

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Operations Research Analyst

Operations Research Analysts use mathematical and analytical methods to solve complex business problems. This course can be beneficial for Operations Research Analysts who want to expand their technical skills and gain a deeper understanding of the data science process. By learning about model deployment and using tools like Python and Flask, Operations Research Analysts can become more effective in leveraging data to optimize business operations.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course may be useful for Quantitative Analysts who want to gain a better understanding of the full data science pipeline, including model deployment. By learning about tools like Python and Flask, Quantitative Analysts can improve their ability to integrate data pipelines and models into their investment strategies.

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Financial Analyst

Financial Analysts use financial data to evaluate and make recommendations on investments. This course may provide Financial Analysts with a foundation in data science principles and practices. By understanding the data science lifecycle and model deployment, Financial Analysts can make more informed decisions about investment opportunities and drive financial performance.

See salaries and explore the career path for Financial Analyst

Market Researcher

Market Researchers collect and analyze data to understand consumer behavior and market trends. This course can provide Market Researchers with a foundation in data science principles and practices. By understanding the data science lifecycle and model deployment, Market Researchers can make more informed decisions about product development and marketing strategies.

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Customer Success Manager

Customer Success Managers help businesses retain and grow their customer base. This course can provide Customer Success Managers with a foundation in data science principles and practices. By understanding the data science lifecycle and model deployment, Customer Success Managers can make more informed decisions about customer engagement and drive customer satisfaction.

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Data Architect

Data Architects design and manage data systems and infrastructures. This course may be useful for Data Architects who want to gain a better understanding of the full data science pipeline, including model deployment. By learning about tools like Python and Flask, Data Architects can improve their ability to integrate data pipelines and models into their data management strategies.

See salaries and explore the career path for Data Architect

IT Business Analyst

IT Business Analysts bridge the gap between business and technology. This course can provide IT Business Analysts with a foundation in data science principles and practices. By understanding the data science lifecycle and model deployment, IT Business Analysts can make more informed decisions about IT investments and drive business value.

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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 Ciclo completo del desarrollo de un proyecto de Data Science.

Information Theory, Inference and Learning...

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Este libro proporciona una introducción integral a la teoría de la información, la inferencia y los algoritmos de aprendizaje. Cubre los fundamentos, los algoritmos y las técnicas, y proporciona ejemplos de código para la implementación.

(English) Information Theory, Inference and Learning...

Hardcover

$$$

Deep Learning

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Este libro ofrece una visión integral del aprendizaje profundo, desde los fundamentos hasta las aplicaciones avanzadas. Cubre la teoría, la arquitectura y las técnicas, y proporciona ejemplos de código para la implementación.

Ciclo completo del desarrollo de un proyecto de Data Science

Three deals to help you save

What's inside

Syllabus

Good to know

Save this course

Activities

Career center

Reading list

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