Descubriendo funciones trigonométricas con Python from Coursera

En la matemática escolar es importante abordar varias perspectivas y técnicas para asimilar los conceptos matemáticos, realizar procedimientos con entendimiento e interpretar los resultados. Por ejemplo, las funciones matemáticas pueden ser estudiadas mediante expresiones algebraicas y trascendentes y representadas en listas, tablas y gráficas.

En este proyecto guiado aprenderás a programar interfaces interactivas para explorar listas de funciones trigonométricas. El proyecto está dirigido a los docentes de la escuela secundaria con conocimientos básicos de programación de computadoras, tales como operaciones aritméticas y sentencias condicionales. Utilizarás el lenguaje Python 3.9 en Jupyter Notebook con el módulo math y la biblioteca ipywidgets para realizar tareas de análisis y de programación mediante guías en vídeos y en un cuaderno de prácticas que incluye enlaces a recursos informáticos y matemáticos. Los vídeos duran aproximadamente una hora y las tareas requieren dedicar alrededor de dos horas. Al finalizar el proyecto estarás en capacidad de programar interfaces básicas para ayudar a los estudiantes a descubrir propiedades y patrones matemáticos en las funciones trigonométricas.

Nota: los proyectos guiados de la serie "Explorando/Tabulando/Graficando funciones ... con Python" contienen varias tareas similares para que puedan ser elegidos independientemente y solo los necesarios.

What's inside

Syllabus

Visión general del proyecto

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Este curso está diseñado para profesores de secundaria con conocimientos básicos de programación, centrándose en las aplicaciones para matemáticas escolares

Este curso se centra en las funciones trigonométricas, proporcionando una base sólida para los educadores de matemáticas

Utiliza Python 3.9, Jupyter Notebook, el módulo math y la biblioteca ipywidgets, herramientas ampliamente utilizadas para el análisis y la programación

Los videos guiados y el cuaderno de prácticas facilitan el aprendizaje y la implementación de las técnicas

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 Descubriendo funciones trigonométricas con Python with these activities:

Revisar Álgebra

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Practica habilidades algebraicas clave para apoyar el aprendizaje de funciones trigonométricas.

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Repasa ecuaciones lineales y cuadráticas.
Practica la factorización y expansión de expresiones algebraicas.
Repasa las propiedades de las funciones y las gráficas.

Crear un Cuaderno de Gráficas de Funciones Trigonométricas

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Desarrolla habilidades de visualización y comprensión de las gráficas de funciones trigonométricas.

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Utiliza Jupyter Notebook para crear un cuaderno interactivo.
Importa las bibliotecas necesarias y define funciones trigonométricas.
Genera y grafica listas de puntos para varias funciones trigonométricas.
Ajusta los parámetros para explorar diferentes rangos y escalas.
Guarda el cuaderno para uso futuro y referencia.

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Career center

Learners who complete Descubriendo funciones trigonométricas con Python will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use the principles of math, statistics, computer science, and business to make sense of data. A background in Python programming is essential for this role since it aids in building machine learning models to predict future outcomes, analyze large data sets, and extract insights.

See salaries and explore the career path for Data Scientist

Software Engineer

Software engineers develop, maintain, and improve software applications and systems. The course helps build a solid foundation in the Python programming language, a key tool used by software engineers. Additionally, the course provides an understanding of mathematical concepts that are important for developing and optimizing software.

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Financial Analyst

Financial Analysts use mathematical and statistical methods to analyze financial data and make investment recommendations. The course provides a solid foundation in Python programming, which is commonly used in financial modeling and data analysis. The course also introduces learners to probability and statistics.

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Actuary

Actuaries use mathematical and statistical methods to assess risk and uncertainty in the insurance and finance industries. Python programming helps automate complex calculations. The course's focus on trigonometric functions will be beneficial for modeling risk and uncertainty.

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Computer Programmer

Computer Programmers write and maintain computer code. The course provides a solid foundation in Python programming, which is a popular language for developing a wide range of applications. The course also teaches problem-solving skills and introduces mathematical concepts.

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Business Analyst

Business Analysts use data analysis and problem-solving skills to help businesses improve their performance. The course provides a foundation in Python programming, which is commonly used for data analysis. The course also develops problem-solving skills through interactive programming exercises.

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Data Analyst

Data Analysts collect, clean, and analyze data to extract insights. Python programming is a key tool for data cleaning and analysis. The course also provides an understanding of statistics and probability, which is essential for interpreting data.

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Statistician

Statisticians use mathematical and statistical methods to collect, analyze, and interpret data. The course provides a foundation in math and statistics, which is essential for understanding and applying statistical methods. The course also introduces learners to Python programming, which is increasingly used in statistics.

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Systems Analyst

Systems Analysts design and implement computer systems. The course provides a foundation in Python programming, which is often used for developing and maintaining software applications. The course also covers problem-solving skills and systems analysis techniques.

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Market Researcher

Market Researchers conduct research to understand market trends and consumer behavior. The course provides a foundation in Python programming, which helps in data collection and analysis. The course also introduces learners to probability and statistics, which are essential for conducting market research.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical methods to analyze financial data and make investment decisions. The course provides a solid foundation in Python programming, which is widely used in quantitative finance. The course also develops problem-solving skills and introduces learners to probability and statistics.

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Software Developer

Software Developers design, develop, and maintain software applications. The course provides a solid foundation in Python programming, a popular language for software development. The course also teaches problem-solving skills and introduces mathematical concepts important for software development.

See salaries and explore the career path for Software Developer

Data Engineer

Data Engineers design and manage data systems. The course provides a foundation in Python programming, which is commonly used for data engineering. The course also introduces learners to database systems and cloud computing.

See salaries and explore the career path for Data Engineer

Business Intelligence Analyst

Business Intelligence Analysts use data analysis and visualization techniques to provide insights for business decision-making. The course provides a foundation in Python programming, which is commonly used for data analysis and visualization. The course also introduces learners to data mining and machine learning techniques.

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Machine Learning Engineer

Machine Learning Engineers design and implement machine learning models. The course provides a foundation in Python programming, which is widely used in machine learning. The course also introduces learners to probability, statistics, and linear algebra, which are essential for understanding and applying machine learning techniques.

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Descubriendo funciones trigonométricas con Python

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