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Regresión lineal simple y múltiple en Python
Leire Ahedo
Leire Ahedo
Regresión lineal simple y múltiple en Python
Register for this course and see more details by visiting:
OpenCourser.com/course/jvddth/regresion
What's inside
Syllabus
Visión general del proyecto
En este proyecto guiado aprenderemos todo lo relacionado con los modelos de regresión lineal.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Lecciona a los estudiantes sobre el análisis de regresión lineal, un método ampliamente utilizado en industrias como la ciencia de datos, la investigación médica y el análisis financiero
Enseña el modelo de regresión lineal simple, que establece una relación lineal entre una variable dependiente y una variable independiente, proporcionando información valiosa para comprender patrones de datos y predecir resultados
Profundiza en el modelo de regresión lineal múltiple, que extiende el concepto a múltiples variables independientes, lo que permite a los estudiantes modelar relaciones más complejas
Proporciona una introducción a la implementación de la regresión lineal en Python, utilizando bibliotecas como NumPy, Pandas y Scikit-learn, lo que permite a los estudiantes aplicar sus conocimientos en situaciones del mundo real
Dirigido a estudiantes principiantes que buscan comprender los fundamentos de la regresión lineal y su aplicación en Python
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Activities
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Regresión lineal simple y múltiple en Python.
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Career center
Learners who complete Regresión lineal simple y múltiple en Python will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, cleaning, and analyzing data to find patterns and trends that can be used to make better business decisions. They use a variety of statistical and machine learning techniques to build models that can predict future outcomes. The Regresión lineal simple y múltiple en Python course can help Data Scientists build a foundation in the statistical and machine learning concepts needed to be successful in this role. The course covers a variety of topics, including data collection, data cleaning, data analysis, and machine learning.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for building and deploying machine learning models. They use a variety of statistical and machine learning techniques to develop models that can predict future outcomes. The Regresión lineal simple y múltiple en Python course can help Machine Learning Engineers build a foundation in the statistical and machine learning concepts needed to be successful in this role. The course covers a variety of topics, including data collection, data cleaning, data analysis, and machine learning.
Statistician
Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data. They use a variety of statistical techniques to draw conclusions about the data they collect. The Regresión lineal simple y múltiple en Python course can help Statisticians build a foundation in the statistical concepts needed to be successful in this role. The course covers a variety of topics, including data collection, data cleaning, data analysis, and statistical modeling.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to find patterns and trends that can be used to make better business decisions. They use a variety of statistical and data mining techniques to analyze data and draw conclusions. The Regresión lineal simple y múltiple en Python course can help Data Analysts build a foundation in the statistical and data mining concepts needed to be successful in this role. The course covers a variety of topics, including data collection, data cleaning, data analysis, and data mining.
Market Researcher
Market Researcher
Market Researchers are responsible for collecting, analyzing, and interpreting data about markets and consumers. They use a variety of statistical and market research techniques to conduct surveys, analyze data, and make recommendations about marketing strategies. The Regresión lineal simple y múltiple en Python course can help Market Researchers build a foundation in the statistical and market research concepts needed to be successful in this role. The course covers a variety of topics, including data collection, data cleaning, data analysis, and market research.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts are responsible for using mathematical and statistical techniques to analyze and improve business operations. They use a variety of statistical and optimization techniques to develop models that can help businesses make better decisions about how to allocate resources and improve efficiency. The Regresión lineal simple y múltiple en Python course can help Operations Research Analysts build a foundation in the statistical and optimization concepts needed to be successful in this role. The course covers a variety of topics, including data collection, data cleaning, data analysis, and optimization.
Financial Analyst
Financial Analyst
Financial Analysts are responsible for analyzing financial data to make investment recommendations. They use a variety of statistical and financial modeling techniques to analyze data and make predictions about future financial performance. The Regresión lineal simple y múltiple en Python course can help Financial Analysts build a foundation in the statistical and financial modeling concepts needed to be successful in this role. The course covers a variety of topics, including data collection, data cleaning, data analysis, and financial modeling.
Risk Analyst
Risk Analyst
Risk Analysts are responsible for identifying and assessing risks to businesses and organizations. They use a variety of statistical and risk management techniques to analyze data and make recommendations about how to mitigate risks. The Regresión lineal simple y múltiple en Python course can help Risk Analysts build a foundation in the statistical and risk management concepts needed to be successful in this role. The course covers a variety of topics, including data collection, data cleaning, data analysis, and risk management.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts are responsible for using mathematical and statistical models to analyze financial data and make investment decisions. They use a variety of statistical and financial modeling techniques to analyze data and make predictions about future financial performance. The Regresión lineal simple y múltiple en Python course can help Quantitative Analysts build a foundation in the statistical and financial modeling concepts needed to be successful in this role. The course covers a variety of topics, including data collection, data cleaning, data analysis, and financial modeling.
Technical Writer
Technical Writer
Technical Writers are responsible for writing technical documentation, such as user manuals, white papers, and training materials. They use a variety of writing and editing skills to create clear and concise documentation that can be easily understood by users. The Regresión lineal simple y múltiple en Python course may be useful to Technical Writers who are interested in using statistical techniques to analyze user feedback and make decisions about how to improve technical documentation.
Product Manager
Product Manager
Product Managers are responsible for planning, developing, and launching new products. They use a variety of marketing and product management techniques to identify market opportunities and develop products that meet the needs of customers. The Regresión lineal simple y múltiple en Python course may be useful to Product Managers who are interested in using statistical techniques to analyze market data and make decisions about product development.
Data Engineer
Data Engineer
Data Engineers are responsible for designing, building, and maintaining data pipelines. They use a variety of software engineering and data engineering tools to create data pipelines that can collect, clean, and transform data. The Regresión lineal simple y múltiple en Python course may be useful to Data Engineers who are interested in using Python to develop data pipelines that use statistical or machine learning techniques.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and testing software applications. They use a variety of programming languages and software development tools to create software that meets the needs of users. The Regresión lineal simple y múltiple en Python course may be useful to Software Engineers who are interested in developing software applications that use statistical or machine learning techniques.
Business Analyst
Business Analyst
Business Analysts are responsible for analyzing business processes and making recommendations about how to improve them. They use a variety of statistical and business analysis techniques to analyze data and make recommendations about how to improve efficiency and effectiveness. The Regresión lineal simple y múltiple en Python course may be useful to Business Analysts who are interested in using statistical techniques to analyze business data.
Project Manager
Project Manager
Project Managers are responsible for planning, executing, and closing projects. They use a variety of project management techniques to ensure that projects are completed on time, within budget, and to the required quality standards. The Regresión lineal simple y múltiple en Python course may be useful to Project Managers who are interested in using statistical techniques to analyze project data and make decisions about project management.
For more career information including salaries, visit:
OpenCourser.com/course/jvddth/regresion
Reading list
We've selected nine 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
Regresión lineal simple y múltiple en Python.
Este libro de texto riguroso proporciona una base teórica sólida para los modelos estadísticos, incluida la regresión lineal. Es una lectura desafiante pero gratificante para aquellos que buscan una comprensión profunda de los fundamentos de la regresión.
Este libro de texto avanzado proporciona una cobertura en profundidad de los modelos de regresión lineal. Es una lectura desafiante pero completa para aquellos que buscan una comprensión exhaustiva de los modelos de regresión.
Este libro de texto cubre los fundamentos estadísticos de la inferencia estadística, incluida la regresión. Proporciona una base teórica sólida para comprender los principios y suposiciones subyacentes a los modelos de regresión.
Este libro proporciona una introducción conceptual a la regresión. Es una lectura complementaria valiosa para comprender los principios intuitivos detrás de los modelos de regresión.
Este libro proporciona una colección de estudios de casos prácticos que ilustran el uso de la regresión lineal en diversas industrias y aplicaciones. Es un recurso útil para comprender cómo se aplican los modelos de regresión en situaciones del mundo real.
Este libro de texto se enfoca en la econometría utilizando R. Si bien no cubre explícitamente la regresión lineal simple, proporciona una base sólida en los principios econométricos que son esenciales para comprender los modelos de regresión en aplicaciones económicas.
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Este libro de texto explora técnicas avanzadas de regresión para el análisis de series temporales. Si bien no cubre explícitamente la regresión lineal simple y múltiple, proporciona una base sólida para comprender los modelos de regresión en el contexto del análisis de series temporales.
Este libro proporciona una introducción accesible al aprendizaje automático. Si bien no cubre específicamente la regresión lineal, ofrece una base conceptual útil para comprender el papel de la regresión en el aprendizaje automático.
Este libro se enfoca en el aprendizaje profundo utilizando Fastai y PyTorch. Aunque no cubre la regresión lineal, proporciona información valiosa sobre las técnicas modernas de aprendizaje automático que se basan en conceptos de regresión.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/jvddth/regresion
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Effort
2 horas
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Leire Ahedo
Language
Español
Good to know
Lecciona a los estudiantes sobre el análisis de regresión lineal, un método ampliamente utilizado en industrias como la ciencia de datos, la investigación médica y el análisis financiero
Enseña el modelo de regresión lineal simple, que establece una relación lineal entre una variable dependiente y una variable independiente, proporcionando información valiosa para comprender patrones de datos y predecir resultados
Profundiza en el modelo de regresión lineal múltiple, que extiende el concepto a múltiples variables independientes, lo que permite a los estudiantes modelar relaciones más complejas
Proporciona una introducción a la implementación de la regresión lineal en Python, utilizando bibliotecas como NumPy, Pandas y Scikit-learn, lo que permite a los estudiantes aplicar sus conocimientos en situaciones del mundo real
Dirigido a estudiantes principiantes que buscan comprender los fundamentos de la regresión lineal y su aplicación en Python
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