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Rafael Irizarry
En este curso, les presentaremos los fundamentos de la programación en R. Creemos que se retiene mejor el conocimiento de R cuando se aplica a problemas específicos y por eso utilizaremos un set de datos del mundo real relacionado al crimen en Estados Unidos. Adquirirán las habilidades necesarias en R para abordar preguntas fundamentales sobre las disparidades en asesinatos entre los diferentes estados.
Read more
En este curso, les presentaremos los fundamentos de la programación en R. Creemos que se retiene mejor el conocimiento de R cuando se aplica a problemas específicos y por eso utilizaremos un set de datos del mundo real relacionado al crimen en Estados Unidos. Adquirirán las habilidades necesarias en R para abordar preguntas fundamentales sobre las disparidades en asesinatos entre los diferentes estados.
Read more
En este curso, les presentaremos los fundamentos de la programación en R. Creemos que se retiene mejor el conocimiento de R cuando se aplica a problemas específicos y por eso utilizaremos un set de datos del mundo real relacionado al crimen en Estados Unidos. Adquirirán las habilidades necesarias en R para abordar preguntas fundamentales sobre las disparidades en asesinatos entre los diferentes estados.
Primero, estudiaremos las funciones y los tipos de datos en R. Luego, explicaremos cómo trabajar con vectores y cuándo utilizar funciones avanzadas.. Además, aprenderemos a aplicar conceptos generales de programación, como los comandos condicionales "if-else" y los bucles-for. Terminaremos estudiando el wrangling, análisis y visualización de datos.
En este curso, nos enfocamos en construir una base sólida en vez de abordar todas las técnicas de R. La demanda de profesionales cualificados en ciencia de datos está creciendo rápidamente, y este curso ofrece conceptos que servirán de bases para aprender destrezas que los ayudarán a enfrentar los desafíos del análisis de datos del mundo real.
Register for this course and see more details by visiting:
OpenCourser.com/course/l37vdd/ciencia
What's inside
Learning objectives
- La sintaxis esencial para r
- Los conceptos de programación fundamentales en r como tipos de datos, vectores, aritmética e indexación
- Cómo realizar operaciones en r incluyendo clasificación, discusión de datos utilizando dplyr y a crear gráficos
- Usted aprenderá:
- La sintaxis esencial para r
- Los conceptos de programación fundamentales en r como tipos de datos, vectores, aritmética e indexación
- Cómo realizar operaciones en r incluyendo clasificación, discusión de datos utilizando dplyr y a crear gráficos
- Usted aprenderá:
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Perfecto para principiantes que buscan una base sólida en los conceptos esenciales de R
Enseña las habilidades básicas para abordar problemas relacionados con las disparidades de asesinatos
Tiene como objetivo desarrollar habilidades para abordar desafíos de análisis de datos del mundo real
El uso de un conjunto de datos del mundo real proporciona relevancia práctica
Se enfoca en construir una base sólida en lugar de cubrir temas exhaustivamente, lo que permite un aprendizaje más profundo
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Reviews summary
Fundamentos esenciales de r para ciencia de datos
Según los estudiantes, este curso es una excelente puerta de entrada al mundo de la programación en R y la ciencia de datos. Los conceptos se explican con claridad, haciendo que temas complejos sean comprensibles. Los ejemplos y ejercicios prácticos son un punto fuerte, ayudando a los estudiantes a afianzar el conocimiento y ganar confianza. Aunque se valora por establecer una base sólida, algunos mencionan que el ritmo puede ser rápido para quienes no tienen experiencia previa en programación, y que es netamente introductorio, sin profundizar en técnicas avanzadas. A pesar de esto, se le considera un gran punto de partida para una carrera en ciencia de datos.
Ideal para fundamentos, no para una inmersión profunda en temas avanzados.
"Lo que sí, es netamente introductorio, no esperes profundizar mucho."
"No lo recomiendo para nadie que busque una inmersión profunda. Es demasiado superficial y los ejemplos son muy básicos."
"Si bien es fundamental, me gustaría un seguimiento con un curso más avanzado del mismo nivel de calidad."
"Me hubiera gustado ver más ejemplos del mundo real o proyectos un poco más complejos."
Los ejercicios y ejemplos son muy útiles para aplicar y reforzar el aprendizaje.
"Los ejemplos y ejercicios son muy útiles para poner en práctica lo aprendido."
"Los ejercicios son muy útiles para aplicar lo aprendido."
"Los ejercicios prácticos refuerzan el aprendizaje."
"Este curso me dio la confianza para empezar a trabajar con R. Los ejercicios bien pensados, hacen que el aprendizaje sea muy efectivo."
El profesor explica conceptos complejos de forma sencilla y didáctica.
"Las explicaciones del profesor son muy claras y los ejemplos y ejercicios son muy útiles."
"La forma en que el profesor explica hace que conceptos complejos parezcan sencillos."
"El profesor es un genio, explica muy bien. Los ejemplos son prácticos y fáciles de seguir."
"El profesor es muy didáctico."
Proporciona una excelente base para principiantes en R y ciencia de datos.
"El curso es excelente para adquirir una base sólida en R y ciencia de datos."
"Me sirvió mucho para entender las bases de R y comenzar mi camino en ciencia de datos."
"Un curso que cumple lo que promete: sentar las bases de R para el análisis de datos."
"Me pareció un curso muy bueno para aprender los fundamentos de R."
El ritmo puede ser rápido, beneficiándose de experiencia previa en programación.
"El ritmo es un poco rápido si no tienes experiencia previa en programación. Es mejor si ya sabes algo de programación."
"El ritmo puede ser un poco exigente para quienes no tienen base."
"Me hubiera gustado un poco más de tiempo para asimilar los conceptos entre módulo y módulo."
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 Ciencia de Datos: Fundamentos de R with these
activities:
Review the basics of programming
Show steps
This activity will prepare you for the course by helping you remember the foundational knowledge of R programming, which will increase learning outcomes.
Browse courses on
Programming Basics
Show steps
-
Review the R syntax and data types.
-
Practice working with vectors and matrices.
-
Brush up on conditional statements and loops.
Organize a study group with classmates
Show steps
Regularly meeting with classmates to discuss course material in a group setting will enhance your understanding through collaborative learning.
Browse courses on
R Programming
Show steps
-
Find a few classmates who are interested in forming a study group.
-
Decide when and where to meet.
-
Establish a schedule and create a list of topics to cover each session.
Complete the R tutorials on Coursera
Show steps
These tutorials will provide hands-on practice with R programming, which will reinforce the concepts you learn in the course.
Browse courses on
R Programming
Show steps
-
Sign up for the Coursera R tutorials.
-
Complete the introductory module.
-
Practice the exercises in each module.
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Seek guidance from an experienced R programmer
Show steps
Connecting with a mentor will have a positive impact on your course work and provide valuable insights from someone with experience in the field.
Browse courses on
Mentorship
Show steps
-
Identify potential mentors who are experienced R programmers.
-
Reach out to your potential mentors and ask for their guidance.
-
Establish regular communication with your mentor.
Working with Vectors in R
Show steps
Complete a series of exercises to practice creating, manipulating, and summarizing vectors in R.
Browse courses on
Vectors
Show steps
-
Create vectors of different data types (e.g., numeric, character, logical).
-
Perform operations on vectors (e.g., addition, subtraction, multiplication).
-
Extract and subset elements from vectors using indexing.
Data Wrangling with dplyr
Show steps
Follow a guided tutorial on using the dplyr package to filter, transform, and summarize data in R.
Browse courses on
Data Wrangling
Show steps
-
Install and load the dplyr package.
-
Learn the basics of data wrangling with dplyr (e.g., filtering, selecting, grouping).
-
Apply dplyr functions to real-world datasets.
Solve R coding challenges on HackerRank
Show steps
Solving R coding challenges will strengthen your programming skills and improve your problem-solving abilities.
Browse courses on
R Programming
Show steps
-
Create an account on HackerRank.
-
Select the R programming track.
-
Solve the coding challenges in the track.
Data Visualization Project
Show steps
Create a data visualization project that showcases your understanding of data analysis and visualization techniques in R.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset and research a topic of interest.
-
Clean and prepare the data for visualization.
-
Create visualizations using appropriate R packages (e.g., ggplot2).
-
Interpret and communicate the results of the visualization.
Build a data visualization project in R
Show steps
This project will allow you to apply the R programming skills you learn in the course to a real-world problem, which will deepen your understanding of the material.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset to analyze.
-
Clean and prepare the data.
-
Create a data visualization using R.
Participate in an R programming competition
Show steps
Participating will provide you with a practical and simulated environment to test your knowledge and refine your R programming capabilities.
Browse courses on
R Programming
Show steps
-
Register for the competition.
-
Find an R programming competition that aligns with your interests.
-
Develop a strategy for solving the competition's challenges.
Review the basics of programming
Show steps
This activity will prepare you for the course by helping you remember the foundational knowledge of R programming, which will increase learning outcomes.
Browse courses on
Programming Basics
Show steps
Show steps
Show steps
- Review the R syntax and data types.
- Practice working with vectors and matrices.
- Brush up on conditional statements and loops.
Organize a study group with classmates
Show steps
Regularly meeting with classmates to discuss course material in a group setting will enhance your understanding through collaborative learning.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Find a few classmates who are interested in forming a study group.
- Decide when and where to meet.
- Establish a schedule and create a list of topics to cover each session.
Complete the R tutorials on Coursera
Show steps
These tutorials will provide hands-on practice with R programming, which will reinforce the concepts you learn in the course.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Sign up for the Coursera R tutorials.
- Complete the introductory module.
- Practice the exercises in each module.
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Seek guidance from an experienced R programmer
Show steps
Connecting with a mentor will have a positive impact on your course work and provide valuable insights from someone with experience in the field.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Identify potential mentors who are experienced R programmers.
- Reach out to your potential mentors and ask for their guidance.
- Establish regular communication with your mentor.
Working with Vectors in R
Show steps
Complete a series of exercises to practice creating, manipulating, and summarizing vectors in R.
Browse courses on
Vectors
Show steps
Show steps
Show steps
- Create vectors of different data types (e.g., numeric, character, logical).
- Perform operations on vectors (e.g., addition, subtraction, multiplication).
- Extract and subset elements from vectors using indexing.
Data Wrangling with dplyr
Show steps
Follow a guided tutorial on using the dplyr package to filter, transform, and summarize data in R.
Browse courses on
Data Wrangling
Show steps
Show steps
Show steps
- Install and load the dplyr package.
- Learn the basics of data wrangling with dplyr (e.g., filtering, selecting, grouping).
- Apply dplyr functions to real-world datasets.
Solve R coding challenges on HackerRank
Show steps
Solving R coding challenges will strengthen your programming skills and improve your problem-solving abilities.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Create an account on HackerRank.
- Select the R programming track.
- Solve the coding challenges in the track.
Data Visualization Project
Show steps
Create a data visualization project that showcases your understanding of data analysis and visualization techniques in R.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset and research a topic of interest.
- Clean and prepare the data for visualization.
- Create visualizations using appropriate R packages (e.g., ggplot2).
- Interpret and communicate the results of the visualization.
Build a data visualization project in R
Show steps
This project will allow you to apply the R programming skills you learn in the course to a real-world problem, which will deepen your understanding of the material.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset to analyze.
- Clean and prepare the data.
- Create a data visualization using R.
Participate in an R programming competition
Show steps
Participating will provide you with a practical and simulated environment to test your knowledge and refine your R programming capabilities.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Register for the competition.
- Find an R programming competition that aligns with your interests.
- Develop a strategy for solving the competition's challenges.
Career center
Learners who complete Ciencia de Datos: Fundamentos de R will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst is someone who collects, processes, and analyzes data to help businesses make informed decisions. This course is a great introduction to data analysis, and it will teach you the skills you need to succeed in this role. With its focus on real-world data and challenges, you will learn how to apply the concepts taught in this course to solve problems in industry.
Data Scientist
Data Scientist
Data Scientists use their knowledge in math, statistics, and computer science to solve business problems. This course will teach you the skills needed to succeed in this field. By providing students with a solid base on which to advance their skillset, this course will serve as a cornerstone of your path toward becoming a well-rounded Data Scientist.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and deploy machine learning models to solve business problems. This course will provide the necessary building blocks to excel in this role. You will learn the fundamentals of R, which is a popular programming language for machine learning.
Business Analyst
Business Analyst
Business Analysts use data and technology to solve business problems. This course will give you foundational skills to help you succeed in this role. With a solid understanding of R and its uses, you will be well-equipped to assist Business Analysts with data analysis.
Statistician
Statistician
Statisticians use data to solve problems. This course may be useful for those looking to enter this field. By familiarizing yourself with R, you will have an advantage in learning statistical methods and their applications.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment decisions. This course may be useful for those looking to enter this field. You will learn how to use R to analyze financial data and build models.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use data to make investment decisions. This course may be useful for those looking to enter this field. You will learn how to use R to analyze financial data and build models.
Market Researcher
Market Researcher
Market Researchers use data to understand consumer behavior. This course may be useful for those looking to enter this field. You will learn how to use R to analyze data and identify trends.
Actuary
Actuary
Actuaries use data to assess risk. This course may be useful for those looking to enter this field. You will learn how to use R to analyze data and build models.
Data Engineer
Data Engineer
Data Engineers build and maintain data pipelines. This course may be useful for those looking to enter this field. You will learn how to use R to work with big data.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course may be useful for those looking to enter this field. You will learn the fundamentals of R, which is a popular programming language for data science.
Computer Scientist
Computer Scientist
Computer Scientists conduct research in computer science. This course may be useful for those looking to enter this field. You will learn the fundamentals of R, which is a popular programming language for data science.
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists create visualizations to communicate data insights. This course may be useful for those looking to enter this field. You will learn how to use R to create visualizations.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. This course may be useful for those looking to enter this field. You will learn how to use R to work with data in databases.
Information Security Analyst
Information Security Analyst
Information Security Analysts protect computer systems and networks from attack. This course may be useful for those looking to enter this field. You will learn the fundamentals of R, which is a popular programming language for data science.
For more career information including salaries, visit:
OpenCourser.com/course/l37vdd/ciencia
Reading list
We've selected 13 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
Ciencia de Datos: Fundamentos de R.
Save
Este libro es un recurso integral para aprender R para la ciencia de datos, cubriendo temas como la manipulación y el análisis de datos, la visualización y el modelado estadístico.
Este libro clásico proporciona una base sólida en métodos de aprendizaje automático y es una lectura valiosa para quienes buscan profundizar en los aspectos estadísticos de la ciencia de datos.
Save
Este libro proporciona una cobertura avanzada de R, cubriendo temas como programación funcional y metaprogramación.
Save
Este libro proporciona una cobertura completa de técnicas de minería de datos, que es esencial para la ciencia de datos.
Este libro se centra en comprender e interpretar modelos de aprendizaje automático, lo que es crucial para la ciencia de datos.
Este libro proporciona una introducción práctica a la ciencia de datos, cubriendo los fundamentos desde cero.
Este libro se enfoca específicamente en el uso de R para el aprendizaje automático, proporcionando ejemplos prácticos.
Este libro aborda las cuestiones éticas relacionadas con la ciencia de datos, lo que es esencial para los profesionales de la ciencia de datos responsables.
Este libro se centra en las aplicaciones prácticas de la ciencia de datos en un contexto empresarial, proporcionando información sobre cómo utilizar los datos para tomar decisiones informadas.
Save
Este libro integral cubre los fundamentos del aprendizaje profundo, proporcionando una base para quienes buscan explorar este campo en desarrollo.
Este libro ofrece una introducción al modelado bayesiano, que es una técnica estadística poderosa utilizada en la ciencia de datos.
Aunque este libro se centra en Python, proporciona información valiosa sobre los conceptos y técnicas de ciencia de datos, lo que lo convierte en una lectura complementaria útil.
Save
Este libro es un recurso práctico para crear visualizaciones de datos efectivas en R.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/l37vdd/ciencia
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Rating
4.5
Effort
2 to 4 hours per week for 4 weeks
Level
Introductory
Via
edX
Institution
Harvard University
Instructor
Rafael Irizarry
Language
Español
Transcript
Español
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