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Alejandro Cataldo
Resultados de aprendizaje:
● Comprender y analizar las herramientas básicas de Python.
● Utilizar herramientas de Python para el desarrollo de técnicas para el manejo y análisis de datos como apoyo a la toma de mejores decisiones, identificando las posibilidades y oportunidades en las organizaciones.
● Identificar las oportunidades y posibilidades que ofrece el uso de la Ciencia de Datos a las organizaciones.
Read more
Resultados de aprendizaje:
● Comprender y analizar las herramientas básicas de Python.
● Utilizar herramientas de Python para el desarrollo de técnicas para el manejo y análisis de datos como apoyo a la toma de mejores decisiones, identificando las posibilidades y oportunidades en las organizaciones.
● Identificar las oportunidades y posibilidades que ofrece el uso de la Ciencia de Datos a las organizaciones.
Read more
Resultados de aprendizaje:
● Comprender y analizar las herramientas básicas de Python.
● Utilizar herramientas de Python para el desarrollo de técnicas para el manejo y análisis de datos como apoyo a la toma de mejores decisiones, identificando las posibilidades y oportunidades en las organizaciones.
● Identificar las oportunidades y posibilidades que ofrece el uso de la Ciencia de Datos a las organizaciones.
Las aplicaciones de técnicas de análisis de datos están siendo cada día más demandadas, debido a su utilización en las organizaciones y a la tendencia mundial de mejorar los procesos de toma de decisiones en base a la evidencia que se puede obtener del análisis de la enorme cantidad de información disponible. Así, la pertinencia del curso está relacionada con la necesaria aplicación de diversos métodos para analizar estos datos. Todo ello, para mejorar diferentes procesos de decisión de corto, mediano y largo plazo de los distintos sistemas dentro de una organización.
Este curso busca que profesionales de diversas áreas y con distintas motivaciones logren comprender cómo el uso de adecuado y eficiente de Python como herramienta computacional para el análisis de datos puede mejorar su toma de decisiones dentro de su organización. Para ello se introducirán los conceptos básicos y generales del análisis de datos, se revisará la importancia de visualizar e identificar valor en los datos y cómo los distintos métodos descriptivos, predictivos, y prescriptivos permiten evidenciar oportunidades y justificar decisiones. Para ejemplificar estos conceptos y su desarrollo a través de Python, se verán casos prácticos en industrias como el retail, medicina y logística, entre otras.
Register for this course and see more details by visiting:
OpenCourser.com/course/414sjs/python
What's inside
Syllabus
Módulo 1: Ciencia de datos y Python
En este módulo te invitamos a conocer la importancia del análisis de datos en la toma de decisiones en las organizaciones del mundo actual.
Read more
Syllabus
Módulo 1: Ciencia de datos y Python
En este módulo te invitamos a conocer la importancia del análisis de datos en la toma de decisiones en las organizaciones del mundo actual.
Read more
Módulo 2: Manejo de base de datos y manejo de los datos
En este módulo conocerás conceptos fundamentales sobre Bases de Datos, conectores y librerías Python para trabajo con Bases de Datos relacionales y extracción directa de información desde Python.
Módulo 3: Visualización avanzada de datos
En este módulo conocerás por qué es importante una visualización de datos y resultados. Además, cómo crear gráficos en Python y se revisará un ejemplo práctico.
Módulo 4: Analítica descriptiva y predictiva
En este módulo podrás conocer conceptos como incertidumbre, certidumbre o riesgo y cómo utilizar Python en la construcción de modelos predictivos.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Desarrolla habilidades y conocimientos fundamentales para el análisis de datos, lo que lo convierte en una opción ideal para estudiantes interesados en este campo
Está diseñado para profesionales de diversas áreas, lo que lo hace accesible para una amplia gama de estudiantes
Proporciona una base sólida para quienes son nuevos en el análisis de datos, pero también fortalece las habilidades de los estudiantes intermedios
Cubre conceptos y métodos fundamentales en el campo, haciéndolo relevante para estudiantes interesados en desarrollar habilidades analíticas de datos
Utiliza Python como herramienta principal, lo que permite a los estudiantes aplicar conocimientos directamente a la práctica
El plan de estudios incluye módulos sobre ciencia de datos, manejo de datos, visualización y análisis, proporcionando una cobertura integral de temas esenciales
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Review Python syntax and data types
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Practice basic operations and functions
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Solidify your understanding of Python before the course begins to better follow along with the course content.
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Go through an online tutorial or course on Python basics
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Review the Python documentation on basic syntax and data types
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Complete a few practice exercises or coding challenges in Python
Organize course materials
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Organize and review materials from the course to improve retention of information and strengthen foundational understanding
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Gather physical and digital course materials
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Organize materials by module or topic
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Create a system for note-taking and review
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Review Python basics
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Revisit your course notes or online resources to review Python basics.
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Solve simple Python exercises or coding challenges to practice.
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Identify areas where you need additional practice or clarification.
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Gain proficiency in working with Python data structures to better apply them in the course projects.
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Complete a set of practice exercises that involve working with different data structures in Python
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Build a small Python script that uses a combination of data structures to solve a problem
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Find a study group or create one with classmates
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Schedule regular meetings and decide on topics to cover
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Share perspectives, ask questions, and work through problems together
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Reinforce your understanding of Python syntax and data manipulation techniques by completing practice exercises.
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Find online Python exercises or use the ones provided in the course materials.
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Solve the exercises using Python.
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Check your solutions against the provided answers or ask for help if needed.
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Enhance your learning experience by collaborating with peers and discussing course concepts.
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Find or create a study group with other students in the course
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Meet regularly to discuss the course material, share notes, and work on assignments together
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Provide feedback and support to your group members
Explore additional Python libraries and resources
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Expand knowledge of Python by exploring additional libraries and resources to supplement course materials and deepen understanding
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Identify Python libraries relevant to data analysis
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Experiment with the libraries in a coding environment
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Identify areas where you need additional practice or clarification.
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Search for relevant Python tutorials online or use the ones provided in the course materials.
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Follow the instructions in the tutorials.
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Try out the examples and exercises provided in the tutorials.
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Connect with fellow learners and share knowledge by participating in study groups or discussion forums.
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Find online or local study groups or discussion forums focused on Python or data analysis.
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Join the groups and actively participate in discussions.
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Ask questions, share your insights, and help others.
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Collaborate on projects or assignments.
Create a visualization of a Python dataset
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Enhance your understanding of data visualization techniques by applying them to real-world datasets.
Show steps
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Find a dataset that you are interested in and load it into Python
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Use Python libraries such as Matplotlib or Seaborn to create visualizations of the data
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Write a short report summarizing your findings and insights from the visualizations
Develop a Python data analysis project
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Apply Python skills to a real-world data analysis project to solidify learning and demonstrate proficiency
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Identify a dataset and research relevant analysis techniques
-
Clean and prepare the data for analysis
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Apply Python libraries and techniques to analyze the data
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Visualize and interpret the results
Write Python code snippets
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Enhance your understanding of Python syntax and data handling by writing and testing your own code snippets.
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Identify a specific problem or task related to data manipulation.
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Design and write a Python code snippet to solve the problem.
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Test the code snippet to ensure it works as intended.
Follow a tutorial on Python machine learning
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Expand your knowledge of Python machine learning techniques by following a structured tutorial.
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Choose a tutorial that covers the machine learning concepts you are interested in
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Follow the tutorial step-by-step, completing all the exercises and examples
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Apply the techniques you learned to a small machine learning project of your own
Build a Python project
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Apply your Python skills to solve real-world problems by building a small Python project.
Browse courses on
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-
Define the problem or goal of your project.
-
Gather the necessary data and resources.
-
Design and develop a Python program to address the problem.
-
Test and refine your program to ensure it meets the desired outcomes.
-
Present or share your project with others for feedback.
Volunteer for a data science project
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Gain practical experience and apply your Python skills in a real-world setting.
Show steps
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Find a data science project that you are interested in and contact the organizers
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Offer to volunteer your time and skills to the project
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Contribute to the project by applying your Python knowledge and skills
Mentor junior Python learners
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巩固自己的 Python 基础知识并帮助他人同时获得宝贵的实践经验。
Browse courses on
Python
Show steps
-
Identify opportunities to mentor junior learners, such as through online forums or local coding meetups.
-
Share your knowledge and provide guidance to learners who are struggling with Python concepts.
-
Create or collect resources, such as code snippets or tutorials, to support your mentees.
Participate in Python competitions
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Push your Python skills to the limit by participating in coding competitions and challenges.
Browse courses on
Python
Show steps
-
Identify and register for relevant Python coding competitions.
-
Study and practice to improve your Python skills.
-
Compete in the competitions and strive for success.
Review Python basics
Show steps
Review basic Python concepts to strengthen foundational skills and enhance comprehension of course materials
Browse courses on
Python
Show steps
Show steps
Show steps
- Review Python syntax and data types
- Practice basic operations and functions
- Review object-oriented programming concepts
Review Python basics
Show steps
Solidify your understanding of Python before the course begins to better follow along with the course content.
Browse courses on
Python Basics
Show steps
Show steps
Show steps
- Go through an online tutorial or course on Python basics
- Review the Python documentation on basic syntax and data types
- Complete a few practice exercises or coding challenges in Python
Organize course materials
Show steps
Organize and review materials from the course to improve retention of information and strengthen foundational understanding
Show steps
Show steps
Show steps
- Gather physical and digital course materials
- Organize materials by module or topic
- Create a system for note-taking and review
16 other activities
Expand to see all activities and additional details
Show all 19 activities
Review Python basics
Show steps
Sharpen your Python skills and refresh your understanding of its fundamental concepts.
Browse courses on
Python
Show steps
Show steps
Show steps
- Revisit your course notes or online resources to review Python basics.
- Solve simple Python exercises or coding challenges to practice.
- Identify areas where you need additional practice or clarification.
Practice working with Python data structures
Show steps
Gain proficiency in working with Python data structures to better apply them in the course projects.
Show steps
Show steps
Show steps
- Complete a set of practice exercises that involve working with different data structures in Python
- Build a small Python script that uses a combination of data structures to solve a problem
Discuss course concepts with peers
Show steps
Engage in peer discussions to clarify concepts, exchange perspectives, and reinforce learning
Show steps
Show steps
Show steps
- Find a study group or create one with classmates
- Schedule regular meetings and decide on topics to cover
- Share perspectives, ask questions, and work through problems together
Practice Python exercises
Show steps
Reinforce your understanding of Python syntax and data manipulation techniques by completing practice exercises.
Browse courses on
Python
Show steps
Show steps
Show steps
- Find online Python exercises or use the ones provided in the course materials.
- Solve the exercises using Python.
- Check your solutions against the provided answers or ask for help if needed.
Join a study group for the course
Show steps
Enhance your learning experience by collaborating with peers and discussing course concepts.
Show steps
Show steps
Show steps
- Find or create a study group with other students in the course
- Meet regularly to discuss the course material, share notes, and work on assignments together
- Provide feedback and support to your group members
Explore additional Python libraries and resources
Show steps
Expand knowledge of Python by exploring additional libraries and resources to supplement course materials and deepen understanding
Browse courses on
Python Libraries
Show steps
Show steps
Show steps
- Identify Python libraries relevant to data analysis
- Review documentation and tutorials for the selected libraries
- Experiment with the libraries in a coding environment
Follow Python tutorials
Show steps
Expand your knowledge and skills in Python by following guided tutorials that cover specific topics and techniques.
Browse courses on
Python
Show steps
Show steps
Show steps
- Identify areas where you need additional practice or clarification.
- Search for relevant Python tutorials online or use the ones provided in the course materials.
- Follow the instructions in the tutorials.
- Try out the examples and exercises provided in the tutorials.
Join study groups or discussion forums
Show steps
Connect with fellow learners and share knowledge by participating in study groups or discussion forums.
Browse courses on
Python
Show steps
Show steps
Show steps
- Find online or local study groups or discussion forums focused on Python or data analysis.
- Join the groups and actively participate in discussions.
- Ask questions, share your insights, and help others.
- Collaborate on projects or assignments.
Create a visualization of a Python dataset
Show steps
Enhance your understanding of data visualization techniques by applying them to real-world datasets.
Show steps
Show steps
Show steps
- Find a dataset that you are interested in and load it into Python
- Use Python libraries such as Matplotlib or Seaborn to create visualizations of the data
- Write a short report summarizing your findings and insights from the visualizations
Develop a Python data analysis project
Show steps
Apply Python skills to a real-world data analysis project to solidify learning and demonstrate proficiency
Show steps
Show steps
Show steps
- Identify a dataset and research relevant analysis techniques
- Clean and prepare the data for analysis
- Apply Python libraries and techniques to analyze the data
- Visualize and interpret the results
Write Python code snippets
Show steps
Enhance your understanding of Python syntax and data handling by writing and testing your own code snippets.
Browse courses on
Python
Show steps
Show steps
Show steps
- Identify a specific problem or task related to data manipulation.
- Design and write a Python code snippet to solve the problem.
- Test the code snippet to ensure it works as intended.
Follow a tutorial on Python machine learning
Show steps
Expand your knowledge of Python machine learning techniques by following a structured tutorial.
Show steps
Show steps
Show steps
- Choose a tutorial that covers the machine learning concepts you are interested in
- Follow the tutorial step-by-step, completing all the exercises and examples
- Apply the techniques you learned to a small machine learning project of your own
Build a Python project
Show steps
Apply your Python skills to solve real-world problems by building a small Python project.
Browse courses on
Python
Show steps
Show steps
Show steps
- Define the problem or goal of your project.
- Gather the necessary data and resources.
- Design and develop a Python program to address the problem.
- Test and refine your program to ensure it meets the desired outcomes.
- Present or share your project with others for feedback.
Volunteer for a data science project
Show steps
Gain practical experience and apply your Python skills in a real-world setting.
Show steps
Show steps
Show steps
- Find a data science project that you are interested in and contact the organizers
- Offer to volunteer your time and skills to the project
- Contribute to the project by applying your Python knowledge and skills
Mentor junior Python learners
Show steps
巩固自己的 Python 基础知识并帮助他人同时获得宝贵的实践经验。
Browse courses on
Python
Show steps
Show steps
Show steps
- Identify opportunities to mentor junior learners, such as through online forums or local coding meetups.
- Share your knowledge and provide guidance to learners who are struggling with Python concepts.
- Create or collect resources, such as code snippets or tutorials, to support your mentees.
Participate in Python competitions
Show steps
Push your Python skills to the limit by participating in coding competitions and challenges.
Browse courses on
Python
Show steps
Show steps
Show steps
- Identify and register for relevant Python coding competitions.
- Study and practice to improve your Python skills.
- Compete in the competitions and strive for success.
Career center
Learners who complete Python para Ciencia de Datos will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist uses their knowledge of statistics, modeling, and machine learning to build systems and models to identify patterns and trends in data. This course will help you develop the skills needed to succeed as a Data Scientist, such as data cleaning, visualization, statistical modeling, and machine learning.
Data Engineer
Data Engineer
A Data Engineer builds and maintains the infrastructure needed to store and process large amounts of data. They design and implement data pipelines, databases, and other systems to ensure that data is available and accessible for analysis. This course will help you develop the skills needed to become a Data Engineer, such as data modeling, database design, and data warehousing.
Data Architect
Data Architect
A Data Architect designs and builds the infrastructure needed to store and process large amounts of data. They work with stakeholders to identify data requirements, develop data models, and implement data management solutions. This course will help you develop the skills needed to become a Data Architect, such as data modeling, database design, and data warehousing.
Business Analyst
Business Analyst
A Business Analyst helps businesses understand their data and make informed decisions. They work with stakeholders to identify business needs, collect and analyze data, and develop recommendations for improvement. This course will help you develop the skills needed to succeed as a Business Analyst, such as data analysis, visualization, and communication.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and analytical methods to solve complex problems in business and industry. They develop and implement solutions to improve efficiency, productivity, and profitability. This course may be useful to learn the basics of operations research, including modeling, optimization, and simulation.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models. They work closely with data scientists and other engineers to ensure that machine learning models are accurate, efficient, and scalable. This course may be useful to learn the basics of machine learning, including data preparation, model building, and model evaluation.
Actuary
Actuary
An Actuary uses mathematical and statistical methods to assess risk and uncertainty. They develop and implement solutions to mitigate risk and protect organizations from financial loss. This course may be useful to learn the basics of actuarial science, including data analysis, modeling, and risk assessment.
Market Researcher
Market Researcher
A Market Researcher conducts research to understand consumer behavior and trends. They design and implement surveys, focus groups, and other methods to collect data. They analyze data to identify opportunities and develop marketing strategies. This course will help you develop the skills needed to succeed as a Market Researcher, such as data collection, analysis, and communication.
Statistician
Statistician
A Statistician collects, analyzes, interprets, and presents data. They use statistical methods to draw conclusions about the world around them. This course may be useful to learn the basics of statistics, including data collection, analysis, and interpretation.
Data Analyst
Data Analyst
A Data Analyst processes large amounts of raw data to identify trends, patterns, and deviations. They prepare data for analysis, build statistical models, and create data visualizations to help businesses make informed decisions. This course may be useful to learn the basics of data analysis, including data cleaning, visualization, and statistical modeling.
Financial Analyst
Financial Analyst
A Financial Analyst evaluates and makes recommendations on investments. They analyze financial data to identify opportunities and assess risk. This course may be useful to learn the basics of financial analysis, including data collection, analysis, and presentation.
Risk Analyst
Risk Analyst
A Risk Analyst identifies, assesses, and manages risk. They develop and implement strategies to mitigate risk and protect organizations from financial loss. This course may be useful to learn the basics of risk analysis, including data collection, analysis, and modeling.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical methods to analyze financial data. They develop and implement models to identify trading opportunities and assess risk. This course may be useful to learn the basics of quantitative analysis, including data analysis, modeling, and risk management.
Computer Scientist
Computer Scientist
A Computer Scientist researches and develops new computing technologies. They design and implement algorithms, develop new programming languages, and study the theory of computation. This course may be useful to learn the basics of computer science, including programming, algorithms, and data structures.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. They work with stakeholders to identify software requirements, design and implement software solutions, and test and debug software. This course may be useful to learn the basics of software engineering, including programming, data structures, and algorithms.
For more career information including salaries, visit:
OpenCourser.com/course/414sjs/python
Reading list
We've selected eight 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
Python para Ciencia de Datos.
Practical guide to machine learning using popular Python libraries. It covers advanced techniques such as deep learning and natural language processing, extending the predictive analytics module of this course.
Comprehensive guide to data science with Python. It covers advanced topics such as data wrangling, feature engineering, and model evaluation, complementing the hands-on approach of this course.
Provides a comprehensive overview of data science from a business perspective. It covers topics such as data mining, predictive modeling, and big data, complementing the focus on data-driven decision-making in this course.
Focuses on data visualization using Python. It covers advanced visualization techniques and libraries such as Matplotlib and Seaborn, supplementing the visualization module of this course.
Este libro cubre técnicas avanzadas de aprendizaje automático y su implementación en Python. Es útil para ampliar el conocimiento y las habilidades en el desarrollo de modelos predictivos.
Este libro proporciona una introducción práctica a la ciencia de datos usando Python. Cubre conceptos básicos como la manipulación de datos, la visualización y el modelado, lo que lo hace un valioso recurso complementario para este curso.
Save
Provides a thorough understanding of both relational (SQL) and non-relational (NoSQL) databases. It valuable supplemental reading for the data management module of this course.
Provides a foundation in computer science using Python. It covers basic programming concepts and data structures, which can be helpful for students who need to strengthen their programming skills before taking this course.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/414sjs/python
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Level
Introductory
Via
Coursera
Institution
Pontificia Universidad Católica de Chile
Instructor
Alejandro Cataldo
Language
Español
Good to know
Desarrolla habilidades y conocimientos fundamentales para el análisis de datos, lo que lo convierte en una opción ideal para estudiantes interesados en este campo
Está diseñado para profesionales de diversas áreas, lo que lo hace accesible para una amplia gama de estudiantes
Proporciona una base sólida para quienes son nuevos en el análisis de datos, pero también fortalece las habilidades de los estudiantes intermedios
Cubre conceptos y métodos fundamentales en el campo, haciéndolo relevante para estudiantes interesados en desarrollar habilidades analíticas de datos
Utiliza Python como herramienta principal, lo que permite a los estudiantes aplicar conocimientos directamente a la práctica
El plan de estudios incluye módulos sobre ciencia de datos, manejo de datos, visualización y análisis, proporcionando una cobertura integral de temas esenciales
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