Python para Ciencia de Datos from Coursera

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.

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.

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

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

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 Python para Ciencia de Datos with these activities:

Review Python basics

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Review basic Python concepts to strengthen foundational skills and enhance comprehension of course materials

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Review Python syntax and data types
Practice basic operations and functions
Review object-oriented programming concepts

Review Python basics

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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
Review the Python documentation on basic syntax and data types
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
Organize materials by module or topic
Create a system for note-taking and review

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Review Python basics

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Sharpen your Python skills and refresh your understanding of its fundamental concepts.

Browse courses on Python

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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

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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
Build a small Python script that uses a combination of data structures to solve a problem

Discuss course concepts with peers

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Engage in peer discussions to clarify concepts, exchange perspectives, and reinforce learning

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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

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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.
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

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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
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

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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
Review documentation and tutorials for the selected libraries
Experiment with the libraries in a coding environment

Follow Python tutorials

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Expand your knowledge and skills in Python by following guided tutorials that cover specific topics and techniques.

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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

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Connect with fellow learners and share knowledge by participating in study groups or discussion forums.

Browse courses on Python

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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

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Enhance your understanding of data visualization techniques by applying them to real-world datasets.

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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

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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
Apply Python libraries and techniques to analyze the data
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.
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

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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
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

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Apply your Python skills to solve real-world problems by building a small Python project.

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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.

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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

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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。

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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.

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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

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Data Engineer

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.

See salaries and explore the career path for Data Architect

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.

See salaries and explore the career path for Business 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.

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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.

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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.

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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.

See salaries and explore the career path for Market Researcher

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.

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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.

See salaries and explore the career path for Data 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.

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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.

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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.

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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.

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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.

See salaries and explore the career path for Software Engineer