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Herramientas para la ciencia de datos

¿Cuáles son algunas de las herramientas de ciencia de datos más populares, cómo las usa y cuáles son sus características? En este curso, aprenderá sobre Jupyter Notebooks, RStudio IDE, Apache Zeppelin y Data Science Experience. Aprenderá para qué se utiliza cada herramienta, qué lenguajes de programación pueden ejecutar, sus características y limitaciones. Con las herramientas alojadas en la nube en Cognitive Class Labs, podrá probar cada herramienta y seguir las instrucciones para ejecutar código simple en Python, R o Scala. Para finalizar el curso, creará un proyecto final con un Jupyter Notebook en IBM Data Science Experience y demostrará su competencia preparando un cuaderno, escribiendo Markdown y compartiendo su trabajo con sus compañeros.

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Introduces 4 significant data science tools

Offers a practical approach with hands-on exercises using Jupyter Notebooks, RStudio IDE, Apache Zeppelin, and Data Science Experience

Each tool's features and limitations are explained clearly

Taught by experienced instructors Romeo Kienzler and Svetlana Levitan

Suitable for various learners, including beginners and those looking to refresh their data science toolkit

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Reviews summary

Herramientas prácticas para la ciencia de datos

Este curso proporciona una revisión de las herramientas esenciales para la ciencia de datos, presentando Jupyter Notebooks, RStudio IDE, Apache Zeppelin y Data Science Experience. Los estudiantes aprenderán sobre los propósitos específicos, los lenguajes de programación compatibles, las funciones y las limitaciones de cada herramienta. El curso incluye ejercicios prácticos alojados en la nube y culmina con un proyecto final que demuestra la competencia en el uso de Jupyter Notebook en IBM Data Science Experience.

Culmina con un proyecto que demuestra habilidades.

"El curso culmina con un proyecto final que demuestra la competencia en el uso de Jupyter Notebook en IBM Data Science Experience."

Proporciona ejercicios prácticos en la nube.

"El curso incluye ejercicios prácticos alojados en la nube"

Presenta las herramientas clave para la ciencia de datos.

"Este curso proporciona una revisión de las herramientas esenciales para la ciencia de datos"

"Los estudiantes aprenderán sobre los propósitos específicos, los lenguajes de programación compatibles, las funciones y las limitaciones de cada herramienta."

Falta de traducción precisa a español.

"Todo esta muy bien, pero me gustaría que esté en español los videos"

"Muy Buen curso, pero los subtítulos en español de los videos muchas veces no correspondían al contenido narrado"

"malas traducciones"

Algunos videos y laboratorios pueden estar desactualizados.

"Algunas pantallas y videos estan desactualizados"

"El Jupyter notebooks in Watson Studio course is outdated"

"La explicación que hacen el video en varias ocasiones no coincide con el material en PDF"

"Los videos son antiguos, muestran una plataforma totalmente diferente a como se ve ahora"

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 Herramientas para la ciencia de datos with these activities:

Connect with Data Science Professionals

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Seek guidance and insights from experienced data scientists to supplement classroom learning and accelerate growth.

Browse courses on Mentorship

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Identify potential mentors in the field
Reach out and request a meeting or informational interview
Prepare questions and topics for discussion
Follow up and maintain a professional relationship

Read 'Data Science for Business'

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Gain a comprehensive understanding of data science principles and applications in a business context, providing foundational knowledge.

View Data Science for Business: What You Need to... on Amazon

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Read and summarize key concepts
Apply concepts to real-world business scenarios

Review Python and R

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Review key concepts and syntax of Python and R to strengthen foundational knowledge in data science.

Browse courses on Python

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Review Python basics (data types, variables, control flow)
Review R basics (data frames, vectors, functions)
Practice writing simple scripts in Python and R

Four other activities

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Participate in Peer Study Groups

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Engage with peers, discuss concepts, and solve problems together to reinforce learning and enhance understanding.

Browse courses on Collaboration

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Join or form a study group with classmates
Regularly meet to review materials, discuss assignments
Collaborate on projects and seek feedback
Prepare for exams together

Solve Jupyter Notebook Exercises

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Apply Python concepts through hands-on practice exercises in Jupyter Notebooks to solidify understanding.

Browse courses on Jupyter Notebooks

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Run provided code snippets in Jupyter Notebooks
Attempt exercises and debug errors
Share and discuss solutions with peers

Design a Data Analysis Pipeline

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Practice designing and implementing a data analysis pipeline to improve efficiency and accuracy in handling large datasets.

Browse courses on Data Pipelines

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Define the data sources and data types
Design the data cleaning and transformation process
Choose appropriate data analysis algorithms and techniques
Implement the data pipeline and evaluate its performance

Build a Data Science Portfolio

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Showcase skills and knowledge by creating a portfolio of data science projects, presentations, or visualizations.

Browse courses on Data Visualization

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Identify potential projects or case studies
Gather data and perform analysis
Design and create data visualizations
Compile work into a portfolio website or presentation

Career center

Learners who complete Herramientas para la ciencia de datos will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts collect, clean, and analyze data to help businesses make better decisions. They work in a variety of industries, including finance, healthcare, and technology. This course can help you develop the skills you need to become a successful Data Analyst. You will learn how to use popular data science tools such as Jupyter Notebooks, RStudio IDE, Apache Zeppelin, and Data Science Experience. You will also learn how to write code in Python, R, and Scala.

See salaries and explore the career path for Data Analyst

Machine Learning Engineer

Machine Learning Engineers build and maintain machine learning models. They work in a variety of industries, including finance, healthcare, and technology. This course can help you develop the skills you need to become a successful Machine Learning Engineer. You will learn how to use popular data science tools such as Jupyter Notebooks, RStudio IDE, Apache Zeppelin, and Data Science Experience. You will also learn how to write code in Python, R, and Scala.

See salaries and explore the career path for Machine Learning Engineer

Data Engineer

Data Engineers build and maintain data pipelines. They work in a variety of industries, including finance, healthcare, and technology. This course can help you develop the skills you need to become a successful Data Engineer. You will learn how to use popular data science tools such as Jupyter Notebooks, RStudio IDE, Apache Zeppelin, and Data Science Experience. You will also learn how to write code in Python, R, and Scala.

See salaries and explore the career path for Data Engineer

Data Scientist

Data Scientists use their knowledge of data analysis and machine learning to extract insights from data. They work in a variety of industries, including finance, healthcare, and technology. This course can help you develop the skills you need to become a successful Data Scientist. You will learn how to use popular data science tools such as Jupyter Notebooks, RStudio IDE, Apache Zeppelin, and Data Science Experience. You will also learn how to write code in Python, R, and Scala. This course may also be helpful for those who wish to pursue a career in Machine Learning or Data Analytics.

See salaries and explore the career path for Data Scientist

Web Developer

Web Developers design and develop websites. They work in a variety of industries, including finance, healthcare, and technology. This course can help you develop the skills you need to become a successful Web Developer. You will learn how to use popular data science tools such as Jupyter Notebooks, RStudio IDE, Apache Zeppelin, and Data Science Experience. You will also learn how to write code in Python, R, and Scala.

See salaries and explore the career path for Web Developer

Software Engineer

Software Engineers design, develop, and maintain software applications. They work in a variety of industries, including finance, healthcare, and technology. This course can help you develop the skills you need to become a successful Software Engineer. You will learn how to use popular data science tools such as Jupyter Notebooks, RStudio IDE, Apache Zeppelin, and Data Science Experience. You will also learn how to write code in Python, R, and Scala.

See salaries and explore the career path for Software Engineer

Data Science Manager

Data Science Managers lead teams of data scientists and data analysts. They work in a variety of industries, including finance, healthcare, and technology. This course can help you develop the skills you need to become a successful Data Science Manager. You will learn how to use popular data science tools such as Jupyter Notebooks, RStudio IDE, Apache Zeppelin, and Data Science Experience. You will also learn how to manage teams and projects.

See salaries and explore the career path for Data Science Manager

Statistician

Statisticians collect, analyze, and interpret data. They work in a variety of industries, including finance, healthcare, and technology. This course can help you develop the skills you need to become a successful Statistician. You will learn how to use popular data science tools such as Jupyter Notebooks, RStudio IDE, Apache Zeppelin, and Data Science Experience. You will also learn how to write code in Python, R, and Scala.

See salaries and explore the career path for Statistician

Financial Analyst

Financial Analysts analyze financial data to help businesses make better decisions. They work in a variety of industries, including finance, healthcare, and technology. This course can help you develop the skills you need to become a successful Financial Analyst. You will learn how to use popular data science tools such as Jupyter Notebooks, RStudio IDE, Apache Zeppelin, and Data Science Experience. You will also learn how to write code in Python, R, and Scala.

See salaries and explore the career path for Financial Analyst

Business Analyst

Business Analysts analyze data to help businesses make better decisions. They work in a variety of industries, including finance, healthcare, and technology. This course can help you develop the skills you need to become a successful Business Analyst. You will learn how to use popular data science tools such as Jupyter Notebooks, RStudio IDE, Apache Zeppelin, and Data Science Experience. You will also learn how to write code in Python, R, and Scala.

See salaries and explore the career path for Business Analyst

Quantitative Analyst

Quantitative Analysts use mathematics and statistics to analyze financial data. They work in a variety of industries, including finance, healthcare, and technology. This course can help you develop the skills you need to become a successful Quantitative Analyst. You will learn how to use popular data science tools such as Jupyter Notebooks, RStudio IDE, Apache Zeppelin, and Data Science Experience. You will also learn how to write code in Python, R, and Scala.

See salaries and explore the career path for Quantitative Analyst

Actuary

Actuaries use mathematics and statistics to assess risk. They work in a variety of industries, including finance, healthcare, and technology. This course can help you develop the skills you need to become a successful Actuary. You will learn how to use popular data science tools such as Jupyter Notebooks, RStudio IDE, Apache Zeppelin, and Data Science Experience. You will also learn how to write code in Python, R, and Scala.

See salaries and explore the career path for Actuary

Operations Research Analyst

Operations Research Analysts use data to help businesses make better decisions. They work in a variety of industries, including finance, healthcare, and technology. This course can help you develop the skills you need to become a successful Operations Research Analyst. You will learn how to use popular data science tools such as Jupyter Notebooks, RStudio IDE, Apache Zeppelin, and Data Science Experience. You will also learn how to write code in Python, R, and Scala.

See salaries and explore the career path for Operations Research Analyst

Data Scientist Intern

Data Scientist Interns work under the supervision of experienced Data Scientists. They learn how to use data science tools and techniques to solve real-world problems. This course can help you develop the skills you need to become a successful Data Scientist Intern. You will learn how to use popular data science tools such as Jupyter Notebooks, RStudio IDE, Apache Zeppelin, and Data Science Experience. You will also learn how to write code in Python, R, and Scala.

See salaries and explore the career path for Data Scientist Intern

Data Analyst Intern

Data Analyst Interns work under the supervision of experienced Data Analysts. They learn how to use data analysis tools and techniques to solve real-world problems. This course can help you develop the skills you need to become a successful Data Analyst Intern. You will learn how to use popular data science tools such as Jupyter Notebooks, RStudio IDE, Apache Zeppelin, and Data Science Experience. You will also learn how to write code in Python, R, and Scala.

See salaries and explore the career path for Data Analyst Intern

Reading list

We've selected 14 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 Herramientas para la ciencia de datos.

The Data Science Handbook

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Comprehensive guide to data science. It covers a wide range of topics, including data collection, analysis, visualization, and modeling.

Herramientas para la ciencia de datos

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Reviews summary

Herramientas prácticas para la ciencia de datos

Activities

Career center

Reading list

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