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Alex Aklson and Polong Lin
El arte de descubrir los conocimientos y las tendencias de los datos ha existido desde la antigüedad. Los antiguos egipcios usaron datos del censo para aumentar la eficiencia en la recaudación de impuestos y predijeron con precisión la inundación del río Nilo...
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El arte de descubrir los conocimientos y las tendencias de los datos ha existido desde la antigüedad. Los antiguos egipcios usaron datos del censo para aumentar la eficiencia en la recaudación de impuestos y predijeron con precisión la inundación del río Nilo cada año. Desde entonces, las personas que trabajan en ciencia de datos han creado un campo único y distinto para el trabajo que realizan. Este campo es ciencia de datos. En este curso, conoceremos a algunos profesionales de la ciencia de datos y obtendremos una visión general de lo que es hoy la ciencia de datos.
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Know what's good
, what to watch for
, and possible dealbreakers
Introduces core data science concepts, principles, and terminology
Taught by experienced data science professionals
Provides an overview of the history and evolution of data science
Suitable for learners with little to no prior data science knowledge
May be beneficial for individuals interested in exploring a career in data science

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Reviews summary

Ciencia de datos: una introducción

Este completo curso ofrece una introducción integral a la Ciencia de Datos, brindando a los estudiantes una sólida comprensión de los conceptos fundamentales, las aplicaciones y las habilidades requeridas. El curso presenta entrevistas con expertos de la industria, lo que proporciona perspectivas valiosas sobre el campo. Además, los estudiantes se benefician del acceso a herramientas prácticas en IBM Cloud, lo que les permite aplicar sus conocimientos.
Expertos de la industria comparten perspectivas valiosas.
"Completo y además las personas que hablan en los videos, manejan bastante las definiciones y dan bueno ejemplos sobre la ciencia de datos."
Excelente curso introductorio a la Ciencia de Datos.
"Excelente curso que sirve como base para conocer de que trata la ciencia de datos y cual es enfoque que debe tener un científico de datos."
Acceso a herramientas prácticas en IBM Cloud.
"Este curso es realmente amplio, la calidad de los videos y material de lectura te permite tener un 100% de comprensión, y las herramientas para ejercitar en IBM Cloud es increíble! mil gracias"
Algunas herramientas y materiales desactualizados.
"Necesita actualización"

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 ¿Qué es la ciencia de datos? with these activities:
Review basic statistics
Ensure you have a solid foundation in statistics, which is essential for data science
Browse courses on Statistics
Show steps
  • Review the concepts of mean, median, and mode
  • Practice calculating standard deviation and variance
  • Understand the difference between correlation and causation
Read 'Data Science for Business'
Introduce yourself to data science concepts at a beginner level
Show steps
  • Read the first five chapters
  • Take notes on the key concepts
  • Complete the exercises at the end of each chapter
Attend a data science meetup
Connect with other data science professionals and learn about industry trends
Browse courses on Networking
Show steps
  • Find a meetup in your area
  • Attend the meetup and introduce yourself
  • Follow up with people you meet
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice SQL queries
Get hands-on experience with SQL, a common tool in data science
Browse courses on SQL
Show steps
  • Create a database
  • Populate it with data
  • Write queries to retrieve and manipulate data
Create a data visualization dashboard
Develop your data storytelling skills by creating a visual representation of data
Browse courses on Data Visualization
Show steps
  • Choose a dataset
  • Select the appropriate charts and graphs
  • Create the dashboard and share it with others
Develop a machine learning model
Gain practical experience with machine learning techniques
Browse courses on Machine Learning
Show steps
  • Choose a dataset
  • Preprocess the data
  • Select and train a machine learning algorithm
  • Evaluate the model
Participate in a data science competition
Test your skills and learn from others in a competitive environment
Browse courses on Kaggle
Show steps
  • Find a competition that aligns with your interests
  • Form a team or work individually
  • Submit your solution

Career center

Learners who complete ¿Qué es la ciencia de datos? will develop knowledge and skills that may be useful to these careers:
Data Scientist
A Data Scientist is a specialist who uses data analysis and scientific methods to extract knowledge and insights from data. This course, ¿Qué es la ciencia de datos?, provides a comprehensive overview of the field of data science, including its history, key concepts, and applications. By taking this course, you will gain a strong foundation in the principles and practices of data science, which will prepare you for a successful career in this field.
Data Analyst
A Data Analyst is responsible for collecting, cleaning, and analyzing data to identify trends and patterns. This course, ¿Qué es la ciencia de datos?, provides an introduction to the field of data science, including the key concepts and methods used by data analysts. By taking this course, you will gain a solid understanding of the principles and practices of data analysis, which will prepare you for a successful career in this field.
Machine Learning Engineer
A Machine Learning Engineer is responsible for designing, developing, and deploying machine learning models. This course, ¿Qué es la ciencia de datos?, provides an overview of the field of data science, including the key concepts and methods used by machine learning engineers. By taking this course, you will gain a solid understanding of the principles and practices of machine learning, which will prepare you for a successful career in this field.
Data Engineer
A Data Engineer is responsible for designing, building, and maintaining data pipelines. This course, ¿Qué es la ciencia de datos?, provides an introduction to the field of data science, including the key concepts and methods used by data engineers. By taking this course, you will gain a solid understanding of the principles and practices of data engineering, which will prepare you for a successful career in this field.
Business Analyst
A Business Analyst is responsible for analyzing business processes and identifying opportunities for improvement. This course, ¿Qué es la ciencia de datos?, provides an introduction to the field of data science, including the key concepts and methods used by business analysts. By taking this course, you will gain a solid understanding of the principles and practices of business analysis, which will prepare you for a successful career in this field.
Statistician
A Statistician is responsible for collecting, analyzing, and interpreting data. This course, ¿Qué es la ciencia de datos?, provides an introduction to the field of data science, including the key concepts and methods used by statisticians. By taking this course, you will gain a solid understanding of the principles and practices of statistics, which will prepare you for a successful career in this field.
Operations Research Analyst
An Operations Research Analyst is responsible for using mathematical and statistical methods to solve business problems. This course, ¿Qué es la ciencia de datos?, provides an introduction to the field of data science, including the key concepts and methods used by operations research analysts. By taking this course, you will gain a solid understanding of the principles and practices of operations research, which will prepare you for a successful career in this field.
Financial Analyst
A Financial Analyst is responsible for analyzing financial data and making investment recommendations. This course, ¿Qué es la ciencia de datos?, provides an introduction to the field of data science, including the key concepts and methods used by financial analysts. By taking this course, you will gain a solid understanding of the principles and practices of financial analysis, which will prepare you for a successful career in this field.
Market Research Analyst
A Market Research Analyst is responsible for collecting and analyzing data about markets and consumer behavior. This course, ¿Qué es la ciencia de datos?, provides an introduction to the field of data science, including the key concepts and methods used by market research analysts. By taking this course, you will gain a solid understanding of the principles and practices of market research, which will prepare you for a successful career in this field.
Actuary
An Actuary is responsible for assessing and managing financial risks. This course, ¿Qué es la ciencia de datos?, provides an introduction to the field of data science, including the key concepts and methods used by actuaries. By taking this course, you will gain a solid understanding of the principles and practices of actuarial science, which will prepare you for a successful career in this field.
Biostatistician
A Biostatistician is responsible for designing and analyzing statistical studies in the field of medicine. This course, ¿Qué es la ciencia de datos?, provides an introduction to the field of data science, including the key concepts and methods used by biostatisticians. By taking this course, you will gain a solid understanding of the principles and practices of biostatistics, which will prepare you for a successful career in this field.
Data Architect
A Data Architect is responsible for designing and managing the architecture of data systems. This course, ¿Qué es la ciencia de datos?, provides an introduction to the field of data science, including the key concepts and methods used by data architects. By taking this course, you will gain a solid understanding of the principles and practices of data architecture, which will prepare you for a successful career in this field.
Database Administrator
A Database Administrator is responsible for managing and maintaining databases. This course, ¿Qué es la ciencia de datos?, provides an introduction to the field of data science, including the key concepts and methods used by database administrators. By taking this course, you will gain a solid understanding of the principles and practices of database administration, which will prepare you for a successful career in this field.
Software Engineer
A Software Engineer is responsible for designing, developing, and maintaining software applications. This course, ¿Qué es la ciencia de datos?, provides an introduction to the field of data science, including the key concepts and methods used by software engineers. By taking this course, you will gain a solid understanding of the principles and practices of software engineering, which will prepare you for a successful career in this field.

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 ¿Qué es la ciencia de datos?.
Textbook that provides a comprehensive overview of data science. It covers topics such as data mining, machine learning, and statistical modeling. It valuable resource for anyone who wants to learn more about the theoretical foundations of data science.
Textbook that provides a comprehensive overview of deep learning. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for anyone who wants to learn more about the theoretical foundations of deep learning.
Textbook that provides a comprehensive overview of statistical learning. It covers topics such as linear regression, logistic regression, and decision trees. It valuable resource for anyone who wants to learn more about the theoretical foundations of statistical learning.
Provides a comprehensive overview of data science, covering topics such as data mining, machine learning, and statistical modeling. It valuable resource for anyone who wants to learn more about the field of data science.
Practical guide to using Python for deep learning. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for anyone who wants to learn more about the practical aspects of deep learning using Python.
Practical guide to using Python for machine learning. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone who wants to learn more about the practical aspects of machine learning using Python.
Practical guide to data science, covering topics such as data cleaning, data analysis, and data visualization. It valuable resource for anyone who wants to learn more about the practical aspects of data science.
Practical guide to using R for data science. It covers topics such as data cleaning, data analysis, and data visualization. It valuable resource for anyone who wants to learn more about the practical aspects of data science using R.
Practical guide to using Python for data science. It covers topics such as data cleaning, data analysis, and data visualization. It valuable resource for anyone who wants to learn more about the practical aspects of data science using Python.
Practical guide to using Python for natural language processing. It covers topics such as text classification, text clustering, and sentiment analysis. It valuable resource for anyone who wants to learn more about the practical aspects of natural language processing using Python.
Practical guide to data science, covering topics such as data cleaning, data analysis, and data visualization. It valuable resource for anyone who wants to learn more about the practical aspects of data science.
Practical guide to data science, covering topics such as data cleaning, data analysis, and data visualization. It valuable resource for anyone who wants to learn more about the practical aspects of data science.

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