Metodología de la ciencia de datos from Coursera

A pesar del reciente aumento de la potencia informática y el acceso a los datos durante las últimas dos décadas, nuestra capacidad para utilizar los datos en el proceso de toma de decisiones se pierde o no se maximiza con demasiada frecuencia, no tenemos una comprensión sólida de las preguntas que se hacen y cómo aplicar los datos correctamente al problema en cuestión. Este curso tiene un propósito, y es compartir una metodología que se pueda utilizar dentro de la ciencia de datos, para garantizar que los datos utilizados en la resolución de problemas sean relevantes y se manipulen adecuadamente para abordar la cuestión en cuestión. En consecuencia, en este curso aprenderá: - Los principales pasos necesarios para abordar un problema de ciencia de datos. - Los principales pasos involucrados en la práctica de la ciencia de datos, desde la formación de un negocio concreto o un problema de investigación, hasta la recopilación y análisis de datos, la construcción de un modelo y la comprensión de los comentarios después de la implementación del modelo. - ¡Cómo piensan los científicos de datos! OFERTA POR TIEMPO LIMITADO: La suscripción cuesta solo $ 39 USD por mes para acceder a materiales calificados y un certificado.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Builds a strong foundation for beginners in science of data

Taught by Alex Aklson and Polong Lin, who are recognized for their work in science of data

Shares a methodology that can be used with science of data

Develops an understanding of the questions that are asked and how to apply the data correctly to the problem in question

Develops the main steps involved in the practice of science of data, from the formation of a concrete business or a research problem, to the gathering and analysis of data, the construction of a model and the understanding of the comments after the implementation of the model

Reviews summary

Metodología para principiantes en ciencia de datos

La totalidad de los estudiantes revisores de este curso de 6 semanas han dado calificaciones de 4 o 5 estrellas y lo han recomendado a otras personas porque creen que es claro, atractivo e informativo. Los estudiantes valoran especialmente que el curso enseñe a pensar como un científico de datos, con énfasis en las metodologías descriptivas y analíticas para comprender y abordar problemas con datos y modelado. Aquellos que hayan completado este curso adquirirán una metodología para utilizar datos en la toma de decisiones.

Laboratorios y ejemplos prácticos

"Ejemplos y pasos bien estructurados..."

"Muy practico, efectivo y con herramientas..."

Claro y conciso

"Está bien explicado a detalle..."

"Conceptos claros, precisos y de fácil entendimiento..."

Enseña a pensar como un científico de datos

"Excelente curso, GRACIAS ...."

"...te ayuda a entender el camino de intepretación..."

Metodología completa de ciencia de datos

"Dar un recorrido acerca de todos los pasos..."

"Me ha ayudado a comprender la metodologia aplicada..."

Falta traducción al español

"No está bien explicado, y falta traducción..."

Problemas para registrarse en los laboratorios IBM

"Los subtítulos se adelantan..."

"lo único malo es que los laboratorios IBM Cloud..."

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 Metodología de la ciencia de datos with these activities:

Review Python programming concepts

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Refresh your understanding of Python programming syntax and concepts, ensuring a strong foundation for data science.

Browse courses on Python

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Revisit Python documentation and tutorials
Practice writing basic Python programs
Review online coding challenges and exercises

Review statistics concepts

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Reinforce your understanding of statistical concepts, facilitating effective data analysis and interpretation.

Browse courses on Descriptive Statistics

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Review statistical theory and formulas
Solve practice problems and exercises
Consult supplementary materials, such as textbooks or online resources

Follow guided tutorials on data analysis techniques

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Enhance your data analysis skills by following step-by-step tutorials, deepening your understanding of practical applications.

Browse courses on Data Analysis Techniques

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Identify relevant tutorials and online courses
Follow the tutorials and complete the exercises
Apply the techniques to real-world datasets

Two other activities

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Solve practice problems on data manipulation and analysis

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Strengthen your data manipulation and analysis abilities through regular practice, improving your confidence and accuracy.

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Find online coding challenges and exercises
Practice solving problems regularly
Review solutions and identify areas for improvement

Seek mentorship from experienced data scientists

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Gain valuable guidance and support by connecting with professionals in the field, accelerating your learning journey.

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Identify potential mentors through professional networks and online platforms
Reach out to potential mentors and request their guidance
Establish regular communication and seek advice

Career center

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

Data Scientist

Data Scientists are in extremely high demand and are responsible for collecting, analyzing, and interpreting data to uncover valuable insights. They collaborate with teams of professionals, including project managers, software engineers, business analysts, and more, to ensure that data is used to make informed decisions.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers use their deep understanding of machine learning algorithms and data modeling to help their organizations make better use of their data. They build, test, and deploy machine learning models to automate complex tasks and gain a competitive advantage.

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Data Analyst

Data Analysts turn raw data into actionable insights that help inform business decisions. They collect, interpret, and visualize data to provide stakeholders with the information they need to identify trends, understand customer behavior, and make better decisions.

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Data Engineer

Data Engineers build and maintain the infrastructure that stores and processes data. They solve complex data problems and use their expertise to make data accessible, reliable, and usable for the organizations they work for.

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Business Intelligence Analyst

Business Intelligence Analysts use data to provide organizations with the insights they need to gain a competitive edge. They work closely with business leaders to identify key metrics and analyze data to inform decision-making and improve business performance.

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Data Architect

Data Architects design and manage the data infrastructure that supports an organization. They work with other IT professionals to ensure that data is stored, managed, and processed in a way that meets the needs of the organization.

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Big Data Engineer

Big Data Engineers design, build, and manage the systems that handle and process large volumes of data. They work on distributed systems and use their expertise to ensure that data is available, reliable, and scalable.

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Statistician

Statisticians use their knowledge of statistics and data analysis to interpret data and draw conclusions. They work in a variety of fields, including research, healthcare, finance, and marketing.

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Market Researcher

Market Researchers use data to understand consumer behavior and market trends. They design and conduct surveys, analyze data, and provide insights that help organizations make better decisions about their products and services.

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Financial Analyst

Financial Analysts use data to make investment recommendations and advise clients on financial matters. They analyze financial data, develop models, and make projections to help clients make informed decisions about their investments.

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Quantitative Analyst

Quantitative Analysts use data to develop and trade financial models. They use mathematical and statistical techniques to analyze data and make investment decisions.

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Actuary

Actuaries use data to assess and manage risk. They work in the insurance industry to develop and price insurance products.

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Operations Research Analyst

Operations Research Analysts use data to solve complex problems in a variety of industries. They use mathematical models and techniques to improve efficiency and productivity.

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Risk Analyst

Risk Analysts use data to assess and manage risk. They identify, analyze, and mitigate risks to help organizations make informed decisions.

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Business Analyst

Business Analysts use data to understand business processes and identify areas for improvement. They work closely with other stakeholders to define requirements and develop solutions.

See salaries and explore the career path for Business Analyst