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Alex Aklson
and
Polong Lin
Apesar do recente aumento do poder da computação e do acesso a dados nas últimas décadas, nossa capacidade de usar os dados em um processo de decisão é frequentemente perdida ou não maximizada. Não temos uma compreensão sólida da pergunta que está sendo feita e como aplicar os dados corretamente à questão.
Este curso busca compartilhar uma metodologia que possa ser usada dentro da ciência de dados, para garantir que os dados usados em resoluções de problemas sejam relevantes e devidamente operados.
Assim, neste curso, você aprenderá:
- As principais etapas da resolução de problemas de ciência de dados.
- As principais...
Read more
Apesar do recente aumento do poder da computação e do acesso a dados nas últimas décadas, nossa capacidade de usar os dados em um processo de decisão é frequentemente perdida ou não maximizada. Não temos uma compreensão sólida da pergunta que está sendo feita e como aplicar os dados corretamente à questão.
Este curso busca compartilhar uma metodologia que possa ser usada dentro da ciência de dados, para garantir que os dados usados em resoluções de problemas sejam relevantes e devidamente operados.
Assim, neste curso, você aprenderá:
- As principais etapas da resolução de problemas de ciência de dados.
- As principais...
Read more
Apesar do recente aumento do poder da computação e do acesso a dados nas últimas décadas, nossa capacidade de usar os dados em um processo de decisão é frequentemente perdida ou não maximizada. Não temos uma compreensão sólida da pergunta que está sendo feita e como aplicar os dados corretamente à questão.
Este curso busca compartilhar uma metodologia que possa ser usada dentro da ciência de dados, para garantir que os dados usados em resoluções de problemas sejam relevantes e devidamente operados.
Assim, neste curso, você aprenderá:
- As principais etapas da resolução de problemas de ciência de dados.
- As principais etapas da prática da ciência de dados, desde a formação de um negócio concreto ou problema de pesquisa, à coleta e análise de dados, construção de um modelo, e ao entendimento de feedback após a implementação do modelo.
- Como cientistas de dados pensam!
OFERTA POR TEMPO LIMITADO: inscrições por apenas US$ 39 por mês com acesso a materiais avaliados e um certificado.
Register for this course and see more details by visiting:
OpenCourser.com/course/nc7dhz/metodologia
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides a detailed framework for data-driven problem-solving, making it useful for those seeking to improve their decision-making skills
Teaches a structured approach to data science, fostering a rigorous understanding of the field
Imparts knowledge from industry experts, ensuring relevance and practical application of the concepts learned
Suitable for beginners seeking a comprehensive introduction to data science
Emphasizes critical thinking and problem-solving, empowering learners to approach data-driven challenges effectively
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Reviews summary
Data science problem solving
The single review for "Metodologia de Ciência de Dados" praises the course's instructors for their knowledge and expertise in data science.
Instructors are very knowledgeable.
"os instrutores tem conhecimento profundo sobre o tema"
Diploma took months to be issued.
"levou messes para sair o diploma"
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 Metodologia de Ciência de Dados with these
activities:
Review foundational data science concepts
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Before starting the course, refresh your understanding of foundational data science concepts to ensure a solid foundation for learning more advanced topics.
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Review lecture notes or textbooks from previous courses
-
Complete practice problems or online quizzes to test your understanding
Create a personal knowledge base
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Compile all materials, including video transcripts, lecture notes, and assigned readings, into a single, easily accessible repository to streamline future review.
Show steps
-
Organize files and materials into a logical structure
-
Review material for completeness and understanding
Form a study group
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Meet regularly with classmates to discuss concepts, work on assignments, and quiz each other, providing opportunities for collaborative learning and reinforcement.
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-
Find like-minded peers with complementary strengths
-
Establish a regular meeting time and place
Four other activities
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Explore online data science tutorials
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Seek out and complete online tutorials that cover specific data science concepts and techniques, providing hands-on experience and reinforcing classroom learning.
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Data Visualization
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-
Identify areas where you need additional support
-
Find high-quality tutorials from reputable sources
-
Follow the tutorials step-by-step and complete the exercises
Contribute to open source data science projects
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Participate in open-source data science projects to gain practical experience, collaborate with others, and contribute to the community while enhancing your skills.
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Data Science
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Identify open-source projects that align with your interests
-
Review the project documentation and identify ways to contribute
-
Submit pull requests with your contributions
Solve data science practice problems
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Complete a set of data science practice problems to test your understanding of concepts and algorithms, identify areas for improvement, and enhance your problem-solving skills.
Browse courses on
Data Analysis
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-
Find a collection of practice problems aligned with the course topics
-
Attempt to solve the problems independently
-
Review your solutions and identify areas where you can improve
Build a data science portfolio
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Create a portfolio of data science projects that demonstrate your skills and knowledge, providing tangible evidence of your abilities to potential employers or clients.
Browse courses on
Data Visualization
Show steps
-
Identify a real-world problem or dataset to work on
-
Apply data science techniques to solve the problem or analyze the data
-
Develop a clear and concise presentation of your findings
Review foundational data science concepts
Show steps
Before starting the course, refresh your understanding of foundational data science concepts to ensure a solid foundation for learning more advanced topics.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review lecture notes or textbooks from previous courses
- Complete practice problems or online quizzes to test your understanding
Create a personal knowledge base
Show steps
Compile all materials, including video transcripts, lecture notes, and assigned readings, into a single, easily accessible repository to streamline future review.
Show steps
Show steps
Show steps
- Organize files and materials into a logical structure
- Review material for completeness and understanding
Form a study group
Show steps
Meet regularly with classmates to discuss concepts, work on assignments, and quiz each other, providing opportunities for collaborative learning and reinforcement.
Show steps
Show steps
Show steps
- Find like-minded peers with complementary strengths
- Establish a regular meeting time and place
Four other activities
Expand to see all activities and additional details
Show all seven activities
Explore online data science tutorials
Show steps
Seek out and complete online tutorials that cover specific data science concepts and techniques, providing hands-on experience and reinforcing classroom learning.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Identify areas where you need additional support
- Find high-quality tutorials from reputable sources
- Follow the tutorials step-by-step and complete the exercises
Contribute to open source data science projects
Show steps
Participate in open-source data science projects to gain practical experience, collaborate with others, and contribute to the community while enhancing your skills.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Identify open-source projects that align with your interests
- Review the project documentation and identify ways to contribute
- Submit pull requests with your contributions
Solve data science practice problems
Show steps
Complete a set of data science practice problems to test your understanding of concepts and algorithms, identify areas for improvement, and enhance your problem-solving skills.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Find a collection of practice problems aligned with the course topics
- Attempt to solve the problems independently
- Review your solutions and identify areas where you can improve
Build a data science portfolio
Show steps
Create a portfolio of data science projects that demonstrate your skills and knowledge, providing tangible evidence of your abilities to potential employers or clients.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Identify a real-world problem or dataset to work on
- Apply data science techniques to solve the problem or analyze the data
- Develop a clear and concise presentation of your findings
Career center
Learners who complete Metodologia de Ciência de Dados will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists apply mathematical and statistical techniques to extract insights from data, helping businesses make better decisions. This course will provide you with the foundational knowledge and skills needed to succeed as a Data Scientist, including methodologies for gathering, analyzing, and interpreting data. You will also learn how to communicate your findings effectively to stakeholders.
Data Analyst
Data Analyst
Data Analysts use data to solve business problems and improve decision-making. They work with data to identify patterns and trends, and to develop recommendations for improvement. This course will provide you with the skills and knowledge needed to become a successful Data Analyst, including methodologies for data collection, analysis, and visualization.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models that can automate tasks and improve decision-making. This course will provide you with the foundational knowledge and skills needed to become a successful Machine Learning Engineer, including methodologies for data collection, model building, and deployment.
Data Engineer
Data Engineer
Data Engineers design and build the infrastructure and tools that are used to manage and process data. They work with data scientists and other stakeholders to ensure that data is available and accessible. This course will provide you with the foundational knowledge and skills needed to become a successful Data Engineer, including methodologies for data collection, storage, and processing.
Business Analyst
Business Analyst
Business Analysts use data and analytics to solve business problems and improve decision-making. They work with stakeholders to identify needs and develop solutions. This course will provide you with the skills and knowledge needed to become a successful Business Analyst, including methodologies for data collection, analysis, and visualization.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical techniques to solve problems in business and industry. They work with data to identify inefficiencies and develop solutions for improvement. This course will provide you with the foundational knowledge and skills needed to become a successful Operations Research Analyst, including methodologies for data collection, analysis, and modeling.
Statistician
Statistician
Statisticians use data to solve problems and make informed decisions. They work with data to identify patterns and trends, and to develop models for prediction and forecasting. This course will provide you with the foundational knowledge and skills needed to become a successful Statistician, including methodologies for data collection, analysis, and interpretation.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. They work with data to develop models for predicting future events and to calculate insurance premiums. This course will provide you with the foundational knowledge and skills needed to become a successful Actuary, including methodologies for data collection, analysis, and modeling.
Epidemiologist
Epidemiologist
Epidemiologists use data to study the distribution and determinants of health-related states or events in specified populations. They work with data to identify risk factors and develop strategies for prevention and control. This course will provide you with the foundational knowledge and skills needed to become a successful Epidemiologist, including methodologies for data collection, analysis, and interpretation.
Data Governance Analyst
Data Governance Analyst
Data Governance Analysts develop and implement policies and procedures for data management. They work with stakeholders to ensure that data is used in a consistent and ethical manner. This course will provide you with the foundational knowledge and skills needed to become a successful Data Governance Analyst, including methodologies for data collection, analysis, and interpretation.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data and make investment decisions. They work with data to identify trends and patterns, and to develop models for predicting future performance. This course will provide you with the foundational knowledge and skills needed to become a successful Quantitative Analyst, including methodologies for data collection, analysis, and modeling.
Market Researcher
Market Researcher
Market Researchers use data to understand consumer behavior and trends. They work with data to identify opportunities and develop marketing strategies. This course will provide you with the foundational knowledge and skills needed to become a successful Market Researcher, including methodologies for data collection, analysis, and interpretation.
Data Architect
Data Architect
Data Architects design and build the architecture for data systems. They work with stakeholders to identify data needs and develop solutions for data management and storage. This course will provide you with the foundational knowledge and skills needed to become a successful Data Architect, including methodologies for data collection, storage, and processing.
Technical Writer
Technical Writer
Technical Writers create documentation for software, products, and services. They work with subject matter experts to gather information and develop clear and concise instructions. This course will provide you with the foundational knowledge and skills needed to become a successful Technical Writer, including methodologies for gathering and organizing information.
User Experience Researcher
User Experience Researcher
User Experience Researchers study how users interact with products and services. They work with data to identify pain points and develop solutions for improvement. This course will provide you with the foundational knowledge and skills needed to become a successful User Experience Researcher, including methodologies for data collection, analysis, and interpretation.
For more career information including salaries, visit:
OpenCourser.com/course/nc7dhz/metodologia
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
Metodologia de Ciência de Dados.
Save
A comprehensive guide to deep learning, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for those who want to understand the theoretical foundations of deep learning.
Provides a comprehensive overview of deep reinforcement learning, covering topics such as deep neural networks, policy gradients, and value-based methods. It valuable resource for those who want to learn how to use deep reinforcement learning for data analysis.
Save
Provides a comprehensive overview of causal inference, covering topics such as graphical models, counterfactuals, and causal diagrams. It valuable resource for those who want to learn how to use causal inference for data analysis.
Provides a comprehensive overview of pattern recognition and machine learning, covering topics such as supervised and unsupervised learning, Bayesian inference, and decision theory. It valuable resource for those who want to learn how to use pattern recognition and machine learning for data analysis.
Provides a comprehensive overview of reinforcement learning, covering topics such as Markov decision processes, value functions, and policy gradients. It valuable resource for those who want to learn how to use reinforcement learning for data analysis.
Provides a comprehensive introduction to Bayesian statistics, using Python as the primary programming language. It valuable resource for those who want to learn how to use Bayesian statistics for data analysis.
Provides a practical introduction to machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It covers a wide range of machine learning algorithms and techniques, making it a valuable resource for those who want to gain hands-on experience.
Provides a comprehensive introduction to Bayesian statistics, using R and Stan as the primary programming languages. It valuable resource for those who want to learn how to use Bayesian statistics for data analysis.
Covers the core concepts and techniques of data science, using Python as the primary programming language. It provides a hands-on approach to learning data science, making it a valuable resource for those who want to gain practical experience.
Save
Provides a comprehensive overview of time series analysis, covering topics such as forecasting, control, and modeling. It valuable resource for those who want to learn how to use time series analysis for data analysis.
Save
Provides a comprehensive overview of econometrics, covering topics such as regression analysis, time series analysis, and forecasting. It valuable resource for those who want to learn how to use econometrics for data analysis.
Save
Provides a comprehensive overview of data science using R, covering topics such as data manipulation, analysis, and visualization. It valuable resource for those who want to learn how to use R for data science.
Provides a comprehensive overview of data science using Python, covering topics such as data manipulation, analysis, and visualization. It valuable resource for those who want to learn how to use Python for data science.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/nc7dhz/metodologia
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Via
Coursera
Institutions
IBM
IBM Skills Network
Instructors
Alex Aklson
Polong Lin
Good to know
Provides a detailed framework for data-driven problem-solving, making it useful for those seeking to improve their decision-making skills
Teaches a structured approach to data science, fostering a rigorous understanding of the field
Imparts knowledge from industry experts, ensuring relevance and practical application of the concepts learned
Suitable for beginners seeking a comprehensive introduction to data science
Emphasizes critical thinking and problem-solving, empowering learners to approach data-driven challenges effectively
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