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Joseph Santarcangelo
Saiba como analisar dados usando Python. Este curso abrange desde o básico do Python até a exploração de diferentes tipos de dados. Você aprenderá como preparar dados para análise, executar análises estatísticas simples, criar visualizações de dados significativas, prever tendências futuras a partir de dados, e muito mais!
Tópicos abordados:
1) Importação de conjuntos de dados
2) Limpeza dos dados
3) Manipulação de estrutura de dados
4) Resumo dos dados
5) Construção de modelos de regressão de aprendizado de máquina
6) Construção de dados pipeline
A análise de dados com Python será realizada por meio de palestras, laboratórios e...
Read more
Saiba como analisar dados usando Python. Este curso abrange desde o básico do Python até a exploração de diferentes tipos de dados. Você aprenderá como preparar dados para análise, executar análises estatísticas simples, criar visualizações de dados significativas, prever tendências futuras a partir de dados, e muito mais!
Tópicos abordados:
1) Importação de conjuntos de dados
2) Limpeza dos dados
3) Manipulação de estrutura de dados
4) Resumo dos dados
5) Construção de modelos de regressão de aprendizado de máquina
6) Construção de dados pipeline
A análise de dados com Python será realizada por meio de palestras, laboratórios e...
Read more
Saiba como analisar dados usando Python. Este curso abrange desde o básico do Python até a exploração de diferentes tipos de dados. Você aprenderá como preparar dados para análise, executar análises estatísticas simples, criar visualizações de dados significativas, prever tendências futuras a partir de dados, e muito mais!
Tópicos abordados:
1) Importação de conjuntos de dados
2) Limpeza dos dados
3) Manipulação de estrutura de dados
4) Resumo dos dados
5) Construção de modelos de regressão de aprendizado de máquina
6) Construção de dados pipeline
A análise de dados com Python será realizada por meio de palestras, laboratórios e tarefas. Ele inclui as seguintes partes:
Bibliotecas de análise de dados:aprenderá a usar bibliotecas Pandas, Numpy e Scipy para trabalhar com uma amostra de conjunto de dados. Apresentaremos o pandas, uma biblioteca de código aberto, e a usaremos para carregar, manipular, analisar e visualizar conjuntos de dados. Em seguida, apresentaremos outra biblioteca de código aberto, a scikit-Learn, e usaremos alguns de seus algoritmos de aprendizado de máquina para criar modelos inteligentes e fazer previsões interessantes. Se você optar por fazer este curso e receber o certificado do curso Coursera, você também ganhará um selo digital da IBM.
OFERTA LIMITADA: a assinatura custa apenas US$ 39,00 por mês para acesso a materiais classificados e a um certificado.
Register for this course and see more details by visiting:
OpenCourser.com/course/gk3q6t/analise
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches data analysis using Python and Pandas, a standard library in the field
Develops expertise in using NumPy, a core library for mathematics and data processing
Provides hands-on labs and interactive materials for practical application
Lays the foundation for further studies in data analysis
Covers a wide range of data analysis topics, from data manipulation to statistical modeling
Requires no prior programming experience, making it accessible to beginners
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Reviews summary
Informative python data analytics course
Análise de dados com Python é um curso útil para iniciantes que buscam conhecimento básico em análise de dados usando Python. Ele cobre tópicos essenciais como importação de dados, limpeza, manipulação de estrutura de dados, resumo de dados e construção de modelos de regressão de aprendizado de máquina.
Utiliza bibliotecas valiosas como Pandas e Scikit-Learn
"...aprenderá a usar bibliotecas Pandas, Numpy e Scipy para trabalhar com uma amostra de conjunto de dados..."
Curso abrangente que cobre tópicos essenciais
"...abrange desde o básico do Python até a exploração de diferentes tipos de dados..."
Suporte limitado disponível
"...muito ruim, sem suporte, ta abandonado!..."
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 Análise de dados com Python with these
activities:
Review the book 'Python for Data Analysis'
Show steps
Review the book 'Python for Data Analysis' to reinforce your understanding of the Python libraries covered in the course.
View
Python for Data Analysis
on Amazon
Show steps
-
Read the book.
-
Take notes on the key concepts.
-
Complete the exercises at the end of each chapter.
Compile your notes and assignments
Show steps
Compile your notes and assignments to create a comprehensive study resource.
Show steps
-
Gather your notes and assignments.
-
Organize your notes and assignments by topic.
-
Create a study guide.
Perform data analysis practice problems
Show steps
Practice data analysis problems to reinforce your understanding of the concepts covered in the course.
Browse courses on
Data Analysis
Show steps
-
Find data analysis practice problems online or in textbooks.
-
Solve the practice problems using the Python libraries covered in the course.
-
Check your solutions against the provided answers.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow tutorials on using Python for data analysis
Show steps
Follow tutorials on using Python for data analysis to supplement the course material.
Browse courses on
Python
Show steps
-
Find tutorials on using Python for data analysis.
-
Follow the tutorials step-by-step.
-
Complete the exercises at the end of each tutorial.
Participate in a peer study group
Show steps
Participate in a peer study group to discuss the course material and work on projects together.
Browse courses on
Data Analysis
Show steps
-
Find a peer study group to join.
-
Attend the study group meetings regularly.
-
Participate in the discussions and work on the projects.
Create a data visualization dashboard
Show steps
Create a data visualization dashboard to demonstrate your understanding of data visualization techniques.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset to work with.
-
Clean and prepare the data.
-
Create visualizations to represent the data.
-
Organize the visualizations into a dashboard.
Attend a data analysis workshop
Show steps
Attend a data analysis workshop to learn new skills and techniques.
Browse courses on
Data Analysis
Show steps
-
Find a data analysis workshop to attend.
-
Register for the workshop.
-
Attend the workshop.
Create a data analysis project
Show steps
Create a data analysis project to demonstrate your understanding of the concepts covered in the course.
Browse courses on
Data Analysis
Show steps
-
Choose a dataset to work with.
-
Clean and prepare the data.
-
Analyze the data.
-
Create visualizations to represent the data.
-
Write a report on your findings.
Review the book 'Python for Data Analysis'
Show steps
Review the book 'Python for Data Analysis' to reinforce your understanding of the Python libraries covered in the course.
View
Python for Data Analysis
on Amazon
Show steps
Show steps
Show steps
- Read the book.
- Take notes on the key concepts.
- Complete the exercises at the end of each chapter.
Compile your notes and assignments
Show steps
Compile your notes and assignments to create a comprehensive study resource.
Show steps
Show steps
Show steps
- Gather your notes and assignments.
- Organize your notes and assignments by topic.
- Create a study guide.
Perform data analysis practice problems
Show steps
Practice data analysis problems to reinforce your understanding of the concepts covered in the course.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Find data analysis practice problems online or in textbooks.
- Solve the practice problems using the Python libraries covered in the course.
- Check your solutions against the provided answers.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow tutorials on using Python for data analysis
Show steps
Follow tutorials on using Python for data analysis to supplement the course material.
Browse courses on
Python
Show steps
Show steps
Show steps
- Find tutorials on using Python for data analysis.
- Follow the tutorials step-by-step.
- Complete the exercises at the end of each tutorial.
Participate in a peer study group
Show steps
Participate in a peer study group to discuss the course material and work on projects together.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Find a peer study group to join.
- Attend the study group meetings regularly.
- Participate in the discussions and work on the projects.
Create a data visualization dashboard
Show steps
Create a data visualization dashboard to demonstrate your understanding of data visualization techniques.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset to work with.
- Clean and prepare the data.
- Create visualizations to represent the data.
- Organize the visualizations into a dashboard.
Attend a data analysis workshop
Show steps
Attend a data analysis workshop to learn new skills and techniques.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Find a data analysis workshop to attend.
- Register for the workshop.
- Attend the workshop.
Create a data analysis project
Show steps
Create a data analysis project to demonstrate your understanding of the concepts covered in the course.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Choose a dataset to work with.
- Clean and prepare the data.
- Analyze the data.
- Create visualizations to represent the data.
- Write a report on your findings.
Career center
Learners who complete Análise de dados com Python will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
This course provides a solid foundation for aspiring Data Scientists. You will learn how to analyze data using Python, which is a key skill in this field. The course covers topics like data cleaning, data manipulation, and machine learning, which are all essential for building and deploying data-driven solutions. By completing this course, you will gain a competitive advantage in the job market and increase your chances of success as a Data Scientist.
Machine Learning Engineer
Machine Learning Engineer
For aspiring Machine Learning Engineers, this course is a valuable asset. It introduces you to Python libraries such as Scikit-Learn and provides hands-on experience in building machine learning models. This knowledge is crucial for designing, implementing, and evaluating machine learning systems. The course will help you build a strong foundation in Python-based machine learning and prepare you for a successful career in this field.
Data Analyst
Data Analyst
Understanding data analysis with Python, as covered in this course, is an essential foundation to building a successful career as a Data Analyst. The course will help you gain proficiency in essential Python libraries like Pandas, NumPy, and Scikit-Learn, which will enhance your ability to clean, explore, analyze, and visualize data. It will also introduce you to machine learning techniques, providing you with a competitive edge in this in-demand field.
Quantitative Analyst
Quantitative Analyst
Finance professionals seeking to enhance their quantitative skills will find this course beneficial. By learning data analysis with Python, you will gain proficiency in handling and analyzing large datasets. The course covers essential libraries like Pandas and NumPy, which are widely used in the financial industry. Moreover, you will gain exposure to machine learning techniques, enabling you to explore predictive modeling and risk assessment.
Business Analyst
Business Analyst
For professionals seeking a career in Business Analysis, this course offers valuable insights. It will enhance your data analysis skills, enabling you to translate raw data into actionable insights for informed decision-making. The course covers data manipulation, statistical analysis, and data visualization techniques, which are essential for understanding business trends and optimizing operations.
Statistician
Statistician
This course provides a strong foundation for individuals pursuing a career in Statistics. You will learn how to use Python for data analysis, which is a valuable skill in this field. The course covers topics like data cleaning, statistical modeling, and data visualization, which are essential for analyzing and interpreting data. By completing this course, you will enhance your statistical knowledge and improve your employability as a Statistician.
Data Engineer
Data Engineer
Aspiring Data Engineers will find this course beneficial as it introduces them to the fundamentals of data analysis with Python. You will gain proficiency in data manipulation, data cleaning, and data visualization techniques. The course also covers topics like data pipelines and machine learning, which are essential for building and maintaining data infrastructure. By completing this course, you will develop a strong foundation in data engineering and increase your chances of success in this field.
Database Administrator
Database Administrator
This course may be useful for individuals interested in a career as a Database Administrator. It provides an introduction to data analysis with Python, which can enhance your ability to manage and analyze data stored in databases. The course covers topics like data cleaning, data manipulation, and data visualization, which are essential for maintaining and optimizing database systems.
Information Security Analyst
Information Security Analyst
For aspiring Information Security Analysts, this course may be helpful as it introduces you to data analysis techniques using Python. You will learn how to use Python for data exploration, data visualization, and statistical analysis. This knowledge can be valuable in identifying and analyzing security threats, conducting risk assessments, and developing security solutions.
Software Engineer
Software Engineer
This course may be useful for individuals interested in a career as a Software Engineer. It provides an introduction to data analysis with Python, which can enhance your ability to design and develop data-driven software applications. The course covers topics like data cleaning, data manipulation, and data visualization, which are essential for building software that can analyze and process data effectively.
Data Visualization Analyst
Data Visualization Analyst
This course may be useful for individuals interested in a career as a Data Visualization Analyst. It introduces you to data analysis techniques using Python, with a focus on data visualization. You will learn how to use Python to create clear and informative visualizations that communicate data insights effectively. This knowledge can be valuable in roles that require the ability to translate data into visual representations.
Market Research Analyst
Market Research Analyst
This course may be useful for individuals interested in a career as a Market Research Analyst. It provides an introduction to data analysis with Python, which can enhance your ability to analyze and interpret market research data. The course covers topics like data cleaning, statistical analysis, and data visualization, which are essential for understanding consumer behavior and market trends.
Operations Research Analyst
Operations Research Analyst
This course may be useful for individuals interested in a career as an Operations Research Analyst. It introduces you to data analysis techniques using Python, with a focus on optimization. You will learn how to use Python to develop and solve optimization models that can help improve decision-making in various industries.
Financial Analyst
Financial Analyst
This course may be useful for individuals interested in a career as a Financial Analyst. It provides an introduction to data analysis with Python, which can enhance your ability to analyze and interpret financial data. The course covers topics like data cleaning, statistical analysis, and data visualization, which are essential for understanding financial trends and making sound investment decisions.
Business Intelligence Analyst
Business Intelligence Analyst
This course may be useful for individuals interested in a career as a Business Intelligence Analyst. It introduces you to data analysis techniques using Python, with a focus on business intelligence. You will learn how to use Python to extract insights from data and develop dashboards and reports that can help businesses make informed decisions.
For more career information including salaries, visit:
OpenCourser.com/course/gk3q6t/analise
Reading list
We've selected 16 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
Análise de dados com Python.
Provides a comprehensive overview of Python for data analysis, covering topics such as data manipulation, data exploration, and data visualization. It valuable resource for learners who want to gain a deeper understanding of Python for data analysis.
Provides a comprehensive overview of the Python programming language and its use in data analysis. It covers essential topics such as data structures, data manipulation, data visualization, and machine learning algorithms. This book would be a valuable resource for learners who want to gain a deeper understanding of Python programming and its applications in data analysis.
Save
Comprehensive guide to deep learning. It covers a wide range of deep learning architectures and techniques, including convolutional neural networks, recurrent neural networks, and generative adversarial networks. This book would be a valuable resource for learners who want to gain a deep understanding of deep learning.
Covers the basics of machine learning with Python, including topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for learners who want to gain practical experience with machine learning.
Comprehensive guide to machine learning using Python. It covers a wide range of machine learning algorithms and techniques, including supervised learning, unsupervised learning, and deep learning. This book would be a valuable resource for learners who want to gain a deep understanding of machine learning using Python.
Provides a step-by-step guide to machine learning with Python. It covers topics such as data preprocessing, model selection, and model evaluation. It valuable resource for learners who want to gain a comprehensive understanding of machine learning.
Provides a comprehensive overview of deep learning with Python. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for learners who want to gain a deeper understanding of deep learning.
Provides a comprehensive introduction to data science and machine learning. It covers essential topics such as data collection, data cleaning, data analysis, and machine learning algorithms. This book would be a valuable resource for learners who want to gain a broad understanding of data science and machine learning.
Provides a comprehensive overview of Python for data science. It covers topics such as data manipulation, data exploration, and data visualization. It valuable resource for learners who want to gain a comprehensive understanding of data science.
Provides a comprehensive overview of statistical learning. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for learners who want to gain a deeper understanding of the underlying principles of statistical learning.
Provides a comprehensive overview of statistical learning. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for learners who want to gain a deeper understanding of the underlying principles of statistical learning.
Provides a comprehensive overview of Python for machine learning. It covers topics such as data preprocessing, model selection, and model evaluation. It valuable resource for learners who want to gain a comprehensive understanding of machine learning.
Provides a comprehensive overview of deep learning for natural language processing. It covers topics such as word embeddings, recurrent neural networks, and transformers. It valuable resource for learners who want to gain a deeper understanding of deep learning for natural language processing.
Provides a comprehensive overview of natural language processing with Python. It covers topics such as text preprocessing, text classification, and text generation. It valuable resource for learners who want to gain a comprehensive understanding of natural language processing.
Provides a comprehensive overview of speech and language processing. It covers topics such as speech recognition, natural language understanding, and speech synthesis. It valuable resource for learners who want to gain a comprehensive understanding of speech and language processing.
Save
Provides a comprehensive overview of computer vision. It covers topics such as image formation, image processing, and object recognition. It valuable resource for learners who want to gain a comprehensive understanding of computer vision.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/gk3q6t/analise
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Time
32 hours
Via
Coursera
Institutions
IBM
IBM Skills Network
Instructor
Joseph Santarcangelo
Good to know
Teaches data analysis using Python and Pandas, a standard library in the field
Develops expertise in using NumPy, a core library for mathematics and data processing
Provides hands-on labs and interactive materials for practical application
Lays the foundation for further studies in data analysis
Covers a wide range of data analysis topics, from data manipulation to statistical modeling
Requires no prior programming experience, making it accessible to beginners
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