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via Udemy |
Go to Course: https://www.udemy.com/course/bootcamp-de-data-science-com-python/
Certainly! Here is a comprehensive review and recommendation for this Coursera course on Data Science: --- **Course Overview and Content:** This comprehensive Data Science course on Coursera is an excellent choice for anyone looking to build a strong foundation and advanced skills in the field. The course offers over 250 hands-on exercises, guiding students through every stage of data analysis — from fundamental concepts to more complex techniques. It emphasizes active learning, making the student the protagonist in their educational journey by engaging with solved problems, summary notebooks, and real-world simulation exercises. **Key Topics and Skills Covered:** - **Practical Programming and Problem Solving:** The course focuses heavily on learning Python through practical exercises related to common data science tasks such as data cleaning, handling missing values, splitting datasets into training and testing sets, and merging datasets. - **Python Libraries and Tools:** Mastery of essential libraries like NumPy, Pandas, Matplotlib, and Seaborn is central to the course. You'll learn to manipulate arrays, perform matrix operations, analyze dataframes, visualize datasets, and interpret visual data representations. - **Basic to Advanced Concepts:** The curriculum revisits fundamental concepts like Linear Algebra via NumPy, providing a strong mathematical foundation necessary for data analysis and machine learning. - **Data Preparation for Machine Learning:** Practical examples demonstrate how to prepare datasets for machine learning projects, a crucial skill in real-world data science. - **Additional Resources:** The course includes an ebook covering Python fundamentals — from variables and data types to control structures and data collections — to reinforce programming basics vital for more complex analyses. - **Specialized Topics:** It introduces key concepts in time series analysis and finance, expanding the student's ability to apply data science techniques in various domains. **Review and Recommendations:** This course is highly recommended for beginners and intermediate learners aiming to develop a solid expertise in Data Science. Its focus on active learning, with numerous exercises and real-world applications, helps deepen understanding and build confidence in handling data projects. The inclusion of multiple libraries and practical scenarios ensures students develop versatile skills that are directly applicable in the industry. The structured approach—from foundational Python skills to advanced data manipulation and visualization—makes it suitable for those looking to start or enhance their Data Science journey. The variety of exercises ensures hands-on experience, which is essential for mastering complex concepts in this dynamic field. **Final Verdict:** If you're looking for a comprehensive, practical, and well-structured course to become proficient in Data Science, this Coursera offering is an excellent choice. Its emphasis on active participation, coupled with extensive content coverage, makes it an invaluable resource for aspiring data scientists, analysts, or anyone interested in data-driven decision-making. --- Feel free to ask if you'd like a more tailored review or additional details!
Esse é um curso completo para a formação de Cientista de Dados, com mais de 250 exercícios de A-Z, abordando desde os conceitos mais básicos até os mais avançados. Foca em metodologias ativas, onde o aluno é protagonista nesse processo, assim, trazemos diversos exercícios resolvidos, notebooks de resumo dos conteúdos e muito mais, com o foco na aprendizagem da programação baseada na prática e simulação de problemas reais (como limpeza de dados, tratamento de missings, separação de dados em treino e teste, agrupamento e junção de datasets, dentre outros).Neste sentido, o curso possui exercícios resolvidos sobre as principais bibliotecas do Python para Data Science: NumPy, Pandas, Matplotlib e Seaborn. Além do que, busca resgatar conceitos elementares da Álgebra Linear, por meio da biblioteca NumPy. Em linhas gerais, o curso apresenta exercícios que englobam as principais funções do NumPy para Data Science, como funções de agregação, definição de matrizes, operações matricias, dentre outras. Quanto ao Pandas, busca-se oferecer um panorama geral partindo da definição de Series e DataFrames, inspeção de datasets, seleção booleana, filtro de linhas de colunas, remoção de linhas e colunas, tratamento de dados ausentes, funções de agrupamento e junção, abertura e escrita de arquivos, funções de estatística descritiva, dentre outros tópicos. Por fim, apresentam-se diversos problemas relacionados a visualização de dados, com as bibliotecas Matplotlib e Seaborn, a partir de datasets clássicos. Noções de Séries temporais e Finanças também são introduzidas. Há ainda exemplos de como preparar um dataset para um projeto de Machine Learning.O curso possui ainda um E-book de fundamentos de Python, abordando os seguintes tópicos:Primeiros passos com Python!Declaração de variáveis e tipos primitivosStringsOperadoresEstruturas condicionaisEstruturas de repetiçãoEstruturas de dados