Data Science: A First Introduction with Python focuses on using the Python programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference. It emphasizes workflows that are clear, reproducible, and shareable, and includes coverage of the basics of version control. Based on educational research and active learning principles, the book uses a modern approach to Python and includes accompanying autograded Jupyter worksheets for interactive, self-directed learning. The text will leave readers well-prepared for data science projects. It is designed for learners from all disciplines with minimal prior knowledge of mathematics and programming. The authors have honed the material through years of experience teaching thousands of undergraduates at the University of British Columbia. Key Features: Includes autograded worksheets for interactive, self-directed learning. Introduces readers to modern data analysis and workflow tools such as Jupyter notebooks and GitHub, and covers cutting-edge data analysis and manipulation Python libraries such as pandas, scikit-learn, and altair. Is designed for a broad audience of learners from all backgrounds and disciplines.
Data Science: A First Introduction with Python focuses on using the Python programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference. It emphasizes workflows that are clear, reproducible, and shareable, and includes coverage of the basics of version control. Based on educational research and active learning principles, the book uses a modern approach to Python and includes accompanying autograded Jupyter worksheets for interactive, self-directed learning. The text will leave readers well-prepared for data science projects. It is designed for learners from all disciplines with minimal prior knowledge of mathematics and programming. The authors have honed the material through years of experience teaching thousands of undergraduates at the University of British Columbia. Key Features: Includes autograded worksheets for interactive, self-directed learning. Introduces readers to modern data analysis and workflow tools such as Jupyter notebooks and GitHub, and covers cutting-edge data analysis and manipulation Python libraries such as pandas, scikit-learn, and altair. Is designed for a broad audience of learners from all backgrounds and disciplines.
Depuis que la route de Nell a recroisé celle de Macsen, tout va de travers. Elle commence déjà à regretter de lui avoir accordé sa confiance, elle qui n’est vraiment pas prête à encaisser une nouvelle trahison. Ses meilleurs amis s’inquiètent pour elle, surtout depuis qu’elle a été renversée par une voiture alors qu’elle participait à une course clandestine. Pourtant, la convalescence de Nell est de courte durée : son frère, toujours en prison, a plus que jamais besoin de son aide.De son côté, Mac est complètement perdu. Il ne comprend pas le soudain revirement de la belle brune. Elle ne vient plus travailler, et toutes ses tentatives de la contacter sont un échec cuisant. Entre retrouvailles et révélations, Macsen et Nell vont devoir faire face à la vérité. Aussi douloureuse soit-elle.
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