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With this handbook, you'll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: ... And this is what you will learn in Python Data Science. You will learn about the main steps that are needed to correctly implement Data Science techniques and the algorithms to help you sort through the data and see some amazing results. The book aligns with the latest ACM/IEEE CS-and-related computing curriculum initiatives and with the Data Science Undergraduate Curriculum Proposal sponsored by the National Science Foundation. This book is the ideal introduction to data science with Microsoft SQL Server and In-Database ML Services. 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Found inside – Page 1Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book. Found insideThis book gives you hands-on experience with the most popular Python data science libraries, Scikit-learn and StatsModels. After reading this book, you’ll have the solid foundation you need to start a career in data science. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn. This book presents some of the most important modeling and prediction techniques, along with relevant applications. This book content also covers the basic questions which are asked during an interview. This book is mainly intended to help people represent their answer in a sensible way to the interviewer. 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