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Data Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data science using tools developed in Python.
It aims to present the reader with concepts in data science and analytics that were deemed to be more advanced or simply out of scope in the author's first book, and are used in data analytics using tools developed in Python such as SciKit Learn, Pandas, Numpy, etc.
This module goes beyond the fundamentals, focusing on advanced concepts and prac-tices in programming for data science. It introduces modularization and object-oriented programming (OOP) as the foundation for writing clean, reusable, and maintainable code.
examination of advanced data science techniques—spanning ensemble methods, gradient boosting frameworks, deep neural architectures, and ...
In this document we outline guidance notes and the process for how an individual can apply for Advanced Data Science Professional and the process and standards against which they are assess.
This study has presented a comprehensive exploration of advanced data science techniques integrated with machine learning to enhance predictive analytics and decision-making across diverse industries.
This chapter provides a comprehensive introduction to the scope, components, tools, and ethical considerations of advanced data science. It sets the stage for deeper exploration in subsequent chapters.
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