Аннотация
Data science is an interdisciplinary field encompassing scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured. It draws principles from mathematics, statistics, information science, computer science, machine learning, visualization, data mining, and predictive analytics. However, it is fundamentally grounded in mathematics.
This book explains and applies the fundamentals of data science crucial for technical professionals such as DBAs and developers who are making career moves toward practicing data science. It is an example-driven book providing complete Python coding examples to complement and clarify data science concepts, and enrich the learning experience.
Coding examples include visualizations whenever appropriate. The book is a necessary precursor to applying and implementing machine learning algorithms, because it introduces the reader to foundational principles of the science of data.
The book is self-contained. All the math, statistics, stochastic, and programming skills required to master the content are covered in the book. In-depth knowledge of object-oriented programming isn’t required, because working and complete examples are provided and explained.
The examples are in-depth and complex when necessary to ensure the acquisition of appropriate data science acumen. The book helps you to build the foundational skills necessary to work with and understand complex data science algorithms.
![Using Asyncio in Python [Understanding Python’s Asynchronous Programming Features]](https://www.rulit.me/data/programs/images/using-asyncio-in-python-understanding-python-s-asynchronous_606937.jpg "Python 3.4 introduced the asyncio library, and Python 3.5 produced the async and await keywords to use it palatably. These new additions allow so-called asynchronousprogramming.
All of these new features, which I’ll refer to under the single name Asyncio, have been received by the Python...")
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