Аннотация
This book amalgamates data science and software engineering in a pragmatic manner. It guides the reader through topics from these worlds and exemplifies concepts through software. As a reader, you will gain insight into areas rarely covered in textbooks, since they are hard to explain and illustrate. You will see the Cynefin framework in action via examples that give an overarching context and systematic approach for your data science endeavors.
The book also introduces you to the most useful Python 3 data science frameworks and tools: Numpy, Pandas, scikit-learn, matplotlib, Seaborn, Dask, Apache Spark, PyTorch, and other auxiliary frameworks.
All examples are self-contained and allow you to reproduce every piece of content from the book, including graphs. The exercises at the end of each chapter advise you how to further deepen your knowledge.
Finally, the book explains, again using lots of examples, all phases of a data science life cycle model: from project initiation to data exploration and retrospection. The aim is to equip you with necessary comprehension pertaining to major areas of data science so that you may see the forest for the trees.
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