Practical Data Science with Python 3

Gain insight into essential data science skills in a holistic manner using data engineering andassociated scalable computational methods. This book covers the most popular Python 3frameworks for both local and distributed (in premise and cloud based) processing. Alongthe way, you will be introduced to many popular open-source frameworks, like, SciPy, scikitlearn,Numba, Apache Spark, etc. The book is structured around examples, so you will graspcore concepts via case studies and Python 3 code. As data science projects gets continuously larger and more complex, software engineeringknowledge and experience is crucial to produce evolvable solutions. You'll see how to createmaintainable software for data science and how to document data engineering practices. This book is a good starting point for people who want to gain practical skills to performdata science. All the code willbe available in the form of IPython notebooks and Python 3programs, which allow you to reproduce all analyses from the book and customize them foryour own purpose. You'll also benefit from advanced topics like Machine Learning,Recommender Systems, and Security in Data Science. Practical Data Science with Pythonwill empower you analyze data, formulate properquestions, and produce actionable insights, three core stages in most data scienceendeavors. What You'll Learn Play the role of a data scientist when completing increasingly challenging exercises using Python 3 Work work with proven data science techniques/technologies Review scalable software engineering practices to ramp up data analysis abilities in the realm of Big Data Apply theory of probability, statistical inference, and algebra to understand the data sciencepractices Who This Book Is For Anyone who would like to embark into the realm of data science usingPython 3.