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As data science projects gets continuously larger and more complex, software engineering knowledge and experience is crucial to produce evolvable solutions. You’ll see how to create maintainable 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 perform data science. All the code will be available in the form of IPython notebooks and Python 3 programs, which allow you to reproduce all analyses from the book and customize them for your own purpose. You’ll also benefit from advanced topics like Machine Learning, Recommender Systems, and Security in Data Science.
Practical Data Science with Python will empower you analyze data, formulate proper questions, and produce actionable insights, three core stages in most data science endeavors.
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 science practices
Who This Book Is For
Anyone who would like to embark into the realm of data science using Python 3.
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