Python Data Science Essentials: A practitioner's guide covering essential data science principles, tools, and techniques, Edition 3

Alberto Boschetti · Luca Massaron · Pietro Marinelli · Matteo Malosetti

Sep 2018 · Packt Publishing Ltd

About this ebook

Gain useful insights from your data using popular data science toolsKey Features

• A one-stop guide to Python libraries such as pandas and NumPy
• Comprehensive coverage of data science operations such as data cleaning and data manipulation
• Choose scalable learning algorithms for your data science tasks

Book DescriptionFully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn. The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost. By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users What you will learn

• Set up your data science toolbox on Windows, Mac, and Linux
• Use the core machine learning methods offered by the scikit-learn library
• Manipulate, fix, and explore data to solve data science problems
• Learn advanced explorative and manipulative techniques to solve data operations
• Optimize your machine learning models for optimized performance
• Explore and cluster graphs, taking advantage of interconnections and links in your data

Who this book is for

If you’re a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book.

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About the author

Alberto Boschetti is a data scientist with expertise in signal processing and statistics. He holds a Ph.D. in telecommunication engineering and currently lives and works in London. In his work projects, he faces challenges ranging from natural language processing (NLP) and behavioral analysis to machine learning and distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events.

Luca Massaron is a data scientist with over a decade of experience in transforming data into high-impact, innovative artifacts, solving real-world problems, and generating value for businesses and stakeholders. He is the author of numerous bestselling books on AI, machine learning, and algorithms. Luca is also a 3x Kaggle Grandmaster who reached number 7 in the worldwide user rankings for his performance in data science competitions. Additionally, he is recognized as a Google Developer Expert (GDE) in AI, Kaggle, and the cloud.

Pietro Marinelli has consistently been ranked among the top data scientists in the world in the Google Artificial Intelligence platform, Kaggle. He has reached 3rd position among Italian data scientists and 214th among 91,000 data scientists around the world. Due to his work on Kaggle, he has been honored to participate as a speaker in Paris Kaggle Day, January 2019. He has been working with artificial intelligence, text analytics, and many other data science techniques for many years, and has more than 10 years experience in designing products based on data for different industries. He has produced a variety of algorithms, ranging from predictive modeling to an advanced simulation algorithm to support senior management's business decisions for a variety of multinational companies. He is currently collaborating as a reviewer for Packt, reviewing AI books. NLP has been one of the core focuses of his projects. He has developed different algorithms for text understanding and classification in different languages (including English, Spanish, Italian, Japanese, German, French, Russian, and Chinese)

Matteo Malosetti is a mathematical engineer working as a data scientist in insurance. He is passionate about NLP applications and Bayesian statistics.