Python Data Science Essentials

Python Data Science Essentials

by Luca Massaron, Alberto Boschetti

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Overview

Gain useful insights from your data using popular data science tools

About This Book
  • 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
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.

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
In Detail

Fully 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 brings modern insight into the core of Python, including the latest versions of the Jupyter notebook, NumPy, pandas, and scikit-learn.

This book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques across data collection, data munging and analysis, visualization, and reporting activities. You will also understand advanced data science topics such as machine learning landscapes, distributed computing, building 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 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.

Product Details

ISBN-13: 9781789537864
Publisher: Packt Publishing
Publication date: 09/28/2018
Pages: 472
Sales rank: 1,064,175
Product dimensions: 7.50(w) x 9.25(h) x 0.95(d)

About the Author

Luca Massaron is a data scientist and a marketing research director specialized in multivariate statistical analysis, machine learning and customer insight with over a decade of experience in solving real world problems and in generating value for stakeholders by applying reasoning, statistics, data mining and algorithms. From being a pioneer of Web audience analysis in Italy to achieving the rank of top ten Kaggler, he has always been passionate about everything regarding data and analysis and about demonstrating the potentiality of data-driven knowledge discovery to both experts and non-experts. Favouring simplicity over unnecessary sophistication, he believes that a lot can be achieved in data science just by doing the essential.

Alberto Boschetti is a data scientist with expertise in signal processing and statistics. He holds a PhD in telecommunication engineering and currently lives and works in London. In his work projects, he faces challenges ranging from NLP, behavioral analysis, machine learning and deep nets to 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.

Table of Contents

Table of Contents

  1. First Steps
  2. Data Munging
  3. The Data Pipeline
  4. Machine Learning
  5. Visualization, Insights, and Results
  6. Social Network Analysis
  7. Deep Learning Beyond the Basics
  8. Spark for Big Data
  9. Appendix A: Strengthen Your Python Foundations

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