Python Data Science Essentials
A practitioner's guide covering essential data science principles, tools, and techniques, 3rd Edition
3rd Edition
Published on 28. September 2018
Book
Paperback/Softback
472 pages
978-1-78953-786-4 (ISBN)
Description
Gain useful insights from your data using popular data science tools
Key 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 forIf 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.
Key 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 forIf 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.
More details
Edition
3rd Revised edition
Language
English
Place of publication
Birmingham
United Kingdom
Edition type
Revised edition
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 26 mm
Weight
874 gr
ISBN-13
978-1-78953-786-4 (9781789537864)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Alberto Boschetti | Luca Massaron | Pietro Marinelli
Python Data Science Essentials
A practitioner's guide covering essential data science principles, tools, and techniques
E-Book
09/2018
3rd Edition
De Gruyter
€34.99
Available for download
Persons
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 and marketing research director specialized in multivariate statistical analysis, machine learning, and customer insight, with over a decade of experience of solving real-world problems and 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 a top-10 Kaggler, he has always been very passionate about every aspect of data and its analysis, and also about demonstrating the potential of data-driven knowledge discovery to both experts and non-experts. Favoring simplicity over unnecessary sophistication, Luca believes that a lot can be achieved in data science just by doing the essentials.
Content
Table of Contents
First Steps
Data Munging
The Data Pipeline
Machine Learning
Visualization, Insights, and Results
Social Network Analysis
Deep Learning Beyond the Basics
Spark for Big Data
Appendix A: Strengthen Your Python Foundations
First Steps
Data Munging
The Data Pipeline
Machine Learning
Visualization, Insights, and Results
Social Network Analysis
Deep Learning Beyond the Basics
Spark for Big Data
Appendix A: Strengthen Your Python Foundations