Python Data Science Essentials
Learn the fundamentals of Data Science with Python
2nd Edition
Published on 13. January 2025
378 pages
978-1-78646-283-1 (ISBN)
Description
Become an efficient data science practitioner by understanding Python\'s key concepts
Key Features
[*] Quickly get familiar with data science using Python 3.5
[*] Save time (and effort) with all the essential tools explained
[*] Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience
Book DescriptionFully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get 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.What you will learn
[*] Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux
[*] Get data ready for your data science project
[*] Manipulate, fix, and explore data in order to solve data science problems
[*] Set up an experimental pipeline to test your data science hypotheses
[*] Choose the most effective and scalable learning algorithm for your data science tasks
[*] Optimize your machine learning models to get the best performance
[*] Explore and cluster graphs, taking advantage of interconnections and links in your data
Who this book is forIf you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills.
Key Features
[*] Quickly get familiar with data science using Python 3.5
[*] Save time (and effort) with all the essential tools explained
[*] Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience
Book DescriptionFully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get 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.What you will learn
[*] Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux
[*] Get data ready for your data science project
[*] Manipulate, fix, and explore data in order to solve data science problems
[*] Set up an experimental pipeline to test your data science hypotheses
[*] Choose the most effective and scalable learning algorithm for your data science tasks
[*] Optimize your machine learning models to get the best performance
[*] Explore and cluster graphs, taking advantage of interconnections and links in your data
Who this book is forIf you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills.
All prices
More details
Edition
2nd Revised edition
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Edition type
Revised edition
File size
5,71 MB
ISBN-13
978-1-78646-283-1 (9781786462831)
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
Python Data Science Essentials - Second Edition
Learn the fundamentals of Data Science with Python
Book
10/2016
Packt Publishing
€56.60
Shipment within 3-4 weeks
Persons
Massaron Luca :
Having joined Kaggle over 10 years ago, Luca Massaron is a Kaggle Grandmaster in discussions and a Kaggle Master in competitions and notebooks. In Kaggle competitions he reached no. 7 in the worldwide rankings. On the professional side, Luca is a data scientist with more than a decade of experience in transforming data into smarter artifacts, solving real-world problems, and generating value for businesses and stakeholders. He is a Google Developer Expert(GDE) in machine learning and the author of best-selling books on AI, machine learning, and algorithms.Boschetti Alberto :
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.
Having joined Kaggle over 10 years ago, Luca Massaron is a Kaggle Grandmaster in discussions and a Kaggle Master in competitions and notebooks. In Kaggle competitions he reached no. 7 in the worldwide rankings. On the professional side, Luca is a data scientist with more than a decade of experience in transforming data into smarter artifacts, solving real-world problems, and generating value for businesses and stakeholders. He is a Google Developer Expert(GDE) in machine learning and the author of best-selling books on AI, machine learning, and algorithms.Boschetti Alberto :
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.
Content
Table of Contents
First steps
Data munging
The Data Science Pipeline
Machine learning
Social Network Analysis
Visualization and presentation
Appendix
First steps
Data munging
The Data Science Pipeline
Machine learning
Social Network Analysis
Visualization and presentation
Appendix
