Author: Alberto Boschetti
Publisher: Packt Publishing Ltd
ISBN: 1786462834
Category : Computers
Languages : en
Pages : 373
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Book Description
Become an efficient data science practitioner by understanding Python's key concepts About This Book 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 Who This Book Is For If 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. 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 In Detail Fully 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. Style and approach The book is structured as a data science project. You will always benefit from clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.
Author: Craig Moodie
Publisher: Roaring Brook Press
ISBN: 1429917954
Category : Young Adult Fiction
Languages : en
Pages : 216
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Book Description
"THE BOAT IS SINKING. YOU HAVE TO MOVE." When Luke's sailing trip goes horribly wrong, he must face the vast and brutal sea in this story of one boy's survival and coming-of-age. On the evening before Luke's family's annual summer sailing trip off Cape Cod, Luke's mother leaves. Luke is left with his angry, confused father on a small boat for a week and the trip goes horribly wrong when a summer storm sweeps Luke's father overboard. Not knowing whether his father is dead or alive, Luke must figure out how to survive on a wrecked sailboat far out to sea. Fans of Gary Paulsen and Will Hobbs will be captivated by Craig Moodie's depiction of the North Atlantic in this coming-of-age adventure.
Author: Jake VanderPlas
Publisher: "O'Reilly Media, Inc."
ISBN: 1491912138
Category : Computers
Languages : en
Pages : 609
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Book Description
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
Author: Bill Deasy
Publisher: Createspace Independent Pub
ISBN: 9781482543285
Category : Fiction
Languages : en
Pages : 214
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Book Description
It's Dan Finbar's freshman year and he's struggling to find his proper place in Harrison College. When he finally breaks through into the world of notorious loner Ransom Seaborn, their friendship is brutally cut short, leaving Finbar with nothing but Ransom's old leather journal and his sometime girlfriend Maggie. As Finbar and Maggie investigate the journal--and Ransom's soul--their discoveries illuminate the dark depths of the human heart and they find that what glitters is indeed sometimes gold.
Author: Kallur Rahman
Publisher: BPB Publications
ISBN: 9391030076
Category : Computers
Languages : en
Pages : 319
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Book Description
Build your data science skills. Start data visualization Using Python. Right away. Become a good data analyst by creating quality data visualizations using Python. KEY FEATURES ● Exciting coverage on loads of Python libraries, including Matplotlib, Seaborn, Pandas, and Plotly. ● Tons of examples, illustrations, and use-cases to demonstrate visual storytelling of varied datasets. ● Covers a strong fundamental understanding of exploratory data analysis (EDA), statistical modeling, and data mining. DESCRIPTION Data visualization plays a major role in solving data science challenges with various capabilities it offers. This book aims to equip you with a sound knowledge of Python in conjunction with the concepts you need to master to succeed as a data visualization expert. The book starts with a brief introduction to the world of data visualization and talks about why it is important, the history of visualization, and the capabilities it offers. You will learn how to do simple Python-based visualization with examples with progressive complexity of key features. The book starts with Matplotlib and explores the power of data visualization with over 50 examples. It then explores the power of data visualization using one of the popular exploratory data analysis-oriented libraries, Pandas. The book talks about statistically inclined data visualization libraries such as Seaborn. The book also teaches how we can leverage bokeh and Plotly for interactive data visualization. Each chapter is enriched and loaded with 30+ examples that will guide you in learning everything about data visualization and storytelling of mixed datasets. WHAT YOU WILL LEARN ● Learn to work with popular Python libraries and frameworks, including Seaborn, Bokeh, and Plotly. ● Practice your data visualization understanding across numerous datasets and real examples. ● Learn to visualize geospatial and time-series datasets. ● Perform correlation and EDA analysis using Pandas and Matplotlib. ● Get to know storytelling of complex and unstructured data using Bokeh and Pandas. ● Learn best practices in writing clean and short python scripts for a quicker visual summary of datasets. WHO THIS BOOK IS FOR This book is for all data analytics professionals, data scientists, and data mining hobbyists who want to be strong data visualizers by learning all the popular Python data visualization libraries. Prior working knowledge of Python is assumed. TABLE OF CONTENTS 1. Introduction to Data Visualization 2. Why Data Visualization 3. Various Data Visualization Elements and Tools 4. Using Matplotlib with Python 5. Using NumPy and Pandas for Plotting 6. Using Seaborn for Visualization 7. Using Bokeh with Python 8. Using Plotly, Folium, and Other Tools for Data Visualization 9. Hands-on Examples and Exercises, Case Studies, and Further Resources
Author: James B. Seaborn
Publisher: Springer Science & Business Media
ISBN: 1468492799
Category : Science
Languages : en
Pages : 250
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Book Description
The book provides a bridge from courses in general physics to the intermediate-level courses in classical mechanics, electrodynamics and quantum mechanics. The author bases the mathematical discussions on specific physical problems to provide a basis for developing mathematical intuition.
Author: Cyrille Rossant
Publisher: Packt Publishing Ltd
ISBN: 178328482X
Category : Computers
Languages : en
Pages : 899
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Book Description
Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists... Basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods.
Author: Wei-Meng Lee
Publisher: John Wiley & Sons
ISBN: 1119545692
Category : Computers
Languages : en
Pages : 324
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Book Description
Python makes machine learning easy for beginners and experienced developers With computing power increasing exponentially and costs decreasing at the same time, there is no better time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on desktop machines. However, machine learning is not for the faint of heart—it requires a good foundation in statistics, as well as programming knowledge. Python Machine Learning will help coders of all levels master one of the most in-demand programming skillsets in use today. Readers will get started by following fundamental topics such as an introduction to Machine Learning and Data Science. For each learning algorithm, readers will use a real-life scenario to show how Python is used to solve the problem at hand. • Python data science—manipulating data and data visualization • Data cleansing • Understanding Machine learning algorithms • Supervised learning algorithms • Unsupervised learning algorithms • Deploying machine learning models Python Machine Learning is essential reading for students, developers, or anyone with a keen interest in taking their coding skills to the next level.
Author: Allen Yu
Publisher: Packt Publishing Ltd
ISBN: 1788292138
Category : Computers
Languages : en
Pages : 328
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Book Description
Unlock deeper insights into visualization in form of 2D and 3D graphs using Matplotlib 2.x About This Book Create and customize live graphs, by adding style, color, font to make appealing graphs. A complete guide with insightful use cases and examples to perform data visualizations with Matplotlib's extensive toolkits. Create timestamp data visualizations on 2D and 3D graphs in form of plots, histogram, bar charts, scatterplots and more. Who This Book Is For This book is for anyone interested in data visualization, to get insights from big data with Python and Matplotlib 2.x. With this book you will be able to extend your knowledge and learn how to use python code in order to visualize your data with Matplotlib. Basic knowledge of Python is expected. What You Will Learn Familiarize with the latest features in Matplotlib 2.x Create data visualizations on 2D and 3D charts in the form of bar charts, bubble charts, heat maps, histograms, scatter plots, stacked area charts, swarm plots and many more. Make clear and appealing figures for scientific publications. Create interactive charts and animation. Extend the functionalities of Matplotlib with third-party packages, such as Basemap, GeoPandas, Mplot3d, Pandas, Scikit-learn, and Seaborn. Design intuitive infographics for effective storytelling. In Detail Big data analytics are driving innovations in scientific research, digital marketing, policy-making and much more. Matplotlib offers simple but powerful plotting interface, versatile plot types and robust customization. Matplotlib 2.x By Example illustrates the methods and applications of various plot types through real world examples. It begins by giving readers the basic know-how on how to create and customize plots by Matplotlib. It further covers how to plot different types of economic data in the form of 2D and 3D graphs, which give insights from a deluge of data from public repositories, such as Quandl Finance. You will learn to visualize geographical data on maps and implement interactive charts. By the end of this book, you will become well versed with Matplotlib in your day-to-day work to perform advanced data visualization. This book will guide you to prepare high quality figures for manuscripts and presentations. You will learn to create intuitive info-graphics and reshaping your message crisply understandable. Style and approach Step by step comprehensive guide filled with real world examples.
Author: Oswald Campesato
Publisher: Stylus Publishing, LLC
ISBN: 1501518852
Category : Computers
Languages : en
Pages : 310
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Book Description
This book is designed to show readers the concepts of Python 3 programming and the art of data visualization. It also explores cutting-edge techniques using ChatGPT/GPT-4 in harmony with Python for generating visuals that tell more compelling data stories. Chapter 1 introduces the essentials of Python, covering a vast array of topics from basic data types, loops, and functions to more advanced constructs like dictionaries, sets, and matrices. In Chapter 2, the focus shifts to NumPy and its powerful array operations, leading into data visualization using prominent libraries such as Matplotlib. Chapter 6 includes Seaborn's rich visualization tools, offering insights into datasets like Iris and Titanic. Further, the book covers other visualization tools and techniques, including SVG graphics, D3 for dynamic visualizations, and more. Chapter 7 covers information about the main features of ChatGPT and GPT-4, as well as some of their competitors. Chapter 8 contains examples of using ChatGPT in order to perform data visualization, such as charts and graphs that are based on datasets (e.g., the Titanic dataset). Companion files with code, datasets, and figures are available for downloading. From foundational Python concepts to the intricacies of data visualization, this book is ideal for Python practitioners, data scientists, and anyone in the field of data analytics looking to enhance their storytelling with data through visuals. It's also perfect for educators seeking material for teaching advanced data visualization techniques. FEATURES Explores cutting-edge techniques using ChatGPT/GPT-4 in harmony with Python for generating visuals that tell more compelling data stories Contains detailed tutorials that guide you through the creation of complex visuals Tackles actual data scenarios and builds your expertise as you apply learned concepts to real datasets Features data manipulation and cleaning with Pandas to prepare flawless datasets ready for visualization Includes companion files with source code, data sets, and figures
