Applied Data Science With Python And Jupyter

Author : Alex Galea
Publisher : Packt Publishing Ltd
Page : 192 pages
File Size : 44,8 Mb
Release : 2018-10-31
Category : Computers
ISBN : 9781789951929

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Applied Data Science with Python and Jupyter by Alex Galea Pdf

Become the master player of data exploration by creating reproducible data processing pipelines, visualizations, and prediction models for your applications. Key FeaturesGet up and running with the Jupyter ecosystem and some example datasetsLearn about key machine learning concepts such as SVM, KNN classifiers, and Random ForestsDiscover how you can use web scraping to gather and parse your own bespoke datasetsBook Description Getting started with data science doesn't have to be an uphill battle. Applied Data Science with Python and Jupyter is a step-by-step guide ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction to these concepts. In this book, you'll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You'll start with the basics of Jupyter, which will be the backbone of the book. After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques. You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations. What you will learnGet up and running with the Jupyter ecosystemIdentify potential areas of investigation and perform exploratory data analysisPlan a machine learning classification strategy and train classification modelsUse validation curves and dimensionality reduction to tune and enhance your modelsScrape tabular data from web pages and transform it into Pandas DataFramesCreate interactive, web-friendly visualizations to clearly communicate your findingsWho this book is for Applied Data Science with Python and Jupyter is ideal for professionals with a variety of job descriptions across a large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries such as Pandas, Matplotlib, and Pandas providing you a useful head start.

Author : Jake VanderPlas
Publisher : "O'Reilly Media, Inc."
Page : 743 pages
File Size : 52,7 Mb
Release : 2016-11-21
Category : Computers
ISBN : 9781491912133

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Python Data Science Handbook by Jake VanderPlas Pdf

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 : Alex Galea
Publisher : Packt Publishing Ltd
Page : 351 pages
File Size : 46,9 Mb
Release : 2020-07-22
Category : Computers
ISBN : 9781800207004

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The The Applied Data Science Workshop by Alex Galea Pdf

Designed with beginners in mind, this workshop helps you make the most of Python libraries and the Jupyter Notebook’s functionality to understand how data science can be applied to solve real-world data problems. Key FeaturesGain useful insights into data science and machine learningExplore the different functionalities and features of a Jupyter NotebookDiscover how Python libraries are used with Jupyter for data analysisBook Description From banking and manufacturing through to education and entertainment, using data science for business has revolutionized almost every sector in the modern world. It has an important role to play in everything from app development to network security. Taking an interactive approach to learning the fundamentals, this book is ideal for beginners. You’ll learn all the best practices and techniques for applying data science in the context of real-world scenarios and examples. Starting with an introduction to data science and machine learning, you’ll start by getting to grips with Jupyter functionality and features. You’ll use Python libraries like sci-kit learn, pandas, Matplotlib, and Seaborn to perform data analysis and data preprocessing on real-world datasets from within your own Jupyter environment. Progressing through the chapters, you’ll train classification models using sci-kit learn, and assess model performance using advanced validation techniques. Towards the end, you’ll use Jupyter Notebooks to document your research, build stakeholder reports, and even analyze web performance data. By the end of The Applied Data Science Workshop, you’ll be prepared to progress from being a beginner to taking your skills to the next level by confidently applying data science techniques and tools to real-world projects. What you will learnUnderstand the key opportunities and challenges in data scienceUse Jupyter for data science tasks such as data analysis and modelingRun exploratory data analysis within a Jupyter NotebookVisualize data with pairwise scatter plots and segmented distributionAssess model performance with advanced validation techniquesParse HTML responses and analyze HTTP requestsWho this book is for If you are an aspiring data scientist who wants to build a career in data science or a developer who wants to explore the applications of data science from scratch and analyze data in Jupyter using Python libraries, then this book is for you. Although a brief understanding of Python programming and machine learning is recommended to help you grasp the topics covered in the book more quickly, it is not mandatory.

Author : Alex Galea
Publisher : Packt Publishing Ltd
Page : 194 pages
File Size : 51,6 Mb
Release : 2018-06-05
Category : Computers
ISBN : 9781789534658

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Beginning Data Science with Python and Jupyter by Alex Galea Pdf

Getting started with data science doesn't have to be an uphill battle. This step-by-step guide is ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction. Key Features Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts like SVM, KNN classifiers and Random Forests Discover how you can use web scraping to gather and parse your own bespoke datasets Book Description Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You'll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world. We'll finish up by showing you how easy it can be to scrape and gather your own data from the open web, so that you can apply your new skills in an actionable context. What you will learn Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts like SVM, KNN classifiers, and Random Forests Plan a machine learning classification strategy and train classification, models Use validation curves and dimensionality reduction to tune and enhance your models Discover how you can use web scraping to gather and parse your own bespoke datasets Scrape tabular data from web pages and transform them into Pandas DataFrames Create interactive, web-friendly visualizations to clearly communicate your findings Who this book is for This book is ideal for professionals with a variety of job descriptions across large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries like Pandas, Matplotlib and Pandas providing you a useful head start.

Author : Prateek Gupta
Publisher : BPB Publications
Page : 323 pages
File Size : 48,7 Mb
Release : 2019-03-27
Category : Computers
ISBN : 9789388511377

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Data Science with Jupyter by Prateek Gupta Pdf

Step-by-step guide to practising data science techniques with Jupyter notebooks Ê Description Modern businesses are awash with data, making data driven decision-making tasks increasingly complex. As a result, relevant technical expertise and analytical skills are required to do such tasks. This book aims to equip you with just enough knowledge of Python in conjunction with skills to use powerful tool such as Jupyter Notebook in order to succeed in the role of a data scientist. Ê The book starts with a brief introduction to the world of data science and the opportunities you may come across along with an overview of the key topics covered in the book. You will learn how to setup Anaconda installation which comes with Jupyter and preinstalled Python packages. Before diving in to several supervised, unsupervised and other machine learning techniques, youÕll learn how to use basic data structures, functions, libraries and packages required to import, clean, visualize and process data.Ê Several machine learning techniques such as regression, classification, clustering, time-series etc have been explained with the use of practical examples and by comparing the performance of various models. Ê By the end of the book, you will come across few case studies to put your knowledge to practice and solve real-life business problems such as building a movie recommendation engine, classifying spam messages, predicting the ability of a borrower to repay loan on time and time series forecasting of housing prices. Remember to practice additional examples provided in the code bundle of the book to master these techniques. Ê Audience The book is intended for anyone looking for a career in data science, all aspiring data scientists who want to learn the most powerful programming language in Machine Learning or working professionals who want to switch their career in Data Science. While no prior knowledge of Data Science or related technologies is assumed, it will be helpful to have some programming experience. Ê Key Features áÊÊÊÊÊÊ Acquire Python skills to do independent data science projects áÊÊÊÊÊÊ Learn the basics of linear algebra and statistical science in Python way áÊÊÊÊÊÊ Understand how and when they're used in data science áÊÊÊÊÊÊ Build predictive models, tune their parameters and analyze performance in few steps áÊÊÊÊÊÊ Cluster, transform, visualize, and extract insights from unlabelled datasets áÊÊÊÊÊÊ Learn how to use matplotlib and seaborn for data visualization áÊÊÊÊÊÊ Implement and save machine learning models for real-world business scenarios Ê Table of Contents 1 )Ê Data Science Fundamentals 2 )Ê Installing Software and Setting up 3 )Ê Lists and Dictionaries 4 )Ê Function and Packages 5 )Ê NumPy Foundation 6 )Ê Pandas and Dataframe 7 )Ê Interacting with Databases 8 )Ê Thinking Statistically in Data Science 9 )Ê How to import data in Python? 10 ) Cleaning of imported data 11 ) Data Visualization 12 ) Data Pre-processing 13 ) Supervised Machine Learning 14 ) Unsupervised Machine Learning 15 ) Handling Time-Series Data 16 ) Time-Series Methods 17 ) Case Study Ð 1 18 ) Case Study Ð 2 19 ) Case Study Ð 3 20 ) Case Study Ð 4

Author : Alex Galea
Publisher : Unknown
Page : 352 pages
File Size : 44,5 Mb
Release : 2020-07-21
Category : Computers
ISBN : 1800202504

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The Applied Data Science Workshop, Second Edition by Alex Galea Pdf

Author : Wes McKinney
Publisher : "O'Reilly Media, Inc."
Page : 676 pages
File Size : 54,6 Mb
Release : 2017-09-25
Category : Computers
ISBN : 9781491957615

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Python for Data Analysis by Wes McKinney Pdf

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples

Author : Peter Bruce,Andrew Bruce
Publisher : "O'Reilly Media, Inc."
Page : 395 pages
File Size : 49,8 Mb
Release : 2017-05-10
Category : Computers
ISBN : 9781491952917

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Practical Statistics for Data Scientists by Peter Bruce,Andrew Bruce Pdf

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data

Author : Frank Kane
Publisher : Packt Publishing Ltd
Page : 420 pages
File Size : 40,8 Mb
Release : 2017-07-31
Category : Computers
ISBN : 9781787280229

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Hands-On Data Science and Python Machine Learning by Frank Kane Pdf

This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale machine learning using Apache Spark. About This Book Take your first steps in the world of data science by understanding the tools and techniques of data analysis Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods Learn how to use Apache Spark for processing Big Data efficiently Who This Book Is For If you are a budding data scientist or a data analyst who wants to analyze and gain actionable insights from data using Python, this book is for you. Programmers with some experience in Python who want to enter the lucrative world of Data Science will also find this book to be very useful, but you don't need to be an expert Python coder or mathematician to get the most from this book. What You Will Learn Learn how to clean your data and ready it for analysis Implement the popular clustering and regression methods in Python Train efficient machine learning models using decision trees and random forests Visualize the results of your analysis using Python's Matplotlib library Use Apache Spark's MLlib package to perform machine learning on large datasets In Detail Join Frank Kane, who worked on Amazon and IMDb's machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank's successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis. Style and approach This comprehensive book is a perfect blend of theory and hands-on code examples in Python which can be used for your reference at any time.

Author : Paul Jeon,Austin Chen
Publisher : Unknown
Page : 400 pages
File Size : 41,5 Mb
Release : 2017-03-31
Category : Electronic
ISBN : 1785880071

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Jupyter for Data Science by Paul Jeon,Austin Chen Pdf

Explore the power of Jupyter and start deploying it in various contexts with the help of this highly practical, up-to-date guideAbout This Book* Find an easy way to code, execute, document, and share your live code with this unique guide* Learn to code efficient notebooks for interactive data analysis and deploy dashboards as web applications* This example-rich guide teaches you how to use Jupyter kernels for the major players in data science-Python, R, and JuliaWho This Book Is ForThis book is for those who are already familiar with data analytics languages such as Python, Scala, or R. It will be particularly useful for those who have used Jupyter but want to take their data analytics skills to the next level by utilizing Jupyter and other data science tools.What you will learn* Understand all the functionalities of Jupyter Notebooks such as creating / sharing documents with code, equations, and visualizations* Find out how to secure and share Notebooks* Use Python, Scala, and R with multiple other packages and other web applications to build different data visualization platforms with Jupyter* Create Jupyter Extensions to build new applications* Develop code that is efficient and effective in the realm of data science / analysis* Build interactive dashboards / widgets of Notebooks* Improve the scalability and performance of Notebooks* Use Jupyter for Machine LearningIn DetailJupyter is a very popular web application that allows you to code, analyze vast amounts of data, and create visualization, text, and rich media in a single document that can be shared across people you wish to collaborate with. Initially designed and used for statistical analysis and creating visualizations out of data, Jupyter Notebook has become so popular among data scientists that over 150,000 Jupyter Notebooks have been created on GitHub.This book will take you a step further with notebooks and help you to build multiple data analytics platforms using Jupyter and other data science tools. You'll learn different ways to engineer your data and analyze it for different purposes using multiple Jupyter Notebooks. You'll begin by setting up a data science environment to create and share Jupyter Notebooks. You'll learn to create Jupyter Notebooks to analyze simple to complex big datasets and visualize using python packages.Further on, you'll learn to capitalize on Python's flexibility and R's structured statistical packages to accelerate your data science investigations for real-world applications. Additionally, we'll cover techniques to scale an application using Jupyter extensions for Spark and other dynamic widgets. By the end of the book, you'll have gained mastery over creating and integrating multiple notebooks, being able to secure and optimize them to perform intuitive, iterative, and robust analytics.

Author : Kennedy Behrman
Publisher : Pearson
Page : 816 pages
File Size : 41,5 Mb
Release : 2021-10-12
Category : Computers
ISBN : 9780136624318

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Foundational Python for Data Science by Kennedy Behrman Pdf

Learn all the foundational Python you'll need to solve real data science problems Data science and machine learning--two of the world's hottest fields--are attracting talent from a wide variety of technical, business, and liberal arts disciplines. Python, the world's #1 programming language, is also the most popular language for data science and machine learning. This is the first guide specifically designed to help millions of people with widely diverse backgrounds learn Python so they can use it for data science and machine learning. Leading data science instructor and practitioner Kennedy Behrman first walks through the process of learning to code for the first time with Python and Jupyter notebook, then introduces key libraries every Python data science programmer needs to master. Once you've learned these foundations, Behrman introduces intermediate and applied Python techniques for real-world problem-solving. Master Google colab notebook Data Science programming Manipulate data with popular Python libraries such as: pandas and numpy Apply Python Data Science recipes to real world projects Learn functional programming essentials unique to Data Science Access case studies, chapter exercises, learning assessments, comprehensive Jupyter based Notebooks, and a complete final project Throughout, Foundational Python for Data Science presents hands-on exercises, learning assessments, case studies, and more--all created with colab (Jupyter compatible) notebooks, so you can execute all coding examples interactively without installing or configuring any software.

Author : Alex Galea,Luis Capelo
Publisher : Packt Publishing Ltd
Page : 317 pages
File Size : 40,8 Mb
Release : 2018-08-31
Category : Computers
ISBN : 9781789806991

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Applied Deep Learning with Python by Alex Galea,Luis Capelo Pdf

A hands-on guide to deep learning that’s filled with intuitive explanations and engaging practical examples Key Features Designed to iteratively develop the skills of Python users who don’t have a data science background Covers the key foundational concepts you’ll need to know when building deep learning systems Full of step-by-step exercises and activities to help build the skills that you need for the real-world Book Description Taking an approach that uses the latest developments in the Python ecosystem, you’ll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before we train our first predictive model. We’ll explore a variety of approaches to classification like support vector networks, random decision forests and k-nearest neighbours to build out your understanding before we move into more complex territory. It’s okay if these terms seem overwhelming; we’ll show you how to put them to work. We’ll build upon our classification coverage by taking a quick look at ethical web scraping and interactive visualizations to help you professionally gather and present your analysis. It’s after this that we start building out our keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data. By guiding you through a trained neural network, we’ll explore common deep learning network architectures (convolutional, recurrent, generative adversarial) and branch out into deep reinforcement learning before we dive into model optimization and evaluation. We’ll do all of this whilst working on a production-ready web application that combines Tensorflow and Keras to produce a meaningful user-friendly result, leaving you with all the skills you need to tackle and develop your own real-world deep learning projects confidently and effectively. What you will learn Discover how you can assemble and clean your very own datasets Develop a tailored machine learning classification strategy Build, train and enhance your own models to solve unique problems Work with production-ready frameworks like Tensorflow and Keras Explain how neural networks operate in clear and simple terms Understand how to deploy your predictions to the web Who this book is for If you're a Python programmer stepping into the world of data science, this is the ideal way to get started.

Author : Marc Wintjen
Publisher : Packt Publishing Ltd
Page : 309 pages
File Size : 40,5 Mb
Release : 2020-06-19
Category : Computers
ISBN : 9781838825096

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Practical Data Analysis Using Jupyter Notebook by Marc Wintjen Pdf

Understand data analysis concepts to make accurate decisions based on data using Python programming and Jupyter Notebook Key FeaturesFind out how to use Python code to extract insights from data using real-world examplesWork with structured data and free text sources to answer questions and add value using dataPerform data analysis from scratch with the help of clear explanations for cleaning, transforming, and visualizing dataBook Description Data literacy is the ability to read, analyze, work with, and argue using data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines these two concepts by sharing proven techniques and hands-on examples so that you can learn how to communicate effectively using data. After introducing you to the basics of data analysis using Jupyter Notebook and Python, the book will take you through the fundamentals of data. Packed with practical examples, this guide will teach you how to clean, wrangle, analyze, and visualize data to gain useful insights, and you'll discover how to answer questions using data with easy-to-follow steps. Later chapters teach you about storytelling with data using charts, such as histograms and scatter plots. As you advance, you'll understand how to work with unstructured data using natural language processing (NLP) techniques to perform sentiment analysis. All the knowledge you gain will help you discover key patterns and trends in data using real-world examples. In addition to this, you will learn how to handle data of varying complexity to perform efficient data analysis using modern Python libraries. By the end of this book, you'll have gained the practical skills you need to analyze data with confidence. What you will learnUnderstand the importance of data literacy and how to communicate effectively using dataFind out how to use Python packages such as NumPy, pandas, Matplotlib, and the Natural Language Toolkit (NLTK) for data analysisWrangle data and create DataFrames using pandasProduce charts and data visualizations using time-series datasetsDiscover relationships and how to join data together using SQLUse NLP techniques to work with unstructured data to create sentiment analysis modelsDiscover patterns in real-world datasets that provide accurate insightsWho this book is for This book is for aspiring data analysts and data scientists looking for hands-on tutorials and real-world examples to understand data analysis concepts using SQL, Python, and Jupyter Notebook. Anyone looking to evolve their skills to become data-driven personally and professionally will also find this book useful. No prior knowledge of data analysis or programming is required to get started with this book.

Author : Jake VanderPlas
Publisher : "O'Reilly Media, Inc."
Page : 591 pages
File Size : 49,7 Mb
Release : 2022-12-06
Category : Computers
ISBN : 9781098121198

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Python Data Science Handbook by Jake VanderPlas Pdf

Python is a first-class tool for many researchers, primarily 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 new edition of 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 the second edition of 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: IPython and Jupyter provide computational environments for scientists using Python NumPy includes the ndarray for efficient storage and manipulation of dense data arrays Pandas contains the DataFrame for efficient storage and manipulation of labeled/columnar data Matplotlib includes capabilities for a flexible range of data visualizations Scikit-learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms

Author : Laura Igual,Santi Seguí
Publisher : Springer
Page : 218 pages
File Size : 41,8 Mb
Release : 2017-02-22
Category : Computers
ISBN : 9783319500171

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Introduction to Data Science by Laura Igual,Santi Seguí Pdf

This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website.