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Mastering Python for Data Science

Mastering Python for Data Science
Author: Samir Madhavan
Publisher: Packt Publishing Ltd
Total Pages: 294
Release: 2015-08-31
Genre: Computers
ISBN: 1784392626
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Explore the world of data science through Python and learn how to make sense of data About This Book Master data science methods using Python and its libraries Create data visualizations and mine for patterns Advanced techniques for the four fundamentals of Data Science with Python - data mining, data analysis, data visualization, and machine learning Who This Book Is For If you are a Python developer who wants to master the world of data science then this book is for you. Some knowledge of data science is assumed. What You Will Learn Manage data and perform linear algebra in Python Derive inferences from the analysis by performing inferential statistics Solve data science problems in Python Create high-end visualizations using Python Evaluate and apply the linear regression technique to estimate the relationships among variables. Build recommendation engines with the various collaborative filtering algorithms Apply the ensemble methods to improve your predictions Work with big data technologies to handle data at scale In Detail Data science is a relatively new knowledge domain which is used by various organizations to make data driven decisions. Data scientists have to wear various hats to work with data and to derive value from it. The Python programming language, beyond having conquered the scientific community in the last decade, is now an indispensable tool for the data science practitioner and a must-know tool for every aspiring data scientist. Using Python will offer you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving. This comprehensive guide helps you move beyond the hype and transcend the theory by providing you with a hands-on, advanced study of data science. Beginning with the essentials of Python in data science, you will learn to manage data and perform linear algebra in Python. You will move on to deriving inferences from the analysis by performing inferential statistics, and mining data to reveal hidden patterns and trends. You will use the matplot library to create high-end visualizations in Python and uncover the fundamentals of machine learning. Next, you will apply the linear regression technique and also learn to apply the logistic regression technique to your applications, before creating recommendation engines with various collaborative filtering algorithms and improving your predictions by applying the ensemble methods. Finally, you will perform K-means clustering, along with an analysis of unstructured data with different text mining techniques and leveraging the power of Python in big data analytics. Style and approach This book is an easy-to-follow, comprehensive guide on data science using Python. The topics covered in the book can all be used in real world scenarios.

Mastering Python Data Analysis

Mastering Python Data Analysis
Author: Magnus Vilhelm Persson
Publisher: Packt Publishing Ltd
Total Pages: 281
Release: 2016-06-27
Genre: Computers
ISBN: 1783553308
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Become an expert at using Python for advanced statistical analysis of data using real-world examples About This Book Clean, format, and explore data using graphical and numerical summaries Leverage the IPython environment to efficiently analyze data with Python Packed with easy-to-follow examples to develop advanced computational skills for the analysis of complex data Who This Book Is For If you are a competent Python developer who wants to take your data analysis skills to the next level by solving complex problems, then this advanced guide is for you. Familiarity with the basics of applying Python libraries to data sets is assumed. What You Will Learn Read, sort, and map various data into Python and Pandas Recognise patterns so you can understand and explore data Use statistical models to discover patterns in data Review classical statistical inference using Python, Pandas, and SciPy Detect similarities and differences in data with clustering Clean your data to make it useful Work in Jupyter Notebook to produce publication ready figures to be included in reports In Detail Python, a multi-paradigm programming language, has become the language of choice for data scientists for data analysis, visualization, and machine learning. Ever imagined how to become an expert at effectively approaching data analysis problems, solving them, and extracting all of the available information from your data? Well, look no further, this is the book you want! Through this comprehensive guide, you will explore data and present results and conclusions from statistical analysis in a meaningful way. You'll be able to quickly and accurately perform the hands-on sorting, reduction, and subsequent analysis, and fully appreciate how data analysis methods can support business decision-making. You'll start off by learning about the tools available for data analysis in Python and will then explore the statistical models that are used to identify patterns in data. Gradually, you'll move on to review statistical inference using Python, Pandas, and SciPy. After that, we'll focus on performing regression using computational tools and you'll get to understand the problem of identifying clusters in data in an algorithmic way. Finally, we delve into advanced techniques to quantify cause and effect using Bayesian methods and you'll discover how to use Python's tools for supervised machine learning. Style and approach This book takes a step-by-step approach to reading, processing, and analyzing data in Python using various methods and tools. Rich in examples, each topic connects to real-world examples and retrieves data directly online where possible. With this book, you are given the knowledge and tools to explore any data on your own, encouraging a curiosity befitting all data scientists.

Python Programming

Python Programming
Author: Andrew Park
Publisher:
Total Pages: 304
Release: 2020-08-22
Genre:
ISBN:
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If you want to learn Python in one week (or less) and learn it well, with useful applications to Data Analysis, Machine Learning and Data Science, then keep reading. Python is one of the most beloved programming languages in any circle of programmers. Software engineers, hackers, and Data Scientists alike are in love with the versatility that Python has to offer. Besides, the Object-Oriented feature of Python coupled with its flexibility is also one of the major attractions for this language. That's the reason why Python is a perfect fit with Data Analysis, Machine Learning and Data Science. Data is the future. The world of technology as we know it is evolving towards an open-source platform where people share ideas freely. This is seen as the first step towards the decentralization of ideas and eliminating unnecessary monopolies. Therefore, the data, tools, and techniques used in the analysis are easily available for anyone to interpret data sets and get relevant explanations. The goal of this 4-in-1 bundle is simple: explaining everything you need to know to Master Python. With a special emphasis on the main steps that are needed to correctly implement Data Analysis and Machine Learning algorithms, In manuscript one, Python for Beginners, you will learn: How to install Python What are the different Python Data Types and Variables Basic Operators of Python Language Data Structures and Functions Conditional and Loops in Python And Much More! In manuscript two, Python Advanced Guide, you will master: Object-Oriented Programming (OOP), Inheritance and Polymorphism Essential Programming Tools Exception Handling Working with Files And Much More! In manuscript three, Python for Data Analysis, you will learn: What Data Analysis is all about and why businesses are investing in this sector The 5 steps of a Data Analysis The 7 Python libraries that make Python one of the best choices for Data Analysis Pandas, Jupyter and PyTorch And Much More! In manuscript four, Applications to Data Science, you will understand: How Data Visualization and Matplotlib can help you to understand the data you are working with. Neural Networks Decision Trees What industries are using data to improve their business with 14 real-world applications And So Much More! Where most books about Python programming are theoretical and have few or little practical examples, this book provides lots of simple, step-by-step examples and illustrations that are used to underline key concepts and help improve your understanding. Furthermore, topics are carefully selected to give you broad exposure to Python, while not overwhelming you with too much information. Also, the outputs of ALL the examples are provided immediately so you do not have to wait till you have access to your computer to test the examples. Even if you have never coded before, this is the perfect guide because it breaks down complex concepts into simple steps and in a concise and simple way that fits well with beginners. Regardless of your previous experience, you will learn the steps of Data Analysis, how to implement them, and the most important real-world applications. Would you like to know more?Scroll Up and Click the BUY NOW Button to Get Your Copy!

Mastering Python

Mastering Python
Author: Rick van Hattem
Publisher: Packt Publishing Ltd
Total Pages: 486
Release: 2016-04-29
Genre: Computers
ISBN: 1785289136
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Master the art of writing beautiful and powerful Python by using all of the features that Python 3.5 offers About This Book Become familiar with the most important and advanced parts of the Python code style Learn the trickier aspects of Python and put it in a structured context for deeper understanding of the language Offers an expert's-eye overview of how these advanced tasks fit together in Python as a whole along with practical examples Who This Book Is For Almost anyone can learn to write working script and create high quality code but they might lack a structured understanding of what it means to be 'Pythonic'. If you are a Python programmer who wants to code efficiently by getting the syntax and usage of a few intricate Python techniques exactly right, this book is for you. What You Will Learn Create a virtualenv and start a new project Understand how and when to use the functional programming paradigm Get familiar with the different ways the decorators can be written in Understand the power of generators and coroutines without digressing into lambda calculus Create metaclasses and how it makes working with Python far easier Generate HTML documentation out of documents and code using Sphinx Learn how to track and optimize application performance, both memory and cpu Use the multiprocessing library, not just locally but also across multiple machines Get a basic understanding of packaging and creating your own libraries/applications In Detail Python is a dynamic programming language. It is known for its high readability and hence it is often the first language learned by new programmers. Python being multi-paradigm, it can be used to achieve the same thing in different ways and it is compatible across different platforms. Even if you find writing Python code easy, writing code that is efficient, easy to maintain, and reuse is not so straightforward. This book is an authoritative guide that will help you learn new advanced methods in a clear and contextualised way. It starts off by creating a project-specific environment using venv, introducing you to different Pythonic syntax and common pitfalls before moving on to cover the functional features in Python. It covers how to create different decorators, generators, and metaclasses. It also introduces you to functools.wraps and coroutines and how they work. Later on you will learn to use asyncio module for asynchronous clients and servers. You will also get familiar with different testing systems such as py.test, doctest, and unittest, and debugging tools such as Python debugger and faulthandler. You will learn to optimize application performance so that it works efficiently across multiple machines and Python versions. Finally, it will teach you how to access C functions with a simple Python call. By the end of the book, you will be able to write more advanced scripts and take on bigger challenges. Style and Approach This book is a comprehensive guide that covers advanced features of the Python language, and communicate them with an authoritative understanding of the underlying rationale for how, when, and why to use them.

Mastering Python Scientific Computing

Mastering Python Scientific Computing
Author: Hemant Kumar Mehta
Publisher: Packt Publishing Ltd
Total Pages: 301
Release: 2015-09-23
Genre: Computers
ISBN: 1783288833
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A complete guide for Python programmers to master scientific computing using Python APIs and tools About This Book The basics of scientific computing to advanced concepts involving parallel and large scale computation are all covered. Most of the Python APIs and tools used in scientific computing are discussed in detail The concepts are discussed with suitable example programs Who This Book Is For If you are a Python programmer and want to get your hands on scientific computing, this book is for you. The book expects you to have had exposure to various concepts of Python programming. What You Will Learn Fundamentals and components of scientific computing Scientific computing data management Performing numerical computing using NumPy and SciPy Concepts and programming for symbolic computing using SymPy Using the plotting library matplotlib for data visualization Data analysis and visualization using Pandas, matplotlib, and IPython Performing parallel and high performance computing Real-life case studies and best practices of scientific computing In Detail In today's world, along with theoretical and experimental work, scientific computing has become an important part of scientific disciplines. Numerical calculations, simulations and computer modeling in this day and age form the vast majority of both experimental and theoretical papers. In the scientific method, replication and reproducibility are two important contributing factors. A complete and concrete scientific result should be reproducible and replicable. Python is suitable for scientific computing. A large community of users, plenty of help and documentation, a large collection of scientific libraries and environments, great performance, and good support makes Python a great choice for scientific computing. At present Python is among the top choices for developing scientific workflow and the book targets existing Python developers to master this domain using Python. The main things to learn in the book are the concept of scientific workflow, managing scientific workflow data and performing computation on this data using Python. The book discusses NumPy, SciPy, SymPy, matplotlib, Pandas and IPython with several example programs. Style and approach This book follows a hands-on approach to explain the complex concepts related to scientific computing. It details various APIs using appropriate examples.

Mastering Python for Web

Mastering Python for Web
Author: Sufyan Bin Uzayr
Publisher: CRC Press
Total Pages: 288
Release: 2022
Genre: Computers
ISBN: 9781003229896
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Python for Web Python definitely tops the charts when it comes to ease of use and beginner-friendly learning curve in the world of programming languages. At the same time, Python is essential when it comes to writing system scripts, processing big data, performing mathematical computations, creating web applications, and rapid prototyping. With this Mastering edition, we have focused especially on the usage of Python for Web. This book explores Python programming fundamentals with interactive projects and introduces core coding concepts and the basics of Python-based web development. The reader should be ready to dive deep into the world of Python for web development in no time. Since Python positions itself in web development as a back-end language, it is usually mixed with another front-end language to build a whole website. At the same time, reasons for using Python in web development are many: it is a flexible, versatile, and highly efficient programming language with dynamic typing capacity. This book helps readers to examine Python's key back-end/front-end programming techniques and guides them through implementing them when creating professional projects. Furthermore, it also focuses on teaching readers how to solve common problems and developing web services with Python frameworks such as Django and Flask. Mastering Python for Web has a goal more ambitious than simply teaching you the ropes - it aims to help you embrace and master problem solving, which could be viewed as the single most crucial skill for a coder. It offers you a focal point on starting as a beginner and growing into an expert by putting your newly acquired knowledge into practice. Programming is a hands-on skill, and this particular book helps you put your skills to test with easy-to-grasp tasks and examples. Learn more about our other Mastering titles at: https://www.routledge.com/Mastering-Computer-Science/book-series/MCS

Python Data Science

Python Data Science
Author: Computer Programming Academy
Publisher:
Total Pages: 202
Release: 2020-11-10
Genre:
ISBN: 9781914185106
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Inside this book you will find all the basic notions to start with Python and all the programming concepts to implement predictive analytics. With our proven strategies you will write efficient Python codes in less than a week!

Mastering Python for Data Science

Mastering Python for Data Science
Author: Samir Medhaven
Publisher: Packt Publishing
Total Pages: 294
Release: 2015-08-31
Genre: Computers
ISBN: 9781784390150
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Explore the world of data science through Python and learn how to make sense of dataAbout This Book• Master data science methods using Python and its libraries• Create data visualizations and mine for patterns• Advanced techniques for the four fundamentals of Data Science with Python - data mining, data analysis, data visualization, and machine learningWho This Book Is ForIf you are a Python developer who wants to master the world of data science then this book is for you. Some knowledge of data science is assumed.What You Will Learn• Manage data and perform linear algebra in Python• Derive inferences from the analysis by performing inferential statistics• Solve data science problems in Python• Create high-end visualizations using Python• Evaluate and apply the linear regression technique to estimate the relationships among variables.• Build recommendation engines with the various collaborative filtering algorithms• Apply the ensemble methods to improve your predictions• Work with big data technologies to handle data at scaleIn DetailData science is a relatively new knowledge domain which is used by various organizations to make data driven decisions. Data scientists have to wear various hats to work with data and to derive value from it. The Python programming language, beyond having conquered the scientific community in the last decade, is now an indispensable tool for the data science practitioner and a must-know tool for every aspiring data scientist. Using Python will offer you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving.This comprehensive guide helps you move beyond the hype and transcend the theory by providing you with a hands-on, advanced study of data science.Beginning with the essentials of Python in data science, you will learn to manage data and perform linear algebra in Python. You will move on to deriving inferences from the analysis by performing inferential statistics, and mining data to reveal hidden patterns and trends. You will use the matplot library to create high-end visualizations in Python and uncover the fundamentals of machine learning. Next, you will apply the linear regression technique and also learn to apply the logistic regression technique to your applications, before creating recommendation engines with various collaborative filtering algorithms and improving your predictions by applying the ensemble methods.Finally, you will perform K-means clustering, along with an analysis of unstructured data with different text mining techniques and leveraging the power of Python in big data analytics.Style and approachThis book is an easy-to-follow, comprehensive guide on data science using Python. The topics covered in the book can all be used in real world scenarios.

Python Data Science Essentials

Python Data Science Essentials
Author: Alberto Boschetti
Publisher: Packt Publishing Ltd
Total Pages: 373
Release: 2016-10-28
Genre: Computers
ISBN: 1786462834
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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.

Mastering Python for Bioinformatics

Mastering Python for Bioinformatics
Author: Ken Youens-Clark
Publisher: "O'Reilly Media, Inc."
Total Pages: 457
Release: 2021-05-05
Genre: Computers
ISBN: 1098100859
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Life scientists today urgently need training in bioinformatics skills. Too many bioinformatics programs are poorly written and barely maintained--usually by students and researchers who've never learned basic programming skills. This practical guide shows postdoc bioinformatics professionals and students how to exploit the best parts of Python to solve problems in biology while creating documented, tested, reproducible software. Ken Youens-Clark, author of Tiny Python Projects (Manning), demonstrates not only how to write effective Python code but also how to use tests to write and refactor scientific programs. You'll learn the latest Python features and toolsâ??including linters, formatters, type checkers, and testsâ??to create documented and tested programs. You'll also tackle 14 challenges in Rosalind, a problem-solving platform for learning bioinformatics and programming. Create command-line Python programs to document and validate parameters Write tests to verify refactor programs and confirm they're correct Address bioinformatics ideas using Python data structures and modules such as Biopython Create reproducible shortcuts and workflows using makefiles Parse essential bioinformatics file formats such as FASTA and FASTQ Find patterns of text using regular expressions Use higher-order functions in Python like filter(), map(), and reduce()