Python for Data Analysis 2nd Edition

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.

Author: William McKinney Wesley

Publisher:

ISBN: 9781491957653

Category:

Page:

View: 547


Python Data Analysis

Learn how to apply powerful data analysis techniques with popular open source Python modules About This Book Find, manipulate, and analyze your data using the Python 3.5 libraries Perform advanced, high-performance linear algebra and ...

Author: Armando Fandango

Publisher: Packt Publishing Ltd

ISBN: 1787127923

Category: Computers

Page: 330

View: 808

Learn how to apply powerful data analysis techniques with popular open source Python modules About This Book Find, manipulate, and analyze your data using the Python 3.5 libraries Perform advanced, high-performance linear algebra and mathematical calculations with clean and efficient Python code An easy-to-follow guide with realistic examples that are frequently used in real-world data analysis projects. Who This Book Is For This book is for programmers, scientists, and engineers who have the knowledge of Python and know the basics of data science. It is for those who wish to learn different data analysis methods using Python 3.5 and its libraries. This book contains all the basic ingredients you need to become an expert data analyst. What You Will Learn Install open source Python modules such NumPy, SciPy, Pandas, stasmodels, scikit-learn,theano, keras, and tensorflow on various platforms Prepare and clean your data, and use it for exploratory analysis Manipulate your data with Pandas Retrieve and store your data from RDBMS, NoSQL, and distributed filesystems such as HDFS and HDF5 Visualize your data with open source libraries such as matplotlib, bokeh, and plotly Learn about various machine learning methods such as supervised, unsupervised, probabilistic, and Bayesian Understand signal processing and time series data analysis Get to grips with graph processing and social network analysis In Detail Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks. With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis. The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries. Style and approach The book takes a very comprehensive approach to enhance your understanding of data analysis. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work. Packed with clear, easy to follow examples, this book will turn you into an ace data analyst in no time.

Python for Data Analysis

Presents case studies and instructions on how to solve data analysis problems using Python.

Author: Wes McKinney

Publisher: "O'Reilly Media, Inc."

ISBN: 1449319793

Category: Computers

Page: 452

View: 818

Presents case studies and instructions on how to solve data analysis problems using Python.

Python for Data Analysis

Even if this is the first time you're hearing about Data Analysis and Python, you can still successfully learn everything this book offers.

Author: Matt Foster

Publisher:

ISBN: 9781654136611

Category:

Page: 132

View: 989

Master the Python Programming Language and Data Analysis With This Comprehensive Guide! If you would like to... Grow your business Get an amazing job Make great business decisions Get rid of the competition... This book will teach you how to achieve all that with the help of data analysis and data science. It might sound like a lot of work, but with proper guidance, you don't need to spend hours bent over textbooks and trying to make sense of a huge amount of information. The goal of this book is not only to learn about data analysis but to go from this theoretical to practical knowledge and application. In other words, you'll be able to complete your own analysis, implement its methods in your business, and master the Python Programming Language! Here's what you'll learn with this book: The importance of data analysis and why every successful business and industry are using it How to process data with tools and techniques used by data scientists The concepts behind Python programming How to use the "data munging" process How to use Python libraries such as Pandas and NumPy for data analysis The importance of data visualization How to create the right analytical algorithm for predicting the market trends How to write codes, and create programs and databases And much more! Even if this is the first time you're hearing about Data Analysis and Python, you can still successfully learn everything this book offers. The instructions are incredibly simple, the methods explained to the finest details and the guides are presented in a step-by-step way. You don't have to be a computer or math expert to develop this skill. You simply need a straightforward guide on the steps you have to take, with clear background explanations to help you understand those steps. If you want to modernize your company and your skills, make the most of your data and become a competitive force on the market, Scroll up, click on "Buy Now with 1-Click", and Get Your Copy Now!

Python for Data Science For Dummies

Data science doesn't have to be scary Curious about data science, but a bit intimidated? Don't be! This book shows you how to use Python to do all sorts of cool things with data science.

Author: John Paul Mueller

Publisher: John Wiley & Sons

ISBN: 1119547628

Category: Computers

Page: 496

View: 951

The fast and easy way to learn Python programming and statistics Python is a general-purpose programming language created in the late 1980s—and named after Monty Python—that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library. Python For Data Science For Dummies is written for people who are new to data analysis, and discusses the basics of Python data analysis programming and statistics. The book also discusses Google Colab, which makes it possible to write Python code in the cloud. Get started with data science and Python Visualize information Wrangle data Learn from data The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction.

Practical Data Analysis

A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache Spark About This Book Learn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and ...

Author: Hector Cuesta

Publisher: Packt Publishing Ltd

ISBN: 1785286668

Category: Computers

Page: 338

View: 368

A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache Spark About This Book Learn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and forecast your data Apply Machine Learning algorithms to different kinds of data such as social networks, time series, and images A hands-on guide to understanding the nature of data and how to turn it into insight Who This Book Is For This book is for developers who want to implement data analysis and data-driven algorithms in a practical way. It is also suitable for those without a background in data analysis or data processing. Basic knowledge of Python programming, statistics, and linear algebra is assumed. What You Will Learn Acquire, format, and visualize your data Build an image-similarity search engine Generate meaningful visualizations anyone can understand Get started with analyzing social network graphs Find out how to implement sentiment text analysis Install data analysis tools such as Pandas, MongoDB, and Apache Spark Get to grips with Apache Spark Implement machine learning algorithms such as classification or forecasting In Detail Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service. This book explains the basic data algorithms without the theoretical jargon, and you'll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark. Style and approach This is a hands-on guide to data analysis and data processing. The concrete examples are explained with simple code and accessible data.

Mastering pandas

This book presents useful data manipulation techniques in pandas to perform complex data analysis in various domains.

Author: Ashish Kumar

Publisher: Packt Publishing Ltd

ISBN: 1789343356

Category: Computers

Page: 674

View: 446

Perform advanced data manipulation tasks using pandas and become an expert data analyst. Key Features Manipulate and analyze your data expertly using the power of pandas Work with missing data and time series data and become a true pandas expert Includes expert tips and techniques on making your data analysis tasks easier Book Description pandas is a popular Python library used by data scientists and analysts worldwide to manipulate and analyze their data. This book presents useful data manipulation techniques in pandas to perform complex data analysis in various domains. An update to our highly successful previous edition with new features, examples, updated code, and more, this book is an in-depth guide to get the most out of pandas for data analysis. Designed for both intermediate users as well as seasoned practitioners, you will learn advanced data manipulation techniques, such as multi-indexing, modifying data structures, and sampling your data, which allow for powerful analysis and help you gain accurate insights from it. With the help of this book, you will apply pandas to different domains, such as Bayesian statistics, predictive analytics, and time series analysis using an example-based approach. And not just that; you will also learn how to prepare powerful, interactive business reports in pandas using the Jupyter notebook. By the end of this book, you will learn how to perform efficient data analysis using pandas on complex data, and become an expert data analyst or data scientist in the process. What you will learn Speed up your data analysis by importing data into pandas Keep relevant data points by selecting subsets of your data Create a high-quality dataset by cleaning data and fixing missing values Compute actionable analytics with grouping and aggregation in pandas Master time series data analysis in pandas Make powerful reports in pandas using Jupyter notebooks Who this book is for This book is for data scientists, analysts and Python developers who wish to explore advanced data analysis and scientific computing techniques using pandas. Some fundamental understanding of Python programming and familiarity with the basic data analysis concepts is all you need to get started with this book.

Interactive Data Visualization with Python

What you will learn Explore and apply different interactive data visualization techniques Manipulate plotting parameters and styles to create appealing plots Customize data visualization for different audiences Design data visualizations ...

Author: Abha Belorkar

Publisher: Packt Publishing Ltd

ISBN: 1800201060

Category: Computers

Page: 362

View: 626

Create your own clear and impactful interactive data visualizations with the powerful data visualization libraries of Python Key Features Study and use Python interactive libraries, such as Bokeh and Plotly Explore different visualization principles and understand when to use which one Create interactive data visualizations with real-world data Book Description With so much data being continuously generated, developers, who can present data as impactful and interesting visualizations, are always in demand. Interactive Data Visualization with Python sharpens your data exploration skills, tells you everything there is to know about interactive data visualization in Python. You'll begin by learning how to draw various plots with Matplotlib and Seaborn, the non-interactive data visualization libraries. You'll study different types of visualizations, compare them, and find out how to select a particular type of visualization to suit your requirements. After you get a hang of the various non-interactive visualization libraries, you'll learn the principles of intuitive and persuasive data visualization, and use Bokeh and Plotly to transform your visuals into strong stories. You'll also gain insight into how interactive data and model visualization can optimize the performance of a regression model. By the end of the course, you'll have a new skill set that'll make you the go-to person for transforming data visualizations into engaging and interesting stories. What you will learn Explore and apply different interactive data visualization techniques Manipulate plotting parameters and styles to create appealing plots Customize data visualization for different audiences Design data visualizations using interactive libraries Use Matplotlib, Seaborn, Altair and Bokeh for drawing appealing plots Customize data visualization for different scenarios Who this book is for This book intends to provide a solid training ground for Python developers, data analysts and data scientists to enable them to present critical data insights in a way that best captures the user's attention and imagination. It serves as a simple step-by-step guide that demonstrates the different types and components of visualization, the principles, and techniques of effective interactivity, as well as common pitfalls to avoid when creating interactive data visualizations. Students should have an intermediate level of competency in writing Python code, as well as some familiarity with using libraries such as pandas.

Python Data Analysis

Perform data collection, data processing, wrangling, visualization, and model
building using Python Avinash Navlani, Armando ... Armando has authored
books such as Python Data Analysis - Second Edition and Mastering TensorFlow
, Packt ...

Author: Avinash Navlani

Publisher: Packt Publishing Ltd

ISBN: 1789953456

Category: Computers

Page: 478

View: 854

Understand data analysis pipelines using machine learning algorithms and techniques with this practical guide Key Features Prepare and clean your data to use it for exploratory analysis, data manipulation, and data wrangling Discover supervised, unsupervised, probabilistic, and Bayesian machine learning methods Get to grips with graph processing and sentiment analysis Book Description Data analysis enables you to generate value from small and big data by discovering new patterns and trends, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you'll get up and running using Python for data analysis by exploring the different phases and methodologies used in data analysis and learning how to use modern libraries from the Python ecosystem to create efficient data pipelines. Starting with the essential statistical and data analysis fundamentals using Python, you'll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. You'll then understand how to conduct time series analysis and signal processing using ARMA models. As you advance, you'll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. In the concluding chapters, you'll work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask. By the end of this data analysis book, you'll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data. What you will learn Explore data science and its various process models Perform data manipulation using NumPy and pandas for aggregating, cleaning, and handling missing values Create interactive visualizations using Matplotlib, Seaborn, and Bokeh Retrieve, process, and store data in a wide range of formats Understand data preprocessing and feature engineering using pandas and scikit-learn Perform time series analysis and signal processing using sunspot cycle data Analyze textual data and image data to perform advanced analysis Get up to speed with parallel computing using Dask Who this book is for This book is for data analysts, business analysts, statisticians, and data scientists looking to learn how to use Python for data analysis. Students and academic faculties will also find this book useful for learning and teaching Python data analysis using a hands-on approach. A basic understanding of math and working knowledge of the Python programming language will help you get started with this book.

Predictive Analytics with Microsoft Azure Machine Learning 2nd Edition

Using simple examples, you will learn how to integrate Python as part of an
Azure ML experiment. This enables you to tap into the powerful capabilities
offered by various Python libraries, such as NumPy, SciPy, pandas, scikit-learn,
and many ...

Author: Valentine Fontama

Publisher: Apress

ISBN: 1484212002

Category: Computers

Page: 291

View: 947

Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book provides a thorough overview of the Microsoft Azure Machine Learning service released for general availability on February 18th, 2015 with practical guidance for building recommenders, propensity models, and churn and predictive maintenance models. The authors use task oriented descriptions and concrete end-to-end examples to ensure that the reader can immediately begin using this new service. The book describes all aspects of the service from data ingress to applying machine learning, evaluating the models, and deploying them as web services. Learn how you can quickly build and deploy sophisticated predictive models with the new Azure Machine Learning from Microsoft. What’s New in the Second Edition? Five new chapters have been added with practical detailed coverage of: Python Integration – a new feature announced February 2015 Data preparation and feature selection Data visualization with Power BI Recommendation engines Selling your models on Azure Marketplace

Getting Started with Haskell Data Analysis

If you enjoyed this book, you may be interested in these other books by Packt:
Python Data Analysis - Second Edition Armando Fandango ISBN:
9781787127487 Install open source Python modules such NumPy, SciPy,
Pandas, stasmodels, ...

Author: James Church

Publisher: Packt Publishing Ltd

ISBN: 178980860X

Category: Computers

Page: 160

View: 353

Put your Haskell skills to work and generate publication-ready visualizations in no time at all Key Features Take your data analysis skills to the next level using the power of Haskell Understand regression analysis, perform multivariate regression, and untangle different cluster varieties Create publication-ready visualizations of data Book Description Every business and organization that collects data is capable of tapping into its own data to gain insights how to improve. Haskell is a purely functional and lazy programming language, well-suited to handling large data analysis problems. This book will take you through the more difficult problems of data analysis in a hands-on manner. This book will help you get up-to-speed with the basics of data analysis and approaches in the Haskell language. You'll learn about statistical computing, file formats (CSV and SQLite3), descriptive statistics, charts, and progress to more advanced concepts such as understanding the importance of normal distribution. While mathematics is a big part of data analysis, we've tried to keep this course simple and approachable so that you can apply what you learn to the real world. By the end of this book, you will have a thorough understanding of data analysis, and the different ways of analyzing data. You will have a mastery of all the tools and techniques in Haskell for effective data analysis. What you will learn Learn to parse a CSV file and read data into the Haskell environment Create Haskell functions for common descriptive statistics functions Create an SQLite3 database using an existing CSV file Learn the versatility of SELECT queries for slicing data into smaller chunks Apply regular expressions in large-scale datasets using both CSV and SQLite3 files Create a Kernel Density Estimator visualization using normal distribution Who this book is for This book is intended for people who wish to expand their knowledge of statistics and data analysis via real-world examples. A basic understanding of the Haskell language is expected. If you are feeling brave, you can jump right into the functional programming style.

Bayesian Analysis with Python

What you will learn Build probabilistic models using the Python library PyMC3 Analyze probabilistic models with the help of ArviZ Acquire the skills required to sanity check models and modify them if necessary Understand the advantages and ...

Author: Osvaldo Martin

Publisher: Packt Publishing Ltd

ISBN: 1789349664

Category: Computers

Page: 356

View: 874

Bayesian modeling with PyMC3 and exploratory analysis of Bayesian models with ArviZ Key Features A step-by-step guide to conduct Bayesian data analyses using PyMC3 and ArviZ A modern, practical and computational approach to Bayesian statistical modeling A tutorial for Bayesian analysis and best practices with the help of sample problems and practice exercises. Book Description The second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models. The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others. By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to. What you will learn Build probabilistic models using the Python library PyMC3 Analyze probabilistic models with the help of ArviZ Acquire the skills required to sanity check models and modify them if necessary Understand the advantages and caveats of hierarchical models Find out how different models can be used to answer different data analysis questions Compare models and choose between alternative ones Discover how different models are unified from a probabilistic perspective Think probabilistically and benefit from the flexibility of the Bayesian framework Who this book is for If you are a student, data scientist, researcher, or a developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory so no previous statistical knowledge is required, although some experience in using Python and NumPy is expected.

IPython Interactive Computing and Visualization Cookbook

Over 100 hands-on recipes to sharpen your skills in high-performance numerical
computing and data science in the Jupyter Notebook, 2nd Edition Cyrille Rossant
. There's more... pandas is the main data wrangling library in Python.

Author: Cyrille Rossant

Publisher: Packt Publishing Ltd

ISBN: 1785881930

Category: Computers

Page: 548

View: 566

Learn to use IPython and Jupyter Notebook for your data analysis and visualization work. Key Features Leverage the Jupyter Notebook for interactive data science and visualization Become an expert in high-performance computing and visualization for data analysis and scientific modeling A comprehensive coverage of scientific computing through many hands-on, example-driven recipes with detailed, step-by-step explanations Book Description Python is one of the leading open source platforms for data science and numerical computing. IPython and the associated Jupyter Notebook offer efficient interfaces to Python for data analysis and interactive visualization, and they constitute an ideal gateway to the platform. IPython Interactive Computing and Visualization Cookbook, Second Edition contains many ready-to-use, focused recipes for high-performance scientific computing and data analysis, from the latest IPython/Jupyter features to the most advanced tricks, to help you write better and faster code. You will apply these state-of-the-art methods to various real-world examples, illustrating topics in applied mathematics, scientific modeling, and machine learning. The first part of the book covers programming techniques: code quality and reproducibility, code optimization, high-performance computing through just-in-time compilation, parallel computing, and graphics card programming. The second part tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics. What you will learn Master all features of the Jupyter Notebook Code better: write high-quality, readable, and well-tested programs; profile and optimize your code; and conduct reproducible interactive computing experiments Visualize data and create interactive plots in the Jupyter Notebook Write blazingly fast Python programs with NumPy, ctypes, Numba, Cython, OpenMP, GPU programming (CUDA), parallel IPython, Dask, and more Analyze data with Bayesian or frequentist statistics (Pandas, PyMC, and R), and learn from actual data through machine learning (scikit-learn) Gain valuable insights into signals, images, and sounds with SciPy, scikit-image, and OpenCV Simulate deterministic and stochastic dynamical systems in Python Familiarize yourself with math in Python using SymPy and Sage: algebra, analysis, logic, graphs, geometry, and probability theory Who this book is for This book is intended for anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, and hobbyists. A basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods.

Pandas 1 x Cookbook

Key Features This is the first book on pandas 1.x Practical, easy to implement recipes for quick solutions to common problems in data using pandas Master the fundamentals of pandas to quickly begin exploring any dataset Book Description The ...

Author: Matt Harrison

Publisher: Packt Publishing Ltd

ISBN: 1839218916

Category: Computers

Page: 626

View: 731

Use the power of pandas to solve most complex scientific computing problems with ease. Revised for pandas 1.x. Key Features This is the first book on pandas 1.x Practical, easy to implement recipes for quick solutions to common problems in data using pandas Master the fundamentals of pandas to quickly begin exploring any dataset Book Description The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through situations that you are highly likely to encounter. This new updated and revised edition provides you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. Many advanced recipes combine several different features across the pandas library to generate results. What you will learn Master data exploration in pandas through dozens of practice problems Group, aggregate, transform, reshape, and filter data Merge data from different sources through pandas SQL-like operations Create visualizations via pandas hooks to matplotlib and seaborn Use pandas, time series functionality to perform powerful analyses Import, clean, and prepare real-world datasets for machine learning Create workflows for processing big data that doesn’t fit in memory Who this book is for This book is for Python developers, data scientists, engineers, and analysts. Pandas is the ideal tool for manipulating structured data with Python and this book provides ample instruction and examples. Not only does it cover the basics required to be proficient, but it goes into the details of idiomatic pandas.

Let Us Python Second Edition

Instead, they are looking for something that can get them off the ground quickly. They are looking for similarities and differences in a feature that they have used in other language(s). This book should help them immediately.

Author: Yashavant Kanetkar

Publisher: BPB Publications

ISBN: 9389845009

Category: Computers

Page: 359

View: 250

Learn Python Quickly, A Programmer-Friendly Guide DESCRIPTION Most Programmer’s learning Python are usually comfortable with some or the other programming language and are not interested in going through the typical learning curve of learning the first programming language. Instead, they are looking for something that can get them off the ground quickly. They are looking for similarities and differences in a feature that they have used in other language(s). This book should help them immediately. It guides you from the fundamentals of using module through the use of advanced object orientation. KEY FEATURES Strengthens the foundations, as detailed explanation of programming language concepts are given in simple manner. Lists down all the important points that you need to know related to various topics in an organized manner. Prepares you for coding related interview and theoretical questions. Provides In depth explanation of complex topics and Questions. Focuses on how to think logically to solve a problem. Follows a systematic approach that will help you to prepare for an interview in short duration of time. Exercises are exceptionally useful to complete the reader’s understanding of a topic. WHAT WILL YOU LEARN Data types, Control flow instructions, console & File Input/Output Strings, list & tuples, List comprehension Sets & Dictionaries, Functions & Lambdas Dictionary Comprehension Modules, classes and objects, Inheritance Operator overloading, Exception handling Iterators & Generators, Decorators, Command-line Parsing WHO THIS BOOK IS FOR Students, Programmers, researchers, and software developers who wish to learn the basics of Python programming language. Table of Contents 1. Introduction to Python 2. Python Basics 3. Strings 4. Decision Control Instruction 5. Repetition Control Instruction 6. Console Input/Output 7. Lists 8. Tuples 9. Sets 10. Dictionaries 11. Comprehensions 12. Functions 13. Recursion 14. Functional Programming 15. Modules and Packages 16. Namespaces 17. Classes and Objects 18. Intricacies of Classes and Objects 19. Containership and Inheritance 20. Iterators and Generators 21. Exception Handling 22. File Input/Output 23. Miscellany 24. Multi-threading 25. Synchronization

Think Stats 2nd Edition

If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics.

Author: Allen Downey

Publisher:

ISBN:

Category:

Page: 226

View: 536

If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. You'll learn the entire process of exploratory data analysis-from collecting data and generating statistics to identifying patterns and testing hypotheses.

Learning Spark

This book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run.

Author: Holden Karau

Publisher: "O'Reilly Media, Inc."

ISBN: 144935906X

Category: Computers

Page: 276

View: 808

This book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. You'll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning.--

Bioinformatics with Python Cookbook

This book covers next-generation sequencing, genomics, metagenomics, population genetics, phylogenetics, and proteomics. You'll learn modern programming techniques to analyze large amounts of biological data.

Author: Tiago Antao

Publisher: Packt Publishing Ltd

ISBN: 1789349982

Category: Computers

Page: 360

View: 178

Discover modern, next-generation sequencing libraries from Python ecosystem to analyze large amounts of biological data Key Features Perform complex bioinformatics analysis using the most important Python libraries and applications Implement next-generation sequencing, metagenomics, automating analysis, population genetics, and more Explore various statistical and machine learning techniques for bioinformatics data analysis Book Description Bioinformatics is an active research field that uses a range of simple-to-advanced computations to extract valuable information from biological data. This book covers next-generation sequencing, genomics, metagenomics, population genetics, phylogenetics, and proteomics. You'll learn modern programming techniques to analyze large amounts of biological data. With the help of real-world examples, you'll convert, analyze, and visualize datasets using various Python tools and libraries. This book will help you get a better understanding of working with a Galaxy server, which is the most widely used bioinformatics web-based pipeline system. This updated edition also includes advanced next-generation sequencing filtering techniques. You'll also explore topics such as SNP discovery using statistical approaches under high-performance computing frameworks such as Dask and Spark. By the end of this book, you'll be able to use and implement modern programming techniques and frameworks to deal with the ever-increasing deluge of bioinformatics data. What you will learn Learn how to process large next-generation sequencing (NGS) datasets Work with genomic dataset using the FASTQ, BAM, and VCF formats Learn to perform sequence comparison and phylogenetic reconstruction Perform complex analysis with protemics data Use Python to interact with Galaxy servers Use High-performance computing techniques with Dask and Spark Visualize protein dataset interactions using Cytoscape Use PCA and Decision Trees, two machine learning techniques, with biological datasets Who this book is for This book is for Data data Scientistsscientists, Bioinformatics bioinformatics analysts, researchers, and Python developers who want to address intermediate-to-advanced biological and bioinformatics problems using a recipe-based approach. Working knowledge of the Python programming language is expected.

Practical Statistics for Data Scientists 2nd Edition

With this updated edition, you'll dive into: Exploratory data analysis Data and sampling distributions Statistical experiments and significance testing Regression and prediction Classification Statistical machine learning Unsupervised ...

Author: Peter Bruce

Publisher:

ISBN:

Category:

Page: 93

View: 625

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this practical guide-now including examples in Python as well as R-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 scientists use statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages, and have had some exposure to statistics but want to learn more, this quick reference bridges the gap in an accessible, readable format. With this updated edition, you'll dive into: Exploratory data analysis Data and sampling distributions Statistical experiments and significance testing Regression and prediction Classification Statistical machine learning Unsupervised learning.

The The Applied Data Science Workshop

The Applied Data Science Workshop explores the key elements and interesting applications of data science techniques with the help of practical examples and interactive exercises.

Author: Alex Galea

Publisher: Packt Publishing Ltd

ISBN: 180020700X

Category: Computers

Page: 352

View: 565

The Applied Data Science Workshop explores the key elements and interesting applications of data science techniques with the help of practical examples and interactive exercises. Following a hands-on approach, it allows you the freedom of analyzing data in the Jupyter Notebook effectively using many diverse open-source Python libraries.