Pricemate

Data Cleaning for Effective Data Science - David Mertz - Bog - Pearson Education (US) - Plusbog.dk

Most machine learning guides cover data cleaning briefly or skip it entirely. However, many data scientists and analysts spend most of their time on data cleaning and data quality tasks, and their effectiveness can make or break project success. In Data Cleaning for Effective Data Science , leading data science trainer David Mertz provides the most systematic guide to cleaning data for any project, using any library or toolset. Mertz introduces many powerful techniques for analyzing, manipulating, and pre-processing data sources. He offers best practices for working with leading data formats such as JSON, CSV, SQL RDBMSes, HDF5, NoSQL databases, files in image formats, binary serialized data structures, and more. Mertz also focuses on crucial issues within the data itself, including missing data, outliers, biasing trends, class imbalance, value imputation, over/under-sampling, normalization and/or randomization, and anomalies. This guide is organized around downloadable datasets, each illuminating specific issues with data integrity or quality. Each chapter explores the best ways to diagnose, analyze, and remediate these issues, offering hands-on practice using tools such as Python, Pandas, sklearn.preprocessing, scipy.stats, R, and Tidyverse. While the examples are demonstrated with widely-used tools, Mertz''s concepts are applicable with any toolset. Each chapter also links to additional datasets with more problems, exercises, and solutions.

DKK 388.00

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Clean Code - Robert C. Martin - Bog - Pearson Education (US) - Plusbog.dk

Even bad code can function. But if code isn’t clean, it can bring a development organization to its knees. Every year, countless hours and significant resources are lost because of poorly written code. But it doesn’t have to be that way. Noted software expert Robert C. Martin presents a revolutionary paradigm with Clean Code: A Handbook of Agile Software Craftsmanship . Martin has teamed up with his colleagues from Object Mentor to distill their best agile practice of cleaning code “on the fly” into a book that will instill within you the values of a software craftsman and make you a better programmer—but only if you work at it. What kind of work will you be doing? You’ll be reading code—lots of code. And you will be challenged to think about what’s right about that code, and what’s wrong with it. More importantly, you will be challenged to reassess your professional values and your commitment to your craft. Clean Code is divided into three parts. The first describes the principles, patterns, and practices of writing clean code. The second part consists of several case studies of increasing complexity. Each case study is an exercise in cleaning up code—of transforming a code base that has some problems into one that is sound and efficient. The third part is the payoff: a single chapter containing a list of heuristics and “smells” gathered while creating the case studies. The result is a knowledge base that describes the way we think when we write, read, and clean code. Readers will come away from this book understanding - - How to tell the difference between good and bad code - - How to write good code and how to transform bad code into good code - - How to create good names, good functions, good objects, and good classes - - How to format code for maximum readability - - How to implement complete error handling without obscuring code logic - - How to unit test and practice test-driven development - This book is a must for any developer, software engineer, project manager, team lead, or systems analyst with an interest in producing better code.

DKK 433.00

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Git Distilled - Peter Bell - Bog - Pearson Education (US) - Plusbog.dk

In the style of Martin Fowler's classics NoSQL Distilled and UML Distilled, Git Distilled presents all you need to know to be fully productive with Git. Peter Bell clearly explains Git's fundamental concepts, starting with simple tasks such as configuring line endings and adding/committing files. He moves through the entire project life cycle, demystifying Git's complexities with simple examples and intuitive metaphors, and drilling down wherever deeper technical knowledge will help you solve specific problems. You'll find dozens of bite-sized technical recipes for handling a wide range of real-world challenges, from fixing merge conflicts to purging passwords, cleaning up local history to improving team collaboration. These recipes are all presented in the context of comprehensive best practices for making the most of both Git and Github. Key topics include: Branching and tagging Merges and conflicts Collaboration patterns Optimizing Git configurations Using additional Git tools and GUIs Going "under the hood" with Git, and fixing problems Whether you're a beginner, an experienced developer, a devOps practitioner, a Git guru, or a business person managing a development team, Git Distilled will help you solve virtually any distributed development problem you're likely to encounter.

DKK 273.00

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My Smart Home for Seniors - Michael Miller - Bog - Pearson Education (US) - Plusbog.dk

Winner, Bronze Award, APEX 2018 and 2018 INDIES Book of the Year Honorable Mention/Health This full-color introduction to the smart home has been written from the ground up with one audience in mind: seniors. No ordinary "beginner''s book," My Smart Home for Seniors approaches every topic from a 50+ person''s point of view, using meaningful, realistic examples. Full-color, step-by-step tasks–in legible print–walk you through making your home safer and easier to live in using smart technology. Learn how to: • Control your home’s lighting with smart bulbs and switches • Make your home more secure with smart doorbells, door locks, and security cameras • Automatically control your home’s temperature with a smart thermostat • Make cooking and cleaning easier with smart appliances • Use voice commands or your smart phone to control your smart devices • Use If This Then That (IFTTT) to make your smart devices interact with each other automatically • Get smart about the security and privacy concerns of smart devices • Set up your smart devices and get them to work with one another • Compare and select the best smart hub for your smart home needs • Learn to use Amazon Alexa™, Google Home™ and other voice-activated devices, as well as Apple’s HomeKit™ on the iPhone, to make your smart devices work together

DKK 205.00

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Exam Ref PL-300 Power BI Data Analyst - Daniil Maslyuk - Bog - Pearson Education (US) - Plusbog.dk

Prepare for Microsoft Exam PL-300 and help demonstrate your real-world ability to deliver actionable insights with Power BI by leveraging available data and domain expertise; to provide meaningful business value through clear data visualizations; to enable others to perform self-service analytics, and to deploy and configure solutions for consumption. Designed for data analysts, business users, and other professionals, this Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the Microsoft Certified: Power BI Data Analyst Associate level. Focus on the expertise measured by these objectives: - - Prepare the data - - Model the data - - Visualize and analyze the data - - Deploy and maintain assets - This Microsoft Exam Ref: - - Organizes its coverage by exam objectives - - Features strategic, what-if scenarios to challenge you - - Assumes you are a data analyst, business intelligence professional, report creator, or other professional seeking to validate your skills and knowledge in analyzing data with Power BI - About the Exam Exam PL-300 focuses on knowledge needed to get data from different data sources; clean, transform, and load data; design and develop data models; create model calculations with DAX; optimize model performance; create reports and dashboards; enhance reports for usability and storytelling; identify patterns and trends; and manage files, datasets, and workspaces. About Microsoft Certification Passing this exam fulfills your requirements for the Microsoft Certified: Power BI Data Analyst Associate certification, demonstrating your understanding of data repositories and data processes, and your skills in designing and building scalable data models, cleaning and transforming data, enabling advanced analytic capabilities to provide meaningful business value, and collaborating with key stakeholders to deliver relevant insights based on identified business requirements. See full details at: microsoft.com/learn

DKK 270.00

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Pandas for Everyone - Daniel Chen - Bog - Pearson Education (US) - Plusbog.dk

Manage and Automate Data Analysis with Pandas in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple data sets. Pandas for Everyone, 2nd Edition, brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world data science problems such as using regularization to prevent data overfitting, or when to use unsupervised machine learning methods to find the underlying structure in a data set.New features to the second edition include: - - Extended coverage of plotting and the seaborn data visualization library - - Expanded examples and resources - - Updated Python 3.9 code and packages coverage, including statsmodels and scikit-learn libraries - - Online bonus material on geopandas, Dask, and creating interactive graphics with Altair - Chen gives you a jumpstart on using Pandas with a realistic data set and covers combining data sets, handling missing data, and structuring data sets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes.Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability and introduces you to the wider Python data analysis ecosystem. - - Work with DataFrames and Series, and import or export data - - Create plots with matplotlib, seaborn, and pandas - - Combine data sets and handle missing data - - Reshape, tidy, and clean data sets so they’re easier to work with - - Convert data types and manipulate text strings - - Apply functions to scale data manipulations - - Aggregate, transform, and filter large data sets with groupby - - Leverage Pandas’ advanced date and time capabilities - - Fit linear models using statsmodels and scikit-learn libraries - - Use generalized linear modeling to fit models with different response variables - - Compare multiple models to select the “best” one - - Regularize to overcome overfitting and improve performance - - Use clustering in unsupervised machine learning -

DKK 419.00

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