Analiza danych
Dipanjan Sarkar, Karthik Ganapathy, Raghav Bali, Tushar...
The Internet has truly become humongous, especially with the rise of various forms of social media in the last decade, which give users a platform to express themselves and also communicate and collaborate with each other. This book will help the reader to understand the current social media landscape and to learn how analytics can be leveraged to derive insights from it. This data can be analyzed to gain valuable insights into the behavior and engagement of users, organizations, businesses, and brands. It will help readers frame business problems and solve them using social data.The book will also cover several practical real-world use cases on social media using R and its advanced packages to utilize data science methodologies such as sentiment analysis, topic modeling, text summarization, recommendation systems, social network analysis, classification, and clustering. This will enable readers to learn different hands-on approaches to obtain data from diverse social media sources such as Twitter and Facebook. It will also show readers how to establish detailed workflows to process, visualize, and analyze data to transform social data into actionable insights.
Learning Spark SQL. Architect streaming analytics and machine learning solutions
Aurobindo Sarkar
In the past year, Apache Spark has been increasingly adopted for the development of distributed applications. Spark SQL APIs provide an optimized interface that helps developers build such applications quickly and easily. However, designing web-scale production applications using Spark SQL APIs can be a complex task. Hence, understanding the design and implementation best practices before you start your project will help you avoid these problems.This book gives an insight into the engineering practices used to design and build real-world, Spark-based applications. The book's hands-on examples will give you the required confidence to work on any future projects you encounter in Spark SQL.It starts by familiarizing you with data exploration and data munging tasks using Spark SQL and Scala. Extensive code examples will help you understand the methods used to implement typical use-cases for various types of applications. You will get a walkthrough of the key concepts and terms that are common to streaming, machine learning, and graph applications. You will also learn key performance-tuning details including Cost Based Optimization (Spark 2.2) in Spark SQL applications. Finally, you will move on to learning how such systems are architected and deployed for a successful delivery of your project.
Learning Splunk Web Framework. Create, extend and publish real time Splunk applications
Vincent Sesto
Building rich applications on the Web using Splunk is now simpler than ever before with the Splunk Web Framework. It empowers developers to build their own web applications with custom dashboards, tables, charts, form searches, and other functionalities in the datasets at their disposal. The book will start with the fundamentals of the Splunk Web Framework, teaching you the secrets of building interesting and user-friendly applications. In the first application, you will learn to analyze and monitor traffic hitting the NASA website and learn to create dashboards for it. You will then learn additional, and more detailed, techniques to enhance the functionalities of the app such as dashboards and forms, editing simple XML, using simple XML extensions, tokens, post-process searches, dynamic drill-downs, the Splunk Web Framework and REST API, and much more. The second app will use historical stock market data and will create custom dashboards using Splunk Web Framework; the book will now cover important topics such as creating HTML dashboards, enhancing the visual appeal of the app using CSS, and moving your app with SplunkJS.The book will provide different and interesting examples instead of the usual “Log, Index, Search, and Graph” so that Splunk will be the first tool readers think of to resolve a problem.
Joshua N. Milligan
Tableau is the gold standard of business intelligence and visual analytics tools in every industry. It enables rapid data visualization and interpretation with charts, graphs, dashboards, and much more. Updated with the latest features of Tableau, this book takes you from the foundations of the Tableau 2019 paradigm through to advanced topics.This third edition of the bestselling guide by Tableau Zen Master, Joshua Milligan, will help you come to grips with updated features, such as set actions and transparent views. Beginning with installation, you'll create your first visualizations with Tableau and then explore practical examples and advanced techniques. You'll create bar charts, tree maps, scatterplots, time series, and a variety of other visualizations. Next, you'll discover techniques to overcome challenges presented by data structure and quality and engage in effective data storytelling and decision making with business critical information. Finally, you'll be introduced to Tableau Prep, and learn how to use it to integrate and shape data for analysis.By the end of this book, you will be equipped to leverage the powerful features of Tableau 2019 for decision making.
Joshua N. Milligan
Learning Tableau strengthens your command on Tableau fundamentals and builds on advanced topics.The book starts by taking you through foundational principles of Tableau. We then demonstrate various types of connections and how to work with metadata. We teach you to use a wide variety of visualizations to analyze and communicate the data, and introduce you to calculations and parameters. We then take an in-depth look at level of detail (LOD) expressions and use them to solve complex data challenges. Up next, we show table calculations, how to extend and alter default visualizations, build an interactive dashboard, and master the art of telling stories with data.This Tableau book will introduce you to visual statistical analytics capabilities, create different types of visualizations and dynamic dashboards for rich user experiences. We then move on to maps and geospatial visualization, and the new Data Model capabilities introduced in Tableau 2020.2. You will further use Tableau Prep’s ability to clean and structure data and share the stories contained in your data.By the end of this book, you will be proficient in implementing the powerful features of Tableau 2020 for decision-making.
Joshua N. Milligan
Tableau 2025 marks a new era in data visualization and analysis, bringing together advanced AI integrations and dynamic user experiences. This sixth edition, written by Tableau Visionary and Zen Master Joshua Miligan, is an end-to-end guide to mastering the latest innovations in Tableau that transform raw data into actionable insights.This edition introduces groundbreaking features like Tableau AI (including Tableau Pulse and Tableau Agent), enhancing your analytical capabilities with AI-driven data exploration and automated insights. With detailed walkthroughs, you’ll learn to build dynamic dashboards that respond to your data in real time and work with sophisticated AI functionalities that predict trends and model scenarios.Whether you're a seasoned data professional or new to Tableau, this book provides the tools you need to leverage Tableau’s full potential. From integrating diverse data sources using the enhanced data model to employing advanced geospatial functions for detailed mapping, every chapter is packed with expert knowledge and practical applications designed to put powerful analytics at your fingertips.*Email sign-up and proof of purchase required
Jos Dirksen
If you know JavaScript and want to start creating 3D graphics that run in any browser, this book is a great choice for you. You don't need to know anything about math or WebGL; all that you need is general knowledge of JavaScript and HTML.
Akhil Arora, Shrey Mehrotra
Today enterprises generate huge volumes of data. In order to provide effective services and to make smarter and more intelligent decisions from these huge volumes of data, enterprises use big-data analytics. In recent years, Hadoop has been used for massive data storage and efficient distributed processing of data. The Yet Another Resource Negotiator (YARN) framework solves the design problems related to resource management faced by the Hadoop 1.x framework by providing a more scalable, efficient, flexible, and highly available resource management framework for distributed data processing.This book starts with an overview of the YARN features and explains how YARN provides a business solution for growing big data needs. You will learn to provision and manage single, as well as multi-node, Hadoop-YARN clusters in the easiest way. You will walk through the YARN administration, life cycle management, application execution, REST APIs, schedulers, security framework and so on. You will gain insights about the YARN components and features such as ResourceManager, NodeManager, ApplicationMaster, Container, Timeline Server, High Availability, Resource Localisation and so on.The book explains Hadoop-YARN commands and the configurations of components and explores topics such as High Availability, Resource Localization and Log aggregation. You will then be ready to develop your own ApplicationMaster and execute it over a Hadoop-YARN cluster.Towards the end of the book, you will learn about the security architecture and integration of YARN with big data technologies like Spark and Storm. This book promises conceptual as well as practical knowledge of resource management using YARN.
Liczby w HR. Matematyczne ramy najbardziej ludzkiej części biznesu
Anna Morawiec-Bartosik
Czy pracę działu HR da się zmierzyć? Współczesny biznes liczbami stoi. Wykresy, tabele, przetwarzanie danych, analizowanie wyników z przeszłości, prognozowanie sprzedaży, zakupów i produkcji... Większość działów w dużych przedsiębiorstwach wspiera się tym, co policzalne. Większość, ale raczej nie dział human resources. Dlaczego? Czyżby HR-owcy nie wierzyli w liczby i stawiali na intuicję? Może. A może po prostu brakuje im rzetelnych narzędzi, dzięki którym można przeanalizować dane, jakie są dostępne dla osób wyspecjalizowanych w zarządzaniu zasobami ludzkimi firmy? Do niedawna działy HR uchodziły powszechnie za najbardziej niemierzalne w przedsiębiorstwach. Ostatnio jednak ten trend się zmienia. Takie pojęcia jak data-based HR czy data-driven HR z roku na rok zyskują na popularności. Zarządzający firmami, a także sami pracownicy human resources chcą mierzyć efektywność coraz większej liczby procesów personalnych, by na podstawie uzyskanych wyników móc planować przyszłe działania. Tylko co mierzyć? Jakimi metodami to robić? Odpowiedzi na te i inne pytania związane z analityką zasobów ludzkich znajdziesz w tej książce. Ta książka pomoże Ci zbudować lub wzmocnić rolę analityki HR w Twojej firmie, pomoże także w rozwijaniu umiejętności i perspektywy analityków HR oraz przekona zarząd i biznes, że warto zainwestować czas i fundusze w rozwijanie tej części biznesu. Wskaże jasne cele analityki personalnej, przeprowadzi Cię krok po kroku przez najważniejsze aspekty liczb w HR, które będziesz mógł wykorzystać w swojej firmie. Przede wszystkim książka pomoże Ci w znalezieniu wspólnego języka z biznesem, finansami i zarządem ― a językiem tym są właśnie dane.
Prashant Kumar Mishra, Mukesh Kumar
Azure Synapse Analytics, which Microsoft describes as the next evolution of Azure SQL Data Warehouse, is a limitless analytics service that brings enterprise data warehousing and big data analytics together. With this book, you'll learn how to discover insights from your data effectively using this platform.The book starts with an overview of Azure Synapse Analytics, its architecture, and how it can be used to improve business intelligence and machine learning capabilities. Next, you'll go on to choose and set up the correct environment for your business problem. You'll also learn a variety of ways to ingest data from various sources and orchestrate the data using transformation techniques offered by Azure Synapse. Later, you'll explore how to handle both relational and non-relational data using the SQL language. As you progress, you'll perform real-time streaming and execute data analysis operations on your data using various languages, before going on to apply ML techniques to derive accurate and granular insights from data. Finally, you'll discover how to protect sensitive data in real time by using security and privacy features.By the end of this Azure book, you'll be able to build end-to-end analytics solutions while focusing on data prep, data management, data warehousing, and AI tasks.
Linear Regression With Python. A Tutorial Introduction to the Mathematics of Regression Analysis
James V Stone
This book offers a detailed yet approachable introduction to linear regression, blending mathematical theory with Python-based practical applications. Beginning with fundamentals, it explains the best-fitting line, regression and causation, and statistical measures like variance, correlation, and the coefficient of determination. Clear examples and Python code ensure readers can connect theory to implementation.As the journey continues, readers explore statistical significance through concepts like t-tests, z-tests, and p-values, understanding how to assess slopes, intercepts, and overall model fit. Advanced chapters cover multivariate regression, introducing matrix formulations, the best-fitting plane, and methods to handle multiple variables. Topics such as Bayesian regression, nonlinear models, and weighted regression are explored in depth, with step-by-step coding guides for hands-on practice.The final sections tie together these techniques with maximum likelihood estimation and practical summaries. Appendices provide resources such as matrix tutorials, key equations, and mathematical symbols. Designed for both beginners and professionals, this book ensures a structured learning experience. Basic mathematical knowledge or foundation is recommended.
LLM Prompt Engineering for Developers. The Art and Science of Unlocking LLMs' True Potential
Aymen El Amri
LLM Prompt Engineering For Developers begins by laying the groundwork with essential principles of natural language processing (NLP), setting the stage for more complex topics. It methodically guides readers through the initial steps of understanding how large language models work, providing a solid foundation that prepares them for the more intricate aspects of prompt engineering.As you proceed, the book transitions into advanced strategies and techniques that reveal how to effectively interact with and utilize these powerful models. From crafting precise prompts that enhance model responses to exploring innovative methods like few-shot and zero-shot learning, this resource is designed to unlock the full potential of language model technology.This book not only teaches the technical skills needed to excel in the field but also addresses the broader implications of AI technology. It encourages thoughtful consideration of ethical issues and the impact of AI on society. By the end of this book, readers will master the technical aspects of prompt engineering & appreciate the importance of responsible AI development, making them well-rounded professionals ready to focus on the advancement of this cutting-edge technology.
Giuseppe Bonaccorso
In this book, you will learn all the important machine learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. The algorithms that are covered in this book are linear regression, logistic regression, SVM, naïve Bayes, k-means, random forest, TensorFlow and feature engineering.In this book, you will how to use these algorithms to resolve your problems, and how they work. This book will also introduce you to natural language processing and recommendation systems, which help you to run multiple algorithms simultaneously.On completion of the book, you will know how to pick the right machine learning algorithm for clustering, classification, or regression for your problem
Dario Radečić
The automation of machine learning tasks allows developers more time to focus on the usability and reactivity of the software powered by machine learning models. TPOT is a Python automated machine learning tool used for optimizing machine learning pipelines using genetic programming. Automating machine learning with TPOT enables individuals and companies to develop production-ready machine learning models cheaper and faster than with traditional methods.With this practical guide to AutoML, developers working with Python on machine learning tasks will be able to put their knowledge to work and become productive quickly. You'll adopt a hands-on approach to learning the implementation of AutoML and associated methodologies. Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, this book will show you how to build automated classification and regression models and compare their performance to custom-built models. As you advance, you'll also develop state-of-the-art models using only a couple of lines of code and see how those models outperform all of your previous models on the same datasets.By the end of this book, you'll have gained the confidence to implement AutoML techniques in your organization on a production level.
Rodolfo Bonnin
Most of us have heard about the term Machine Learning, but surprisingly the question frequently asked by developers across the globe is, “How do I get started in Machine Learning?”. One reason could be attributed to the vastness of the subject area because people often get overwhelmed by the abstractness of ML and terms such as regression, supervised learning, probability density function, and so on. This book is a systematic guide teaching you how to implement various Machine Learning techniques and their day-to-day application and development. You will start with the very basics of data and mathematical models in easy-to-follow language that you are familiar with; you will feel at home while implementing the examples. The book will introduce you to various libraries and frameworks used in the world of Machine Learning, and then, without wasting any time, you will get to the point and implement Regression, Clustering, classification, Neural networks, and more with fun examples. As you get to grips with the techniques, you’ll learn to implement those concepts to solve real-world scenarios for ML applications such as image analysis, Natural Language processing, and anomaly detections of time series data. By the end of the book, you will have learned various ML techniques to develop more efficient and intelligent applications.
Machine Learning for the Web. Gaining insight and intelligence from the internet with Python
Andrea Isoni
Python is a general purpose and also a comparatively easy to learn programming language. Hence it is the language of choice for data scientists to prototype, visualize, and run data analyses on small and medium-sized data sets. This is a unique book that helps bridge the gap between machine learning and web development. It focuses on the difficulties of implementing predictive analytics in web applications. We focus on the Python language, frameworks, tools, and libraries, showing you how to build a machine learning system. You will explore the core machine learning concepts and then develop and deploy the data into a web application using the Django framework. You will also learn to carry out web, document, and server mining tasks, and build recommendation engines. Later, you will explore Python’s impressive Django framework and will find out how to build a modern simple web app with machine learning features.