Analiza danych
Analiza danych jest ekscytującą dyscypliną, która umożliwia zrozumienie pewnych zjawisk, uzyskanie wglądu i wiedzy na podstawie surowych danych. Pojęcie to oznacza dokładnie przetwarzanie danych za pomocą technik matematycznych i statystycznych w celu uzyskania cennych wniosków, podjęcia ważnych decyzji i opracowania przydatnych produktów. Termin ten wywodzi się od angielskiego data science, często traktowanego jako synonim takich terminów, jak analityka biznesowa, badania operacyjne, business intelligence, wywiad konkurencyjny, analiza i modelowanie danych, a także pozyskiwanie wiedzy. Dzięki takim technologiom, jak języki Python czy R, platformy Hadoop i Spark masz szansę wyciągnąć maksimum wniosków, dostrzec szanse na rozwój swojej organizacji albo przewidzieć i zapobiec zagrożeniom.
Vikram Garg, Sharan Kumar Ravindran
With an increase in the number of users on the web, the content generated has increased substantially, bringing in the need to gain insights into the untapped gold mine that is social media data. For computational statistics, R has an advantage over other languages in providing readily-available data extraction and transformation packages, making it easier to carry out your ETL tasks. Along with this, its data visualization packages help users get a better understanding of the underlying data distributions while its range of standard statistical packages simplify analysis of the data.This book will teach you how powerful business cases are solved by applying machine learning techniques on social media data. You will learn about important and recent developments in the field of social media, along with a few advanced topics such as Open Authorization (OAuth). Through practical examples, you will access data from R using APIs of various social media sites such as Twitter, Facebook, Instagram, GitHub, Foursquare, LinkedIn, Blogger, and other networks. We will provide you with detailed explanations on the implementation of various use cases using R programming.With this handy guide, you will be ready to embark on your journey as an independent social media analyst.
Duygu Altinok
spaCy is an industrial-grade, efficient NLP Python library. It offers various pre-trained models and ready-to-use features. Mastering spaCy provides you with end-to-end coverage of spaCy's features and real-world applications.You'll begin by installing spaCy and downloading models, before progressing to spaCy's features and prototyping real-world NLP apps. Next, you'll get familiar with visualizing with spaCy's popular visualizer displaCy. The book also equips you with practical illustrations for pattern matching and helps you advance into the world of semantics with word vectors. Statistical information extraction methods are also explained in detail. Later, you'll cover an interactive business case study that shows you how to combine all spaCy features for creating a real-world NLP pipeline. You'll implement ML models such as sentiment analysis, intent recognition, and context resolution. The book further focuses on classification with popular frameworks such as TensorFlow's Keras API together with spaCy. You'll cover popular topics, including intent classification and sentiment analysis, and use them on popular datasets and interpret the classification results.By the end of this book, you'll be able to confidently use spaCy, including its linguistic features, word vectors, and classifiers, to create your own NLP apps.
Mastering Spark for Data Science. Lightning fast and scalable data science solutions
Andrew Morgan, Antoine Amend, Matthew Hallett, David...
Data science seeks to transform the world using data, and this is typically achievedthrough disrupting and changing real processes in real industries. In order to operate at this level you need to build data science solutions of substance –solutions that solve real problems. Spark has emerged as the big data platform of choice for data scientists due to its speed, scalability, and easy-to-use APIs.This book deep dives into using Spark to deliver production-grade data sciencesolutions. This process is demonstrated by exploring the construction of a sophisticated global news analysis service that uses Spark to generate continuous geopolitical and current affairs insights.You will learn all about the core Spark APIs and take a comprehensive tour of advanced libraries, including Spark SQL, Spark Streaming, MLlib, and more.You will be introduced to advanced techniques and methods that will help you to construct commercial-grade data products. Focusing on a sequence of tutorials that deliver a working news intelligence service, you will learn about advanced Spark architectures, how to work with geographic data in Spark, and how to tune Spark algorithms so they scale linearly.
Marleen Meier, David Baldwin
Tableau is one of the leading business intelligence (BI) tools used to solve BI and analytics challenges. With this book, you will master Tableau's features and offerings in various paradigms of the BI domain. This book is also the second edition of the popular Mastering Tableau series, with new features, examples, and updated code. The book covers essential Tableau concepts and its advanced functionalities. Using Tableau Hyper and Tableau Prep, you’ll be able to handle and prepare data easily. You’ll gear up to perform complex joins, spatial joins, union, and data blending tasks using practical examples. Following this, you’ll learn how to perform data densification to make displaying granular data easier. Next, you’ll explore expert-level examples to help you with advanced calculations, mapping, and visual design using various Tableau extensions. With the help of examples, you’ll also learn about improving dashboard performance, connecting Tableau Server, and understanding data visualizations. In the final chapters, you’ll cover advanced use cases such as Self-Service Analytics, Time Series Analytics, and Geo-Spatial Analytics, and learn to connect Tableau to R, Python, and MATLAB. By the end of this book, you’ll have mastered the advanced offerings of Tableau and be able to tackle common and not-so-common challenges faced in the BI domain.
Marleen Meier, David Baldwin, Kate Strachnyi
Tableau is one of the leading business intelligence (BI) tools that can help you solve data analysis challenges. With this book, you will master Tableau's features and offerings in various paradigms of the BI domain.Updated with fresh topics including Quick Level of Detail expressions, the newest Tableau Server features, Einstein Discovery, and more, this book covers essential Tableau concepts and advanced functionalities. Leveraging Tableau Hyper files and using Prep Builder, you’ll be able to perform data preparation and handling easily. You’ll gear up to perform complex joins, spatial joins, unions, and data blending tasks using practical examples. Next, you’ll learn how to execute data densification and further explore expert-level examples to help you with calculations, mapping, and visual design using Tableau extensions. You’ll also learn about improving dashboard performance, connecting to Tableau Server and understanding data visualization with examples. Finally, you'll cover advanced use cases such as self-service analysis, time series analysis, and geo-spatial analysis, and connect Tableau to Python and R to implement programming functionalities within it.By the end of this Tableau book, you’ll have mastered the advanced offerings of Tableau 2021 and be able to tackle common and advanced challenges in the BI domain.
Marleen Meier
This edition of the bestselling Tableau guide will teach you how to leverage Tableau's newest features and offerings in various paradigms of the BI domain. Updated with fresh topics, including the newest features in Tableau Server, Tableau Prep Builder, and Desktop, and up-to-date examples, this book will take you from mastering essential Tableau concepts to advanced functionalities. A chapter on AI-powered analytics has also been added.Throughout this book, you'll learn how to use Tableau Prep Builder to easily perform data preparation and handling, as well as complex joins, spatial joins and unions, using practical examples. You'll get to grips with OData or Google BigQuery, executing data densification and exploring other expert-level examples to help you with calculations, mappings, and visual designs.Later chapters will teach you all about improving dashboard performance, Tableau Server, Tableau exchange, Data Governance and AI-powered features.. Finally, you'll cover advanced use cases, such as self-service analysis, time series analysis, geo-spatial analysis, and how to connect Tableau to Python and R to implement programming functionalities within Tableau.By the end of this book, you'll have mastered Tableau 2026 and be able to tackle common and advanced challenges in the BI domain.
Mastering Text Mining with R. Extract and recognize your text data
Avinash Paul, KUMAR ASHISH
Text Mining (or text data mining or text analytics) is the process of extracting useful and high-quality information from text by devising patterns and trends. R provides an extensive ecosystem to mine text through its many frameworks and packages.Starting with basic information about the statistics concepts used in text mining, this book will teach you how to access, cleanse, and process text using the R language and will equip you with the tools and the associated knowledge about different tagging, chunking, and entailment approaches and their usage in natural language processing. Moving on, this book will teach you different dimensionality reduction techniques and their implementation in R. Next, we will cover pattern recognition in text data utilizing classification mechanisms, perform entity recognition, and develop an ontology learning framework. By the end of the book, you will develop a practical application from the concepts learned, and will understand how text mining can be leveraged to analyze the massively available data on social media.
Sergiy Suchok
There are many algorithms for data analysis and it’s not always possible to quickly choose the best one for each case. Implementation of the algorithms takes a lot of time. With the help of Mathematica, you can quickly get a result from the use of a particular method, because this system contains almost all the known algorithms for data analysis.If you are not a programmer but you need to analyze data, this book will show you the capabilities of Mathematica when just few strings of intelligible code help to solve huge tasks from statistical issues to pattern recognition. If you're a programmer, with the help of this book, you will learn how to use the library of algorithms implemented in Mathematica in your programs, as well as how to write algorithm testing procedure.With each chapter, you'll be more immersed in the special world of Mathematica. Along with intuitive queries for data processing, we will highlight the nuances and features of this system, allowing you to build effective analysis systems.With the help of this book, you will learn how to optimize the computations by combining your libraries with the Mathematica kernel.
Mathematics for Business. Explore Essential Mathematical Concepts and Techniques for Decision Making
Mercury Learning and Information, Gary Bronson, Richard...
This course provides a comprehensive understanding of quantitative methods essential for economic forecasting, resource allocation, portfolio analysis, inventory management, data-mining, and addressing social and climate challenges. Starting with foundational topics like finite mathematics and the mathematics of finance, it progresses to differential calculus, optimization, and curve fitting. These concepts are vital for solving contemporary business problems.Learners will explore algebra, finite math, finance mathematics, calculus, optimization techniques, and curve fitting, applying these methods to realistic business scenarios. Topics include cash flow, amortization, interest, loans, annuities, revenue/cost models, break-even analysis, inventory control, and econometrics. Each section includes extensive examples and exercises, reinforced by key terms and concepts, making the material accessible and practical.The course begins with basic mathematical concepts and advances through increasingly complex topics. By the end, learners will have the tools to tackle various business problems using quantitative methods, making this course invaluable for anyone in the business field. This structured approach ensures both theoretical knowledge and practical application, preparing learners for real-world challenges.
MATLAB for Machine Learning. Practical examples of regression, clustering and neural networks
Giuseppe Ciaburro
MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners.You’ll start by getting your system ready with t he MATLAB environment for machine learning and you’ll see how to easily interact with the Matlab workspace. We’ll then move on to data cleansing, mining and analyzing various data types in machine learning and you’ll see how to display data values on a plot. Next, you’ll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You’ll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you’ll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB.
Matplotlib 2.x By Example. Multi-dimensional charts, graphs, and plots in Python
Allen Yu, Claire Chung, Aldrin Yim
Big data analytics are driving innovations in scientific research, digital marketing, policy-making and much more. Matplotlib offers simple but powerful plotting interface, versatile plot types and robust customization.Matplotlib 2.x By Example illustrates the methods and applications of various plot types through real world examples. It begins by giving readers the basic know-how on how to create and customize plots by Matplotlib. It further covers how to plot different types of economic data in the form of 2D and 3D graphs, which give insights from a deluge of data from public repositories, such as Quandl Finance. You will learn to visualize geographical data on maps and implement interactive charts.By the end of this book, you will become well versed with Matplotlib in your day-to-day work to perform advanced data visualization. This book will guide you to prepare high quality figures for manuscripts and presentations. You will learn to create intuitive info-graphics and reshaping your message crisply understandable.
Srinivasa Rao Poladi
Matplotlib provides a large library of customizable plots, along with a comprehensive set of backends. Matplotlib 3.0 Cookbook is your hands-on guide to exploring the world of Matplotlib, and covers the most effective plotting packages for Python 3.7. With the help of this cookbook, you'll be able to tackle any problem you might come across while designing attractive, insightful data visualizations. With the help of over 150 recipes, you'll learn how to develop plots related to business intelligence, data science, and engineering disciplines with highly detailed visualizations. Once you've familiarized yourself with the fundamentals, you'll move on to developing professional dashboards with a wide variety of graphs and sophisticated grid layouts in 2D and 3D. You'll annotate and add rich text to the plots, enabling the creation of a business storyline. In addition to this, you'll learn how to save figures and animations in various formats for downstream deployment, followed by extending the functionality offered by various internal and third-party toolkits, such as axisartist, axes_grid, Cartopy, and Seaborn. By the end of this book, you'll be able to create high-quality customized plots and deploy them on the web and on supported GUI applications such as Tkinter, Qt 5, and wxPython by implementing real-world use cases and examples.
Metoda Lean Analytics. Zbuduj sukces startupu w oparciu o analizę danych
Alistair Croll, Benjamin Yoskovitz
Lektura obowiązkowa dla wszystkich zainteresowanych wykorzystaniem analityki w pracach nad nowym produktem i odnoszeniem sukcesów biznesowych bez konieczności wiecznego zgadywania. Peter Yared, dyrektor ds. IT, CBS Interactive To nie jest kolejna książka o liczbach, a rzecz o praktycznych wskaźnikach. Alistair i Ben nauczą Cię, jak przebić się przez mgłę danych i skupić się na właściwych, istotnych wskaźnikach, które zadecydują o Twojej porażce lub sukcesie. Ash Maurya, założyciel i dyrektor generalny Spark59 oraz WiredReach, autor książki Metoda Running Lean Okiełznane dane Jesteś przedsiębiorcą? Masz innowacyjny produkt i chcesz wejść z nim na rynek? Możesz wybrać jedną z dwóch dróg: tradycyjną, opartą na odwiecznych mądrościach starych mistrzów, albo nowoczesną, z użyciem modelu Lean Startup. Jeśli wybrałeś pierwszy sposób - zmień lekturę, jeśli drugi - gratulacje! W tej książce znajdziesz kompletny proces analityczny, od generowania pomysłów po przygotowanie zestawienia produktu i rynku. Dowiesz się z niej, jak zweryfikować swój pomysł, znaleźć odpowiednich klientów, zdefiniować ostateczną wersję produktu, zarobić na swojej działalności i ją wypromować. Znajdziesz tu konkretne i przydatne informacje, oparte na ponad trzydziestu analizach przypadku, bez których nie może się obejść żaden przedsiębiorca. Książka jest skierowana również do analityków internetowych i analityków danych, ponieważ pozwala powiązać efekty ich pracy z rozważaniami biznesowymi. Zamieszczone tu treści zainteresują też ludzi zaangażowanych w rozwój produktu, zarządzanie nim, marketing, PR oraz działalność inwestycyjną, ponieważ dzięki nim łatwiej będzie im zrozumieć i oceniać startupy. Rewolucja w sześciu prostych krokach Stwórz coś, co klienci pokochają. Zaangażuj ludzi, aby znaleźli Twój produkt i zaczęli z niego korzystać. Poznaj model Lean Startup, podstawy analityki oraz mentalności kierowania się danymi, niezbędnej do odniesienia sukcesu. Dowiedz się, na którym etapie rozwoju się znajdujesz, nad czym powinieneś pracować oraz jak zastosować model Lean Analytics we własnym startupie. Znajdź niezłe punkty odniesienia dla różnych wskaźników i naucz się wyznaczać własne wartości docelowe. Sprawdź, w jaki sposób możesz zastosować zasady Lean Analytics w funkcjonującej już organizacji, bo przecież podejście oparte na danych sprawdza się nie tylko w nowo powstałych firmach. Alistair Croll od niemal dwudziestu lat jest przedsiębiorcą, autorem książek i prelegentem. Zajmował się dużymi zbiorami danych, chmurami obliczeniowymi i startupami. W 2001 roku współuczestniczył w zakładaniu firmy Coradiant. Od tamtej pory aktywnie pomaga wielu nowo powstającym firmom i wspiera liczne startupowe imprezy. Benjamin Yoskovitz jest przedsiębiorcą z ponadpiętnastoletnim doświadczeniem w branży internetowej. Współzałożyciel Standout Jobs i Year One Labs, pełni funkcję mentora dla wielu startupów i akceleratorów przedsiębiorczości. Regularnie przemawia podczas licznych konferencji poświęconych problematyce startupów.
MrExcel's Holy Macro! Books, Mike Girvin
Unlock the full potential of Microsoft 365 Excel with this extensive guide, crafted for both beginners and seasoned users alike. Begin by uncovering the foundational reasons behind Excel’s creation and its unmatched significance in the business world. Dive deep into the structure of Excel files, worksheets, and key concepts that underscore the application’s versatility. As you progress, master efficient workflows, keyboard shortcuts, and powerful formulas, making Excel an indispensable tool for solving complex problems.Moving forward, the book will guide you through advanced topics, including logical tests, lookup functions, and the latest features like LET and LAMBDA functions. Gain hands-on experience with data analysis, exploring the full capabilities of standard pivot tables, advanced Power Query, and Power BI. Each chapter builds on the last, ensuring that you gain both practical skills and a deep understanding of Excel’s capabilities, preparing you to confidently tackle even the most challenging data tasks.By the end of this guide, you’ll not only be adept at using Excel but also equipped with strategies to apply Excel's advanced features to real-world scenarios—whether you’re interested in financial modeling, big data analysis, or simply enhancing efficiency in your day-to-day tasks.