Przewodnik po MongoDB. Wydajna i skalowalna baza danych. Wydanie III

Shannon Bradshaw, Eoin Brazil, Kristina Chodorow

MongoDB jest wieloplatformowym, nierelacyjnym systemem do obsługi baz danych, napisanym w języku C++. Nie przypomina ściśle ustrukturyzowanych relacyjnych baz danych, zamiast tego korzysta z dokumentów w formacie BSON. Ułatwia to bardziej naturalne przetwarzanie informacji w aplikacjach, oczywiście przy zachowaniu możliwości tworzenia hierarchii oraz indeksowania. W ten sposób cały system zyskuje na wydajności, co jest szczególnie istotne przy przetwarzaniu bardzo dużych zbiorów danych. MongoDB umożliwia stosowanie elastycznych modeli danych, uzyskiwanie wysokiego poziomu dostępności i poziome skalowanie. Ten praktyczny przewodnik jest przeznaczony dla użytkowników bazy MongoDB w wersji 4.2. W przystępny i konkretny sposób opisuje zalety stosowania dokumentowych baz danych, równocześnie wskazuje zaawansowane metody konfiguracji systemu oraz możliwe zastosowania w różnych projektach. Książka zainteresuje zarówno użytkowników i administratorów MongoDB, jak i programistów tworzących złożone aplikacje. Przedstawia kwestie tworzenia zapytań, indeksów, agregacji, transakcji, zbiorów replik, zarządzania systemem, shardingu i administrowania danymi, trwałości danych, monitorowania systemu oraz jego zabezpieczenia. Znalazło się tu także wprowadzenie do pracy z MongoDB, omówiono też zasady pracy z klastrem shardów oraz administrowania aplikacją i serwerem bazy MongoDB. W książce między innymi: ogólne zasady pracy z MongoDB operacje zapisu i wyszukiwania oraz tworzenie złożonych zapytań indeksy w kolekcjach, agregowanie danych i transakcje lokalny zbiór replik i korzystanie z replikacji konfiguracja elementów klastra monitorowanie systemu, kopie bezpieczeństwa i odtwarzanie bazy MongoDB MongoDB - przekonaj się na własnym systemie!

Python Data Analysis Cookbook. Clean, scrape, analyze, and visualize data with the power of Python!

Ivan Idris

Data analysis is a rapidly evolving field and Python is a multi-paradigm programming language suitable for object-oriented application development and functional design patterns. As Python offers a range of tools and libraries for all purposes, it has slowly evolved as the primary language for data science, including topics on: data analysis, visualization, and machine learning.Python Data Analysis Cookbook focuses on reproducibility and creating production-ready systems. You will start with recipes that set the foundation for data analysis with libraries such as matplotlib, NumPy, and pandas. You will learn to create visualizations by choosing color maps and palettes then dive into statistical data analysis using distribution algorithms and correlations. You’ll then help you find your way around different data and numerical problems, get to grips with Spark and HDFS, and then set up migration scripts for web mining.In this book, you will dive deeper into recipes on spectral analysis, smoothing, and bootstrapping methods. Moving on, you will learn to rank stocks and check market efficiency, then work with metrics and clusters. You will achieve parallelism to improve system performance by using multiple threads and speeding up your code.By the end of the book, you will be capable of handling various data analysis techniques in Python and devising solutions for problem scenarios.

Python: Data Analytics and Visualization. Perform data processing and analysis with the help of python libraries, gain practical insights into predictive modeling and generate effective results in a variety of visually appealing charts using the plotting packages in Python

Martin Czygan, Phuong Vo.T.H, Ashish Kumar, Kirthi Raman

You will start the course with an introduction to the principles of data analysis and supported libraries, along with NumPy basics for statistics and data processing. Next, you will overview the Pandas package and use its powerful features to solve data-processing problems. Moving on, you will get a brief overview of the Matplotlib API .Next, you will learn to manipulate time and data structures, and load and store data in a file or database using Python packages. You will learn how to apply powerful packages in Python to process raw data into pure and helpful data using examples. You will also get a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or building helpful products such as recommendations and predictions using Scikit-learn. After this, you will move on to a data analytics specialization—predictive analytics. Social media and IOT have resulted in an avalanche of data. You will get started with predictive analytics using Python. You will see how to create predictive models from data. You will get balanced information on statistical and mathematical concepts, and implement them in Python using libraries such as Pandas, scikit-learn, and NumPy. You’ll learn more about the best predictive modeling algorithms such as Linear Regression, Decision Tree, and Logistic Regression. Finally, you will master best practices in predictive modeling.After this, you will get all the practical guidance you need to help you on the journey to effective data visualization. Starting with a chapter on data frameworks, which explains the transformation of data into information and eventually knowledge, this path subsequently cover the complete visualization process using the most popular Python libraries with working examplesThis Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:? Getting Started with Python Data Analysis, Phuong Vo.T.H &Martin Czygan•Learning Predictive Analytics with Python, Ashish Kumar•Mastering Python Data Visualization, Kirthi Raman

Python Data Science Essentials. Learn the fundamentals of Data Science with Python - Second Edition

Alberto Boschetti, Luca Massaron

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.

Python Feature Engineering Cookbook. Over 70 recipes for creating, engineering, and transforming features to build machine learning models - Second Edition

Soledad Galli

Feature engineering, the process of transforming variables and creating features, albeit time-consuming, ensures that your machine learning models perform seamlessly. This second edition of Python Feature Engineering Cookbook will take the struggle out of feature engineering by showing you how to use open source Python libraries to accelerate the process via a plethora of practical, hands-on recipes.This updated edition begins by addressing fundamental data challenges such as missing data and categorical values, before moving on to strategies for dealing with skewed distributions and outliers. The concluding chapters show you how to develop new features from various types of data, including text, time series, and relational databases. With the help of numerous open source Python libraries, you'll learn how to implement each feature engineering method in a performant, reproducible, and elegant manner.By the end of this Python book, you will have the tools and expertise needed to confidently build end-to-end and reproducible feature engineering pipelines that can be deployed into production.

Python Machine Learning Cookbook. 100 recipes that teach you how to perform various machine learning tasks in the real world

Prateek Joshi

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.

Python: Real World Machine Learning. Take your Python Machine learning skills to the next level

Prateek Joshi, Luca Massaron, John Hearty, Alberto Boschetti, ...

Machine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is transforming the way we understand and interact with the world around us.In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.The second module, Advanced Machine Learning with Python, is designed to take you on a guided tour of the most relevant and powerful machine learning techniques and you’ll acquire a broad set of powerful skills in the area of feature selection and feature engineering.The third module in this learning path, Large Scale Machine Learning with Python, dives into scalable machine learning and the three forms of scalability. It covers the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.This Learning Path will teach you Python machine learning for the real world. The machine learning techniques covered in this Learning Path are at the forefront of commercial practice.This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:? Python Machine Learning Cookbook by Prateek Joshi? Advanced Machine Learning with Python by John Hearty? Large Scale Machine Learning with Python by Bastiaan Sjardin, Alberto Boschetti, Luca Massaron

QlikView 11 for Developers. This book is smartly built around a practical case study – HighCloud Airlines – to help you gain an in-depth understanding of how to build applications for Business Intelligence using QlikView. A superb hands-on guide

Miguel Garc?É?íÂ!!=a, Barry Harmsen, Miguel Angel Garcia

Business Intelligence technologies are a must-have in every business to make informed decisions and keep up-to speed with the ever-evolving markets. QlikView's disruptive technology makes it a key player and leader in the industry; with its flexibility and powerful analytics environment, building QlikView apps can be mastered by both, business users as well as developers.This book will help you learn QlikView Development from a basic to a practitioner level using a step-by-step approach in a practical environment, and apply proven best practices on each topic.Throughout the book, we will build a QlikView app based on real data about Airline Operations that will help HighCloud Airlines make informed business decisions and analysis-guided strategies. HighCloud Airlines executives are evaluating if entering the US market is a good strategy and, if so, which line of business should they focus their investments on; they need QlikView to make the best decision.The application will be evolving chapter by chapter, along with your skills, going from a simple proof of concept to creating a Data Model, adding a custom style, building a Dashboard and handling and manipulating the source data via script. We will meet the HighCloud Airlines requirement by using many different data visualization objects and time-saving techniques.The whole application uses real data taken from the Bureau of Transportations statistics of the US and encompasses the operations of Airlines both domestic and international. With three years worth of data, you will help HighCloud Airlines discover where people travel the most, which are the Carriers with the most market share, what is the average load factor per airline, which aircraft is the most used to perform flights, which are the busiest airports, and a whole universe of new insights.