Discover techniques to summarize the characteristics of your data using PyPlot, NumPy, SciPy, and pandas Key Features Understand the fundamental concepts of exploratory data analysis using Python Find missing values in your data and identify the correlation between different variables Practice graphical exploratory analysis techniques using Matplotlib and the Seaborn Python package Book DescriptionExploratory Data Analysis (EDA) is an approach to data analysis that involves the application of diverse techniques to gain insights into a dataset. This book will help you gain practical knowledge of the main pillars of EDA - data cleaning, data preparation, data exploration, and data visualization. You'll start by performing EDA using open source datasets and perform simple to advanced analyses to turn data into meaningful insights. You'll then learn various descriptive statistical techniques to describe the basic characteristics of data and progress to performing EDA on time-series data. As you advance, you'll learn how to implement EDA techniques for model development and evaluation and build predictive models to visualize results. Using Python for data analysis, you'll work with real-world datasets, understand data, summarize its characteristics, and visualize it for business intelligence. By the end of this EDA book, you'll have developed the skills required to carry out a preliminary investigation on any dataset, yield insights into data, present your results with visual aids, and build a model that correctly predicts future outcomes. What you will learn Import, clean, and explore data to perform preliminary analysis using powerful Python packages Identify and transform erroneous data using different data wrangling techniques Explore the use of multiple regression to describe non-linear relationships Discover hypothesis testing and explore techniques of time-series analysis Understand and interpret results obtained from graphical analysis Build, train, and optimize predictive models to estimate results Perform complex EDA techniques on open source datasets Who this book is forThis EDA book is for anyone interested in data analysis, especially students, statisticians, data analysts, and data scientists. The practical concepts presented in this book can be applied in various disciplines to enhance decision-making processes with data analysis and synthesis. Fundamental knowledge of Python programming and statistical concepts is all you need to get started with this book.

Integrate Redux with React and other front-end JavaScript frameworks efficiently and manage application states effectively Key Features Get better at building web applications with state management using Redux Learn the fundamentals of Redux to structure your app more efficiently This guide will teach you develop complex apps that would be easier to maintain Book DescriptionStarting with a detailed overview of Redux, we will follow the test-driven development (TDD) approach to develop single-page applications. We will set up JEST for testing and use JEST to test React, Redux, Redux-Sage, Reducers, and other components. We will then add important middleware and set up immutableJS in our application. We will use common data structures such as Map, List, Set, and OrderedList from the immutableJS framework. We will then add user interfaces using ReactJS, Redux-Form, and Ant Design. We will explore the use of react-router-dom and its functions. We will create a list of routes that we will need in order to create our application, and explore routing on the server site and create the required routes for our application. We will then debug our application and integrate Redux Dev tools. We will then set up our API server and create the API required for our application. We will dive into a modern approach to structuring our server site components in terms of Model, Controller, Helper functions, and utilities functions. We will explore the use of NodeJS with Express to build the REST API components. Finally, we will venture into the possibilities of extending the application for further research, including deployment and optimization. What you will learn Follow the test-driven development (TDD) approach to develop a single-page application Add important middleware, such as Redux store middleware, redux-saga middleware, and language middleware, to your application Understand how to use immutableJS in your application Build interactive components using ReactJS Configure react-router-redux and explore the differences between react-router-dom and react-router-redux Use Redux Dev tools to debug your application Set up our API server and create the API required for our application Who this book is forThis book is meant for JavaScript developers interesting in learning state management and building easy to maintain web applications.

Solve all big data problems by learning how to create efficient data models Key Features Create effective models that get the most out of big data Apply your knowledge to datasets from Twitter and weather data to learn big data Tackle different data modeling challenges with expert techniques presented in this book Book DescriptionModeling and managing data is a central focus of all big data projects. In fact, a database is considered to be effective only if you have a logical and sophisticated data model. This book will help you develop practical skills in modeling your own big data projects and improve the performance of analytical queries for your specific business requirements. To start with, you'll get a quick introduction to big data and understand the different data modeling and data management platforms for big data. Then you'll work with structured and semi-structured data with the help of real-life examples. Once you've got to grips with the basics, you'll use the SQL Developer Data Modeler to create your own data models containing different file types such as CSV, XML, and JSON. You'll also learn to create graph data models and explore data modeling with streaming data using real-world datasets. By the end of this book, you'll be able to design and develop efficient data models for varying data sizes easily and efficiently. What you will learn Get insights into big data and discover various data models Explore conceptual, logical, and big data models Understand how to model data containing different file types Run through data modeling with examples of Twitter, Bitcoin, IMDB and weather data modeling Create data models such as Graph Data and Vector Space Model structured and unstructured data using Python and R Who this book is forThis book is great for programmers, geologists, biologists, and every professional who deals with spatial data. If you want to learn how to handle GIS, GPS, and remote sensing data, then this book is for you. Basic knowledge of R and QGIS would be helpful.