Exploratory Data Analysis With Python Lab Manual
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Computational Methods and GIS Applications in Social Science - Lab Manual
This lab manual is a companion to the third edition of the textbook Computational Methods and GIS Applications in Social Science. It uses the open-source platform KNIME to illustrate a step-by-step implementation of each case study in the book. KNIME is a workflow-based platform supporting visual programming and multiple scripting language such as R, Python, and Java. The intuitive, structural workflow not only helps students better understand the methodology of each case study in the book, but also enables them to easily replicate, transplant and expand the workflow for further exploration with new data or models. This lab manual could also be used as a GIS automation reference for advanced users in spatial analysis. FEATURES The first hands-on, open-source KNIME lab manual written in tutorial style and focused on GIS applications in social science Includes 22 case studies from the United States and China that parallel the methods developed in the textbook Provides clear step-by-step explanations on how to use the open-source platform KNIME to understand basic and advanced analytical methods through real-life case studies Enables readers to easily replicate and expand their work with new data and models A valuable guide for students and practitioners worldwide engaged in efforts to develop GIS automation in spatial analysis This lab manual is intended for upper-level undergraduate and graduate students taking courses in quantitative geography, spatial analysis, GIS applications in socioeconomic studies, GIS applications in business, and location theory, as well as researchers in the similar fields of geography, city and regional planning, sociology, and public administration.
Real-Time Environmental Monitoring
This lab manual is a companion to the second edition of the textbook Real-Time Environmental Monitoring: Sensors and Systems. Tested in pedagogical settings by the author for many years, it includes applications with state-of-the-art sensor technology and programs such as R, Python, Arduino, PHP, HTML, and SQL. It helps students and instructors in science and engineering better understand how to use and design a variety of sensors, and how to build systems and databases when monitoring different environments such as soil, water, and air. Examples of low-cost and open-access systems are included and can serve as the basis of learning tools for the concepts and techniques described in the textbook. Furthermore, the manual provides links to websites and scripts in R that allow learning how to analyze a variety of datasets available from repositories and databases maintained by many agencies and institutions. The first hands-on environmental monitoring lab manual written in tutorial style and classroom tested. Includes 14 lab guides that parallel the theory developed in 14 chapters in the companion textbook. Provides clear step-by-step protocols to understand basic and advanced theory through applicable exercises and problems. Injects a practical implementation of the existing textbook. A valuable guide for students and practitioners worldwide engaged in efforts to develop, employ, and maintain environmental monitors. Intended for upper-level undergraduate and graduate students taking courses in electrical engineering, civil and environmental engineering, mechanical engineering, geosciences, and environmental sciences, as well as instructors who teach these courses. Professionals working in fields such as environmental services, and researchers and academics in engineering will also benefit from the range of topics included in this lab manual.
Exploratory Data Analysis with Python Cookbook
Author: Ayodele Oluleye
language: en
Publisher: Packt Publishing Ltd
Release Date: 2023-06-30
Extract valuable insights from data by leveraging various analysis and visualization techniques with this comprehensive guide Purchase of the print or Kindle book includes a free PDF eBook Key Features Gain practical experience in conducting EDA on a single variable of interest in Python Learn the different techniques for analyzing and exploring tabular, time series, and textual data in Python Get well versed in data visualization using leading Python libraries like Matplotlib and seaborn Book DescriptionIn today's data-centric world, the ability to extract meaningful insights from vast amounts of data has become a valuable skill across industries. Exploratory Data Analysis (EDA) lies at the heart of this process, enabling us to comprehend, visualize, and derive valuable insights from various forms of data. This book is a comprehensive guide to Exploratory Data Analysis using the Python programming language. It provides practical steps needed to effectively explore, analyze, and visualize structured and unstructured data. It offers hands-on guidance and code for concepts such as generating summary statistics, analyzing single and multiple variables, visualizing data, analyzing text data, handling outliers, handling missing values and automating the EDA process. It is suited for data scientists, data analysts, researchers or curious learners looking to gain essential knowledge and practical steps for analyzing vast amounts of data to uncover insights. Python is an open-source general purpose programming language which is used widely for data science and data analysis given its simplicity and versatility. It offers several libraries which can be used to clean, analyze, and visualize data. In this book, we will explore popular Python libraries such as Pandas, Matplotlib, and Seaborn and provide workable code for analyzing data in Python using these libraries. By the end of this book, you will have gained comprehensive knowledge about EDA and mastered the powerful set of EDA techniques and tools required for analyzing both structured and unstructured data to derive valuable insights.What you will learn Perform EDA with leading python data visualization libraries Execute univariate, bivariate and multivariate analysis on tabular data Uncover patterns and relationships within time series data Identify hidden patterns within textual data Learn different techniques to prepare data for analysis Overcome challenge of outliers and missing values during data analysis Leverage automated EDA for fast and efficient analysis Who this book is forWhether you are a data analyst, data scientist, researcher or a curious learner looking to analyze structured and unstructured data, this book will appeal to you. It aims to empower you with essential knowledge and practical skills for analyzing and visualizing data to uncover insights. It covers several EDA concepts and provides hands-on instructions on how these can be applied using various Python libraries. Familiarity with basic statistical concepts and foundational knowledge of python programming will help you understand the content better and maximize your learning experience.