Eda

Good Cascade Impactor Practices, AIM and EDA for Orally Inhaled Products

The purpose of this publication is to introduce a new, simpler and more effective way in which to interpret pharmaceutical aerosol particle size data from orally inhaled products (OIPs). Currently, the compendial and regulatory requirements dictate the need for measurements by full resolution multi-stage cascade impactor (CI), a process that is demanding for the operator, time consuming, prone to experimental error, and challenging for method transfers from one laboratory to another. Furthermore, we shall show that the current practice of reducing information from mass-weighted aerodynamic particle size distribution (APSD) measurements through the use of CI stage groupings is not the most effective decision-making tool for OIP quality control (QC) in comparison with newly introduced, mutually-independent efficient data analysis (EDA) metrics that can be derived either from full resolution or abbreviated impactor measurements (AIM).

Hands-On Exploratory Data Analysis with Python

Discover techniques to summarize the characteristics of your data using PyPlot, NumPy, SciPy, and pandas Key FeaturesUnderstand the fundamental concepts of exploratory data analysis using PythonFind missing values in your data and identify the correlation between different variablesPractice graphical exploratory analysis techniques using Matplotlib and the Seaborn Python packageBook Description Exploratory 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 learnImport, clean, and explore data to perform preliminary analysis using powerful Python packagesIdentify and transform erroneous data using different data wrangling techniquesExplore the use of multiple regression to describe non-linear relationshipsDiscover hypothesis testing and explore techniques of time-series analysisUnderstand and interpret results obtained from graphical analysisBuild, train, and optimize predictive models to estimate resultsPerform complex EDA techniques on open source datasetsWho this book is for This 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.

EDA Oversight