Road runoff characteristics on costal zones – Exploratory Data Analysis based on a pilot case study

2011 ◽  
pp. 275-284
Author(s):  
Pedro Antunes ◽  
Paulo Ramísio
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Road Runoff ◽  
Exploratory Data

scholarly journals A Sensor-Based Methodology to Differentiate Pure and Mixed White Tequilas Based on Fused Infrared Spectra and Multivariate Data Treatment

Chemosensors ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 47
Author(s):  
Christian Hazael Pérez-Beltrán ◽  
Víctor M. Zúñiga-Arroyo ◽  
José M. Andrade ◽  
Luis Cuadros-Rodríguez ◽  
Guadalupe Pérez-Caballero ◽  
...  
Keyword(s):  
Data Analysis ◽  
Ir Spectra ◽  
Multivariate Data Analysis ◽  
Exploratory Data Analysis ◽  
Multivariate Data ◽  
Simple Application ◽  
Infrared Measurements ◽  
Exploratory Data ◽  
Pls Discriminant Analysis

Mexican Tequila is one of the most demanded import spirits in Europe. Its fast-raising worldwide request makes counterfeiting a profitable activity affecting both consumers and legal distillers. In this paper, a sensor-based methodology based on a combination of infrared measurements (IR) and multivariate data analysis (MVA) is presented. The case study is about differentiating two categories of white Tequila: pure Tequila (or ‘100% agave’) and mixed Tequila (or simply, Tequila). The IR spectra were treated and fused with a low-level approach. Exploratory data analysis was performed using PCA and partial least squares (PLS), whilst the authentication analyses were carried out with PLS-discriminant analysis (DA) and soft independent modeling for class analogy (SIMCA) models. Results demonstrated that data fusion of IR spectra enhanced the outcomes of the authentication models capable of differentiating pure from mixed Tequilas. In fact, PLS-DA presented the best results which correctly classified all fifteen commercial validation samples. The methodology thus presented is fast, cheap, and of simple application in the Tequila industry.

scholarly journals Big Data Analytics: A Spotify Case Study

2021 ◽  
Vol 9 (10) ◽  
pp. 1823-1829
Author(s):  
Suraj Ingle
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Customer Satisfaction ◽  
Customer Loyalty ◽  
Data Analytics ◽  
Exploratory Data Analysis ◽  
Big Data Analytics ◽  
Exploratory Data ◽  
Insight Into

Abstract: By developing products that are in line with consumer needs, anticipating their profitability and manufacturing them, Big Data has opened up a lot of possibilities for building customer loyalty and commercial business by proactively engaging and comprehensively streamlining offers across all customer touch points. The use of big data to determine the best, most efficient ways to engage and interact with their customers will be discussed in this paper. An insight into how Spotify intends to provide music lovers additional ways to find their favourite songs, interact with artists, and improve Spotify recommendations has been provided. Keywords: Big Data, Data Analytics, Customer Satisfaction, Exploratory Data Analysis

scholarly journals Systematic Approach to Perform Task Centric Exploratory Data Analysis with Case study

2021 ◽  
Vol 10 (3) ◽  
pp. 1920-1927
Keyword(s):  
Data Analysis ◽  
Systematic Approach ◽  
Exploratory Data Analysis ◽  
Data Organization ◽  
Interactive Programming ◽  
Different Types ◽  
Exploratory Data ◽  
Hidden Patterns ◽  
Visualization Techniques

Exploratorydata analysis is a method to summarize main characteristics of data, and also to understand data more deeply using visualization techniques. This paper focuses on defining systematic approach in the form of well-defined sequence of steps to explore data in various aspects. Every organization produces lot of data. Organization needs to analyze this data very carefully to extract hidden patterns in the data. Task Centric EDA[2]produces actionable insights as outcome to improve business process.This uses Pythonprogramming language and Jupyter Notebook for data analysis. Python is an object oriented and interactive programming language, which contains rich sets of libraries likepandas, MATplotlib, seaborn[10]etc. We have used different types of charts and various types of parameters to analyze retail dataset and to improve sales using precision marketing.

Assessing Systematic Bias in Museum Collections

2013 ◽  
Vol 1 (2) ◽  
pp. 77-90 ◽  
Author(s):  
Angela C. Huster
Keyword(s):  
Data Analysis ◽  
Side Effects ◽  
Exploratory Data Analysis ◽  
Systematic Bias ◽  
Museum Collections ◽  
Chi Square ◽  
Spindle Whorls ◽  
Exploratory Data ◽  
Metric Traits

AbstractArchaeologists often overlook museum collections for research purposes due to concerns about the representativeness of the artifacts in the collection. Such concerns can be addressed by comparing non-scientifically produced collections to collections of known standing, such as those from more modern projects, using the same exploratory data analysis and basic statistical methods that archaeologists use to compare sets of artifacts in other situations. As a case study, this article compares spindle whorls recovered during the 2007 excavations at the Aztec site of Calixtlahuaca with two museum collections of whorls attributed to the site. A variety of metric and non-metric traits were recorded for each item in the three collections and were compared using Kolmogrov-Smirnov, Chi-square, and Fisher's exact tests. Both museum collections show a bias toward more elaborately decorated pieces, but with varying side effects on other attributes of analytical interest.

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