scholarly journals Exploratory data science for discovery and ex‐ante assessment of operational policies: Insights from vehicle sharing

2020 ◽  
Author(s):  
Tobias Brandt ◽  
Oliver Dlugosch
Keyword(s):  
Data Science ◽  
Operational Policies ◽  
Ex Ante ◽  
Exploratory Data

scholarly journals Extracting Value from Industrial Alarms and Events: A Data-Driven Approach Based on Exploratory Data Analysis

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2772 ◽  
Author(s):  
Aguinaldo Bezerra ◽  
Ivanovitch Silva ◽  
Luiz Affonso Guedes ◽  
Diego Silva ◽  
Gustavo Leitão ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Exchange ◽  
Data Science ◽  
Exploratory Data Analysis ◽  
Data Driven ◽  
Industrial Data ◽  
Industrial Big Data ◽  
Data Driven Approach ◽  
Exploratory Data

Alarm and event logs are an immense but latent source of knowledge commonly undervalued in industry. Though, the current massive data-exchange, high efficiency and strong competitiveness landscape, boosted by Industry 4.0 and IIoT (Industrial Internet of Things) paradigms, does not accommodate such a data misuse and demands more incisive approaches when analyzing industrial data. Advances in Data Science and Big Data (or more precisely, Industrial Big Data) have been enabling novel approaches in data analysis which can be great allies in extracting hitherto hidden information from plant operation data. Coping with that, this work proposes the use of Exploratory Data Analysis (EDA) as a promising data-driven approach to pave industrial alarm and event analysis. This approach proved to be fully able to increase industrial perception by extracting insights and valuable information from real-world industrial data without making prior assumptions.

Introduction to Data Science

2021 ◽  
pp. 1-18
Author(s):  
M. Govindarajan
Keyword(s):  
Data Analysis ◽  
Predictive Modeling ◽  
Data Science ◽  
Science Data ◽  
Science Practice ◽  
Scientific Methods ◽  
Exploratory Data ◽  
Product Creation ◽  
Modeling Data ◽  
Term Data

This chapter focuses on introduction to the field of data science. Data science is the area of study which involves extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes. The term data science has emerged because of the evolution of mathematical statistics, data analysis, and big data. Data science helps to discover hidden patterns from the raw data. It enables to translate a business problem into a research project and then translate it back into a practical solution. The purpose of this chapter is to provide emphasis on integration and synthesis of concepts, techniques, applications, and tools to deal with various facets of data science practice, including data collection and integration, exploratory data analysis, predictive modeling, descriptive modeling, data product creation, evaluation, and effective communication.

Exploratory data analysis for pre and post 24/7/365 attending radiologist coverage support in an emergency department: fundamentals of data science

2019 ◽  
Vol 27 (3) ◽  
pp. 233-251
Author(s):  
Sabeena Jalal ◽  
Marshall E Lloyd ◽  
Faisal Khosa ◽  
Grace I-Hsuan Hsu ◽  
Savvas Nicolaou
Keyword(s):  
Emergency Department ◽  
Data Analysis ◽  
Data Science ◽  
Exploratory Data Analysis ◽  
Exploratory Data

scholarly journals Visual Data Science

2021 ◽  
Author(s):  
Johanna Schmidt
Keyword(s):  
Data Analysis ◽  
Data Visualization ◽  
Data Science ◽  
Future Research ◽  
The Past ◽  
Exploratory Data ◽  
Visualization Techniques ◽  
Source Of Information ◽  
Existing Data ◽  
Advanced Visualization

Organizations are collecting an increasing amount of data every day. To make use of this rich source of information, more and more employees have to deal with data analysis and data science. Exploring data, understanding its structure, and finding new insights, can be greatly supported by data visualization. Therefore, the increasing interest in data science and data analytics also leads to a growing interest in data visualization and exploratory data analysis. We will outline how existing data visualization techniques are already successfully employed in different data science workflow stages. In some cases, visualization is beneficial, while still future research will be needed for other categories. The vast amount of libraries and applications available for data visualization has fostered its usage in data science. We will highlight the differences among the libraries and applications currently available. Unfortunately, there is still a clear gap between visualization research developments over the past decades and the features provided by commonly used tools and data science applications. Although basic charting options are commonly available, more advanced visualization techniques have hardly been integrated as new features yet.

scholarly journals ExDRa: Exploratory Data Science on Federated Raw Data

2021 ◽  
Author(s):  
Sebastian Baunsgaard ◽  
Matthias Boehm ◽  
Ankit Chaudhary ◽  
Behrouz Derakhshan ◽  
Stefan Geißelsöder ◽  
...  
Keyword(s):  
Data Science ◽  
Raw Data ◽  
Exploratory Data

scholarly journals Reproducible geospatial data science: Exploratory data analysis using collaborative analysis environments

2018 ◽  
Vol 70 (5) ◽  
pp. 1844-1859
Author(s):  
Alber Sánchez ◽  
Lubia Vinhas ◽  
Gilberto Queiroz ◽  
Rolf Simoes ◽  
Vitor Gomes ◽  
...  
Keyword(s):  
Data Analysis ◽  
Data Science ◽  
Exploratory Data Analysis ◽  
Geospatial Data ◽  
Exploratory Data ◽  
Collaborative Analysis

scholarly journals Python: A Quintessential approach towards Data Science

2021 ◽  
Vol 9 (VI) ◽  
pp. 3018-3024
Author(s):  
Aniket M. Wazarkar
Keyword(s):  
Big Data ◽  
Programming Languages ◽  
Data Science ◽  
Object Oriented Programming ◽  
Software Applications ◽  
Efficient Data ◽  
Exploratory Data ◽  
Data Scientist ◽  
Analytical Tools ◽  
Oriented Programming Language

Python is an interpreted object-oriented programming language that is sustainably procuring vogue in the field of data science and analytics by fabricating complex software applications. Establishing a righteous nexus between developers and data scientists. Python has undoubtedly become paramount for data scientists mindful of cosmic and robust standard libraries which are used for analyzing and visualizing the data. Data scientists have to deal with the exceedingly large amount of data alias as big data. With elementary usage and a vast set of python libraries, Python has doubtlessly become an admired option to handle big data. Python has developed and evolved analytical tools which can help data scientist in developing machine learning models, web services, data mining, data classification, exploratory data analysis, etc. In this paper, we will scrutinize various tools which are used by python programmers for efficient data analytics, their scope with contrast to other programming languages.

Analisis Prediktif untuk Keputusan Bisnis : Peramalan Penjualan

2021 ◽  
Vol 1 (1) ◽  
pp. 32-40
Author(s):  
Amir Mahmud Husein ◽  
Fachrul Rozi Lubis ◽  
Muhammad Khoiruddin Harahap
Keyword(s):  
Data Analysis ◽  
Data Science ◽  
Exploratory Data Analysis ◽  
Exploratory Data

Peramalan penjualan produk adalah aspek utama dari manajemen pembelian, persediaan yang melebihi permintaan atau kekurangan akan berdampak pada manajemen pelayanan maupun secara ekominis. Makalah ini fokus mencoba menyajikan penerapan analisis prediktif dengan mengadopsi kerangka kerja Data Science (ilmu data) untuk menemukan wawasan yang berguna dalam pengambilan keputusan bisnis khususnya tentang peramalan penjualan produk di masa depan. Kerangka CRISP-DM diusulkan dengan tahapan pemahasan bisnis, pemahaman dan persiapan data, exploratory data analysis (EDA) dan pemodelan. Berdasarkan hasil pengujian data penjualan yang dievaluasi berdasarkan RMSE dan MAE, algoritma XGBoost menghasilkan prediksi berada dalam 1,3% kemudian ARIMA sebesar 1.6%, masih lebih baik dibandingkan LinearRegression, RandomForestdan LSTM dengan tingkat kesalahan sebesar 1.81%, 1.97%, 2.21% pada masing-masing algoritma dari data aktual.

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