Data Engineering 4.0

2021 ◽  
Author(s):  
Herbert Weber
Keyword(s):  
Data Engineering

Data Engineering

2019 ◽  
pp. 29-71
Author(s):  
Ervin Varga
Keyword(s):  
Data Engineering

scholarly journals Are Artificial Intelligence (AI) And Machine Learning (ML) Having An Effective Role In Helping Humanity Address The New Coronavirus Pandemic?

2020 ◽  
Vol 17 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Outbreaks ◽  
Hospital Capacity ◽  
Data Engineering ◽  
Clinical Presentations ◽  
The Face ◽  
Effective Role ◽  
Using Data ◽  
The Right

COVID-19 has become a pandemic affecting the most of countries in the world. One of the most difficult decisions doctors face during the Covid-19 epidemic is determining which patients will stay in hospital, and which are safe to recover at home. In the face of overcrowded hospital capacity and an entirely new disease with little data-based evidence for diagnosis and treatment, the old rules for determining which patients should be admitted have proven ineffective. But machine learning can help make the right decision early, save lives and lower healthcare costs. So, there is therefore an urgent and imperative need to collect data describing clinical presentations, risks, epidemiology and outcomes. On the other side, artificial intelligence(AI) and machine learning(ML) are considered a strong firewall against outbreaks of diseases and epidemics due to its ability to quickly detect, examine and diagnose these diseases and epidemics.AI is being used as a tool to support the fight against the epidemic that swept the entire world since the beginning of 2020.. This paper presents the potential for using data engineering, ML and AI to confront the Coronavirus, predict the evolution of disease outbreaks, and conduct research in order to develop a vaccine or effective treatment that protects humanity from these deadly diseases.

scholarly journals Data Wrangling using Python

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3491-3495
Keyword(s):  
Data Analytics ◽  
Data Science ◽  
Research Work ◽  
Research Area ◽  
Mapping Data ◽  
Data Format ◽  
Data Analyst ◽  
Data Engineering ◽  
Data Scientist ◽  
Term Data

The term Data Engineering did not get much popularity as the terminologies like Data Science or Data Analytics, mainly because the importance of this technique or concept is normally observed or experienced only during working with data or handling data or playing with data as a Data Scientist or Data Analyst. Though neither of these two, but as an academician and the urge to learn, while working with Python, this topic ‘Data engineering’ and one of its major sub topic or concept ‘Data Wrangling’ has drawn attention and this paper is a small step to explain the experience of handling data which uses Wrangling concept, using Python. So Data Wrangling, earlier referred to as Data Munging (when done by hand or manually), is the method of transforming and mapping data from one available data format into another format with the idea of making it more appropriate and important for a variety of relatedm purposes such as analytics. Data wrangling is the modern name used for data pre-processing rather Munging. The Python Library used for the research work shown here is called Pandas. Though the major Research Area is ‘Application of Data Analytics on Academic Data using Python’, this paper focuses on a small preliminary topic of the mentioned research work named Data wrangling using Python (Pandas Library).

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