scholarly journals Performing Examination on H1B Visa using Data Analytics Techniques to Enhance the Employability Skills

2019 ◽  
Vol 9 (1) ◽  
pp. 7506-7509
Keyword(s):  
Data Analytics ◽  
Data Science ◽  
Employability Skills ◽  
Complete Analysis ◽  
Visualization Technique ◽  
Data Set ◽  
Foreign Labour ◽  
Data Engineering ◽  
Using Data ◽  
Job Postings

Data is useless without the skill to analyse it. Technology professional’s expertise in Data engineering are in high demand. The number of job postings related to Analytics has increased substantially. This paper provides a complete analysis on the H1B visa applicants. The analysis is based on the job positions, number of petitions filed by industry every year, demanding jobs with hike salary etc. The data set has been collected from The Office of Foreign Labour Certification (OFLC), the department responsible for issuing H1B. The Data visualization technique is used mainly to perform the analysis with respect to various parameters. This visualisation is done with the help of base map, a library in python for data science. The analysis report will provide a better enhancement in providing employability on skill based.

scholarly journals New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
César de Oliveira Ferreira Silva ◽  
Mariana Matulovic ◽  
Rodrigo Lilla Manzione
Keyword(s):  
Decision Making ◽  
Data Analytics ◽  
Data Science ◽  
Final Decision ◽  
Aquifer Recharge ◽  
Groundwater Governance ◽  
Ethical Attitude ◽  
New Information ◽  
Modeling Experiment ◽  
Using Data

Abstract Groundwater governance uses modeling to support decision making. Therefore, data science techniques are essential. Specific difficulties arise because variables must be used that cannot be directly measured, such as aquifer recharge and groundwater flow. However, such techniques involve dealing with (often not very explicitly stated) ethical questions. To support groundwater governance, these ethical questions cannot be solved straightforward. In this study, we propose an approach called “open-minded roadmap” to guide data analytics and modeling for groundwater governance decision making. To frame the ethical questions, we use the concept of geoethical thinking, a method to combine geoscience-expertise and societal responsibility of the geoscientist. We present a case study in groundwater monitoring modeling experiment using data analytics methods in southeast Brazil. A model based on fuzzy logic (with high expert intervention) and three data-driven models (with low expert intervention) are tested and evaluated for aquifer recharge in watersheds. The roadmap approach consists of three issues: (a) data acquisition, (b) modeling and (c) the open-minded (geo)ethical attitude. The level of expert intervention in the modeling stage and model validation are discussed. A search for gaps in the model use is made, anticipating issues through the development of application scenarios, to reach a final decision. When the model is validated in one watershed and then extrapolated to neighboring watersheds, we found large asymmetries in the recharge estimatives. Hence, we can show that more information (data, expertise etc.) is needed to improve the models’ predictability-skill. In the resulting iterative approach, new questions will arise (as new information comes available), and therefore, steady recourse to the open-minded roadmap is recommended. Graphic abstract

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).

Big Data Analytics

2017 ◽  
Vol 11 (2) ◽  
pp. 41-56 ◽  
Author(s):  
Yingxu Wang ◽  
Jun Peng
Keyword(s):  
Big Data ◽  
Cognitive Processes ◽  
Data Analytics ◽  
Data Science ◽  
Computational Models ◽  
Big Data Analytics ◽  
Data Systems ◽  
Data Engineering ◽  
Big Data Systems ◽  
Cognitive Foundations

Big data are pervasively generated by human cognitive processes, formal inferences, and system quantifications. This paper presents the cognitive foundations of big data systems towards big data science. The key perceptual model of big data systems is the recursively typed hyperstructure (RTHS). The RTHS model reveals the inherited complexities and unprecedented difficulty in big data engineering. This finding leads to a set of mathematical and computational models for efficiently processing big data systems. The cognitive relationship between data, information, knowledge, and intelligence is formally described.

scholarly journals Dr Miner: An Application of Auto Detecting Diabetic Retinopathy using Auto Colour Correlogramand Bagging

2021 ◽  
Vol 12 (3) ◽  
pp. 1916-1922
Author(s):  
Chew-Wai Yap Et.al
Keyword(s):  
Diabetic Retinopathy ◽  
Data Science ◽  
Learning Algorithms ◽  
Clinical Excellence ◽  
Fundus Images ◽  
Data Set ◽  
Detection Model ◽  
Nearest Neighbours ◽  
Using Data ◽  
The Uk

An application of auto-detecting Diabetic Retinopathy (DR) is indispensable to aid the ophthalmologists in diagnosing patients and also to help relevant organisations in accumulating and analysing data. This project presents DR Miner, an application that can extract data from fundus images, identify the symptoms of DR in retina images by using data science approaches, and collect the ophthalmologist’s review to improve the detection model in the future. To form the DR data set with binary classes, Auto Colour Correlogram (ACC) was utilised to extract the features from DR images. Over-sampling was then conducted to balance the class distribution in the data set. To reduce the variance of the single learning algorithms, we evaluated various bagging approaches. Theresults showed that the bagging approaches gave better results than the single learning algorithms in general. Out of all bagging approaches we evaluated, bagged k-nearest neighbours gave the best result. The sensitivity achieved was 85.1%, which met the requirement set by the UK National Institute for Clinical Excellence.

scholarly journals Assessing the educational performance of different Brazilian school cycles using data science methods

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248525
Author(s):  
Joyce de Souza Zanirato Maia ◽  
Ana Paula Arantes Bueno ◽  
João Ricardo Sato
Keyword(s):  
School Performance ◽  
Data Science ◽  
School Structure ◽  
Science Methods ◽  
Data Set ◽  
Good School ◽  
Using Data ◽  
The Impact ◽  
Educational Indicators

Educational indicators are metrics that assist in assessing the quality of the educational system. They are often associated with economic and social factors suggested to contribute to good school performance, however there is no consensus on the impact of these factors. The main objective of this work was to evaluate the factors related to school performance. Using a data set composed by Brazilian schools’ performance (IDEB), socioeconomic and school structure variables, we generated different models. The non-linear model predicted the best performance, measured by the error and determination coefficient metrics. The heterogeneity of the importance of the variable between school cycles and regions of the country was detected, this effect may contribute to the development of public educational policies.

Use of Data Analytics to Increase the Efficiency of Last Mile Logistics for Ecommerce Deliveries

2021 ◽  
pp. 167-180
Author(s):  
Gaurav Nagpal ◽  
Gaurav Kumar Bishnoi ◽  
Harman Singh Dhami ◽  
Akshat Vijayvargia
Keyword(s):  
Data Analytics ◽  
Data Science ◽  
Operational Efficiency ◽  
Use Of Data ◽  
Last Mile ◽  
Digital Transactions ◽  
Using Data ◽  
Operational Excellence ◽  
The Way

With the increasing share of digital transactions in the business, the way of operating the businesses has changed drastically, leading to an immense opportunity for achieving the operational excellence in the digital transactions. This chapter focusses on the ways of using data science to improve the operational efficiency of the last mile leg in the delivery shipments for e-commerce. Some of these avenues are predicting the attrition of field executives, identification of fake delivery attempts, reduction of mis-routing, identification of bad addresses, more effective resolution of weight disputes with the clients, reverse geo-coding for locality mapping, etc. The chapter also discusses the caution to be exercised in the use of data science, and the flip side of trying to quantify and dissect the phenomenon that is so complex and subjective in nature.

‘How can you not be romantic about baseball?’ Or how we are platonic about data

2019 ◽  
Vol 26 (5-6) ◽  
pp. 1347-1366
Author(s):  
Gerald Sim
Keyword(s):  
Big Data ◽  
Critical Discourse Analysis ◽  
Data Analytics ◽  
Data Science ◽  
Film Studies ◽  
Film Adaptation ◽  
Critical Discourse ◽  
Cultural Literacy ◽  
Cultural Work ◽  
Using Data

Due to the influence of Michael Lewis’s book and its film adaptation of the same title, ‘Moneyball’ is now a euphemism for using data analytics to generate insights. These texts perform important cultural explications of machine learning. Methodologically informed by critical discourse analysis, film studies, and cultural studies, this essay describes how the 2011 film in particular aestheticizes epistemological notions such as data framing, the semantic gap, and deep learning. Moneyball also proffers a view of analytics as Platonic knowledge, functioning ideologically alongside nerd archetypes and buddy-comedy conventions. The resultant duality between the Platonic and embodied, innervated by relations between visibility and invisibility, typifies the way people relate to Big Data and to the institutions that govern our digital lives in algorithmic culture. Moneyball performs cultural work by encouraging us to embrace data science while remaining alienated from technology and deferential to experts. Calls for technological literacy in the age of Big Data cannot underestimate the importance of cultural literacy.

scholarly journals Big Data Science and EXASOL as Big Data Analytics tool

2019 ◽  
Vol 8 (9S) ◽  
pp. 933-937
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
System Architectures ◽  
Huge Data ◽  
Exploratory Approach ◽  
Data Scientist ◽  
Using Data

Big data and Data science are the two top trends of recent years. Both can be combined together as big data science. This leads to the demand for new system architectures which facilitates the development of processes which can handle huge data volumes without deterring the agility, flexibility and the interactive feel which suits the exploratory approach of a data scientist. Businesses today have found ways of using data as the principal factor for value generation. These data-driven businesses apply a variety of data tools as data analysis is one of the chief elements in this process. In order to raise data science to the new computational level that is required to meet the challenges of big data and interactive advanced analytics, EXASOL has introduced a new technological approach. This tool enables us more effective and easy data analysis.

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