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.

scholarly journals The Art of Data Science and Big Data Analytics: Inspecting and Transforming Data

2020 ◽  
Vol 9 (1) ◽  
pp. 45-56
Author(s):  
Akella Subhadra
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Storage ◽  
Data Analytics ◽  
Data Science ◽  
New Technologies ◽  
Big Data Analytics ◽  
Business Strategies ◽  
Science Data ◽  
Using Data

Data Science is associated with new discoveries, the discovery of value from the data. It is a practice of deriving insights and developing business strategies through transformation of data in to useful information. It has been evaluated as a scientific field and research evolution in disciplines like statistics, computing science, intelligence science, and practical transformation in the domains like science, engineering, public sector, business and lifestyle. The field encompasses the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. It also tackles related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation. In this paper we entitled epicycles of analysis, formal modeling, from data analysis to data science, data analytics -A keystone of data science, The Big data is not a single technology but an amalgamation of old and new technologies that assistance companies gain actionable awareness. The big data is vital because it manages, store and manipulates large amount of data at the desirable speed and time. Big data addresses detached requirements, in other words the amalgamate of multiple un-associated datasets, processing of large amounts of amorphous data and harvesting of unseen information in a time-sensitive generation. As businesses struggle to stay up with changing market requirements, some companies are finding creative ways to use Big Data to their growing business needs and increasingly complex problems. As organizations evolve their processes and see the opportunities that Big Data can provide, they struggle to beyond traditional Business Intelligence activities, like using data to populate reports and dashboards, and move toward Data Science- driven projects that plan to answer more open-ended and sophisticated questions. Although some organizations are fortunate to have data scientists, most are not, because there is a growing talent gap that makes finding and hiring data scientists in a timely manner is difficult. This paper, aimed to demonstrate a close view about Data science, big data, including big data concepts like data storage, data processing, and data analysis of these technological developments, we also provide brief description about big data analytics and its characteristics , data structures, data analytics life cycle, emphasizes critical points on these issues.

Current Approaches to Advanced Research in Data Science at International Top League Universities

2020 ◽  
Vol 17 (6) ◽  
pp. 2806-2811
Author(s):  
Wahidah Hashim ◽  
A/L Jayaretnam Prathees ◽  
Marini Othman ◽  
Andino Maseleno
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
World Data ◽  
The People ◽  
Software Engineers ◽  
Data Scientist ◽  
The Right

Data Science also known as Analytics, has a high demand in the industries right now, where professionals who are well trained in this field are being recruited by many large companies. Before the existence of data science, companies and industries search for software engineers and data analysis to sort IT related problems. However, as the internet start to being used by most of the people in the world, data keep on pouring in a large volume and velocity, software engineers and data analysis could not handle it anymore. Analyzing the tremendous size of data is called Big Data Analytics. Corporate companies have already started to realize that data scientists are the right person to tackle Big Data related problems. Low supply of data scientist has hiked in the salary of the data scientist, as the pay for a data scientist many more time higher compare to other IT related professionals. Knowledge in data science can solve any data related problems in this world. Data scientist are not only recruited by tech-giants like Google and Amazon, medium organizations also started to understand the importance of data science and they too recruit data scientist for their company. In this paper, we will explore on the requirement and knowledges of data science that can be covered in UNITEN’s computer science syllabus.

scholarly journals University Students’ Readiness for Job Opportunities in Big Data Analytics

2021 ◽  
Vol 2084 (1) ◽  
pp. 012026
Author(s):  
Sarah Yusoff ◽  
Nur Hidayah Md Noh ◽  
Norulhidayah Isa
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Basic Knowledge ◽  
Job Opportunities ◽  
Graphical Tool ◽  
Box And Whisker Plot ◽  
Whisker Plot

Abstract This study aims to explore the students’ level of readiness in taking up job opportunities in big data analytics and determine the contributing factors to students’ readiness. In addition, the crucial factors that need to be resolved are identified. This job field requires some significant criteria such as, willing to work as a team, self-effort, and specialised skills such as data visualisations and data storytelling, big data analysis, and basic knowledge on tools for big data analytics. Intellipaat.com, a platform that offers various professional online training courses, has ranked position in big data analytics and data science as the highest paying jobs in 2019. However, from 2019 onwards, Malaysia has been predicted to suffer a shortfall of data analysis professionals of up to 7,000-15,000. Our educational institutions are being encouraged to create more graduates to meet this need. The question arises on whether students are prepared and willing to work in this sector once they graduate. An online survey was constructed and distributed to all UiTM students enrolled in various bachelor’s degrees and master’s programmes. One hundred and thirty-nine students participated in this survey. A graphical tool for data tabulation was presented using a box-and-whisker plot. Additionally, correlation analysis and multiple regression were used to determine the relationship and factors that can contribute the students’ readiness for job opportunities in big data analytics. The results from the box-and-whisker plot have discovered an excellent sign of students’ readiness towards job opportunities in big data analytics. Correlation analyses has shown a weak to moderate relationship among factors and multiple linear regression analyses revealed the data visualisation including storytelling skill (DVSS) and teamwork (TW) have significantly given some impacts on the students’ opportunity in big data analytics career. The results of this study are expected to provide insights into students’ readiness for job opportunities in big data analytics.

scholarly journals Exploratory Data Analysis and Offence Prediction

2021 ◽  
Vol 12 (6) ◽  
pp. 2761-2768
Author(s):  
Vivek Gaurav Singh Et al.
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Data Science ◽  
Visual Analysis ◽  
Big Data Analytics ◽  
Network Models ◽  
Trend Prediction ◽  
Neural Network Models ◽  
Indian States

Big data is a part of data science that pinpoint different ways to diagnosis, systematically withdraw facts from informational collections that are excessively enormous or complex to be managed by customary information handling application software. Big Data Analytics(BDA) is a specific tactic for breaking down and recognizing assorted examples, kindred, and patterns inside a massive volume in order. Big data analytics (BDA) is a meticulous approach to data analysing and recognising unique layers, connections, and trends ina gigantic volume of data. We apply BDA to illegitimate information collected in this paper, where preliminary data analysis was conducted for visual analysis and trend prediction. Following statistical analysis and visualisation, some incredibly interesting facts and patterns emerge from illegal data in INDIAN states i.e. (Uttar Pradesh, New Delhi, Goa). The prognostic results demonstrate that Kerasstateful LSTM execute enhanced than neural network models. These capable outcomes will allow police departments and law enforcement agencies to better understand crime problems and gain insights that will allow them to schedule activities, predict the likelihood of incidents, efficiently allocate resources, and optimise decision making.

scholarly journals A multilevel approach to big data analysis using analytic tools and actor network theory

2018 ◽  
Vol 20 (1) ◽  
Author(s):  
Tiko Iyamu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Network Theory ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Actor Network Theory ◽  
Big Data Analysis ◽  
Data Sets ◽  
Multilevel Approach ◽  
Actor Network

Background: Over the years, big data analytics has been statically carried out in a programmed way, which does not allow for translation of data sets from a subjective perspective. This approach affects an understanding of why and how data sets manifest themselves into various forms in the way that they do. This has a negative impact on the accuracy, redundancy and usefulness of data sets, which in turn affects the value of operations and the competitive effectiveness of an organisation. Also, the current single approach lacks a detailed examination of data sets, which big data deserve in order to improve purposefulness and usefulness.Objective: The purpose of this study was to propose a multilevel approach to big data analysis. This includes examining how a sociotechnical theory, the actor network theory (ANT), can be complementarily used with analytic tools for big data analysis.Method: In the study, the qualitative methods were employed from the interpretivist approach perspective.Results: From the findings, a framework that offers big data analytics at two levels, micro- (strategic) and macro- (operational) levels, was developed. Based on the framework, a model was developed, which can be used to guide the analysis of heterogeneous data sets that exist within networks.Conclusion: The multilevel approach ensures a fully detailed analysis, which is intended to increase accuracy, reduce redundancy and put the manipulation and manifestation of data sets into perspectives for improved organisations’ competitiveness.

Big Data Analytics in Action

2019 ◽  
pp. 266-294 ◽  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Insight Into

Have you ever wondered how companies that adopt big data and analytics have generated value? Which algorithm are they using for which situation? And what was the result? These points will be discussed in this chapter in order to highlight the importance of big data analytics. To this end, and in order to give a quick introduction to what is being done in data analytics applications and to trigger the reader's interest, the author introduces some applications examples. This will allow you, in more detail, to gain more insight into the types and uses of algorithms for data analysis. So, enjoy the examples.

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