DATA SCIENCE IEEE PAPERS AND PROJECTS-2020



Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems
to extract knowledge and insights from structured and unstructured data. Data science is related to data mining and big data.



Big data science at AMED-BINDS
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Science is considered to rest upon four basic pillars. Traditionally, experiment and theory are considered as the first and second of these. Advances in computing power have driven simulation to become the third pillar. The development of large repositories of data so

ASCENDS: Advanced data science toolkit for non- data scientists
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Recently, advances in machine learning and artificial intelligence have been playing more and more critical roles in a wide range of areas. For the last several years, industries have shown that how learning from data identifying patterns and making decisions with minimal

Data Management for Data ScienceTowards Embedded Analytics.
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ABSTRACT The rise of Data Science has caused an influx of new users in need of data management solutions. However, instead of utilizing existing RDBMS solutions they are opting to use a stack of independent solutions for data storage and processing glued

Usage of Visualization Techniques in Data Science Workflows.
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The increasing interest in data science and data analytics lead to a growing interest in data visualization and exploratory visual data analysis. However, there is still a clear gap between new developments in visualization research, and the visualization techniques

Doing Data Science : A Framework and Case Study
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Todays data revolution is not just about big data it is about data of all sizes and types. While the issues of volume and velocity presented by the ingestion of massive amounts of data remain prevalent, it is the rapidly developing challenges being presented by the third v

Data Science
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By data science, we mean the area of study where we learn how to incorporate data analysis tasks, as well as knowledge of the enterprise to come up with useful queries, use of statistics and visualization techniques to interpret the results, and predict future strategies to

The PAULING FILE project and materials platform for data science : From big data toward materials genome
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One of the oldest initiatives in materials informatics, the PAULING FILE project, is described. It includes the comprehensive database for inorganic crystalline compounds, their atomic structures, intrinsic physical properties and phase diagrams. On top of that, the powerful

COVID-19 Is a Data Science Issue
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COVID-19 is far more than just a data science issue itsa massive public health problem that has resulted in many deaths and is throwing a harsh light onto how we structure our society when it comes to important things like the availability and affordability of healthcare

NumPy/SciPy Recipes for Data Science : Mean Discrepancy Minimization for Vector Quantization
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We revisit the idea of vector quantization and show how to accomplish it through minimizing the mean discrepancy between two kernel density estimates. The resulting algorithm is similar to the one we discussed in the context of information theoretic vector quantization Obtaining accurate information about online activities in near-real time is becoming increasingly difficult, particularly because of the constantly increasing data volume, complexity, variety, and veracity, as well as the unscalability of traditional data processingDuring this digital era, France, like many other countries, is undergoing a transition from paper-based assessments to digital assessments in education. There is a rising interest in technology-enhanced items which offer innovative ways to assess traditional competencies

Data Science in Clinical Pharmacology and Drug Development for Improving Health Outcomes in Patients
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Data Science is about drawing useful conclusions from large and diverse data sets through exploration, prediction, and inference. 1 Alternatively, Wikipedia defines data science as a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to

Materials Data Science for Microstructural Characterization of Archaeological Concrete
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Ancient Roman concrete presents exceptional durability, low-carbon footprint, and interlocking minerals that add cohesion to the final composition. Understanding of the structural characteristics of these materials using X-ray tomography (XRT) is of paramount

NumPy/SciPy Recipes for Data Science : Information Theoretic Vector Quantization
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Abstract In this note, we discuss how to implement the idea of information theoretic vector quantization using NumPy. Since our code is properly vectorized, it shows decent runtime performance I. INTRODUCTION The term vector quantization (VQ) commonly refers to the reasonable idea

Statistics for Data Science and Policy Analysis
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This book constitutes the refereed proceedings of the Applied Statistics and Policy Analysis Conference (ASPAC2019), held on 5 6 September 201 in Wagga Wagga, NSW, Australia. The overwhelming growth of data and its users is a reality, which has put new

Decoding Data Science
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To harness the power of data science an organization needs the right data the right people, and the right culture willing to understand the new information and insights and factor them into decisionmaking processes. Employing data science is not without costs or risks, so it is Today a wide range of technological and pervasive applications have made available a huge amount of heterogeneous complex data collections. Digging deep these data collections can unearth a rich spectrum of knowledge valuable to add intelligence in targetThe tremendous advances in inexpensive computing power and intelligent techniques have opened many opportunities for managing and investigating data in virtually every field including engineering, science healthcare, business, and so on. A number of paradigms

Wheels of All Colors, Shapes, and Sizes Working Together: A Vision of Common Purpose in Data Science
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At Patterns, it doesnt really matter what domain the data being discussed and used in our articles are from, what matters is that the data science solutions being proposed in the articles can be shared across domain boundaries. Often, when I am talking to people about

Persistent Homology in Data Science
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The function P stems from a system identification for a closed-loop controller in motion control.? Task: Detect peak at non-zero frequency, which is the natural frequency of the system.? 0-th persistence diagram of super-levelset filtration of P.? Can be computed in a

The cognitive and mathematical foundations of big data science
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The big data play an indispensable role not only in a wide range of science fields and engineering applications, but also in the cognitive mechanisms of the sensation, the quantification, the qualification, the estimation, the measurement, the memory, and the

The data science tools for research of emigratio n processes in Ukraine
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The process of world globalization, labor, and academic mobility, the visa-free regime with the EU countries have caused a significant revival of migration processes in Ukraine. However, there is still the research gap in the most informative, and, at the same time

Quantifying the Evolution of Success with Network and Data Science Tools
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In this thesis, I quantify several aspects of success, defined as the collective response to individual performance in five different domains: science film, music, literature, and urban spaces. The general goal of my work is to provide new insights into the evolution of success

Data Science in Global Health Highlighting the Burdens of Human Papillomavirus and Cervical Cancer in the MENA Region Using Open Source Data and
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Cervical cancer is a top driver of death and disability across the MENA region with at least 601 deaths annually. Nearly all cases of cervical cancer are caused by Human papillomavirus (HPV), the most common viral infection of the reproductive tract. HPV

An Exploration on the Significance of Data Science in Organisational Development
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The study examines the significance importance of data science to organisational development. The study also described the roles of data scientist in a commercial organization. The study was carried out by digging up issues around the opinions of

Data Science : The emerging domain in IT
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With the increase in advance data collection tools, the scope of inferential statistics and predictive analytics has increased to a great extent and the dependency on numbers for policy formation, business solutions for complex issues, the datadriven decision taken in Data visualisation is an effective strategy to communicate information. From a multidisciplinary perspective, applying this methodology (typical of computer science ) to humanistic purposes (such as visualising musical, narrative and dramaturgical structure)

NumPy/SciPy Recipes for Data Science : Projections onto the Standard Simplex
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In plain(er) English, this is to say that, for a given objective function f : Rm → R, we need to find a minimizer x ∈ Rm whose elements xi are greater than or equal to zero and sum to one. Since this means that any feasible solution must reside in the standard simplex If the objective function

Bridging Advanced Data Science Machine Learning and Future of Accounting and Auditing: A Theoretical Review
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The aim of this theoretical review is to provide a basic understanding of advanced data science the process of data science data science paradigm, tools for data science technologies such as are R-Programming, Python, Hadoop, Tableau, D3. js, Data Wrapper

Data science : fundamental principles
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We live in a world where we collect huge amounts of data . Traditional methods and techniques are no longer sufficient to process them. In addition to the sophisticated development of computers, new ways of processing data are evolving. Data Science is aReceived: 17 July / Accepted: 17 February Ó The Author(s) Abstract In any data science and analytics project, the task of mapping a domain-specific problem to an adequate set of data mining methods by experts of the field is a crucial step

Data Science Techniques, Tools and Predictions
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Almighty created human being with numerous wants and needs which makes them associated with their own data choices and preferences. To grow and develop any business or organizations it is very obligatory to know their clients requests or customer needs based

Bayesian Methods for Data Science Using R
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Caterpillar plots: visualizes each parameters credible interval and median (especially useful for many parameters) Density plots: shows where the parameters posterior distribution lies and helps compare across chains (if densities differ between chains, then the chains arent The invited surveys at TOP are routinely assigned to three reviewers, who write public (and signed) reports. The author of the survey is then given the opportunity of a rebuttal. This process surprise those of us used to publish articles in applied mathematics and

Big Data Analysis: The Leap into a New Science Methodology
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Because of the increasing amount and ubiquity of diagnostic imaging and therapeutic procedures data in the fields of neurooncology and neurodegenerative and functional disorders, neurosurgical patients data are firmly in the realm of Big Data . Use of data Knowledge in engineering sciences is about sharing our ideas of research to others. In engineering, it has many ways to exhibit. Among them, conference is the best way to propose your idea of research and its future scope, and it is the best way to add energy to

Open-source data science and machine learning course with python
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Development of Data Science Program for Working Women
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In recent years, data science has been paid attention in Japan, and data science education has begun to be implemented for working people. It can be said that with the progress of the knowledge-based society, the need for the ability to understand and analyze data is

Bridging the skills gap in the Data Science and Internet of Things domains: A Vocational Education and Training Curriculum
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Abstract Data Science and Internet of Things are among the key drivers regarding the skills and competences needed by IT professionals. Given the value that Data Science and Internet of Things have for the EU economy and society, the foreseen skills gap at these two

Community detection and non-linear dimension reduction techniques in data science
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We studied two different approaches to interpreting high dimensional data sets: community detection and non-linear dimension reduction. Under community detection, we reviewed spectral clustering as presented in . We also examined the diffusion maps algorithm, a

Foundations for Private, Fair, and Robust Data Science
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Much of modern machine learning and statistics is based on the following paradigm: the algorithm designer specifies an objective function, and then optimizes it over some class of models. This is a powerful methodology, but while it generally results in a tool that is

Making Data Science Count In and For Education Joshua M. Rosenberg (jmrosenberg@ utk. edu) University of Tennessee Michael Lawson (lawsonm@ usc
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New data sources and analytic techniques have enabled educational researchers to ask new questions and work to address enduring problems, yet there are challenges to those learning and applying these methods. In this chapter, we provide an overview of a nascent

Mbarara University of Science and Technology (MUST)
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Health institutions are increasingly collecting vast amounts of patient data . However, mining data from those different institutions is not possible for various challenges. In this chapter, we will report on our experience on the trend of Data Science in Global Health in Uganda. The

CLOUD COMPUTING SURVEY ON SERVICES, ENHANCEMENTS AND CHALLENGES IN THE ERA OF MACHINE LEARNING AND DATA SCIENCE
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Cloud computing has sweeping impact on the human productivity. Today its used for Computing, Storage, Predictions and Intelligent Decision Making, among others. Intelligent Decision Making using Machine Learning has pushed for the Cloud Services to be evenIn this paper, we review the recent history of the concept of statistical literacy and highlight the need to integrate the new challenges and critical issues from data science associated with Big Data including ethics, epistemology, mathematical justification, and math washingWe use methods from network science to analyze corruption risk in a large administrative dataset of over 4 million public procurement contracts from European Union member states covering the years 2016. By mapping procurement markets as bipartite networks of

Data Analysis: Types, Process, Methods, Techniques and Tools
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The process of evaluating data using analytical and logical reasoning to examine each component of the data provided. This form of analysis is just one of the many steps that must be completed when conducting a research experiment. Data from various sources isThe ability to access and search metadata for marine science data is both a key requirement for answering fundamental principles of data management (making data Findable, Accessible, Interoperable and Reusable) and also in meeting domain-specific, community

Harmonizing models and observations: data assimilation for Earth System Science
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Abstract Models and observations are two fundamental methodological approaches in Earth system science (ESS). They evolve collaboratively and enhance one another. However, neither of these two approaches is perfect, and they have incompatibilities due to their Online communities are now extremely numerous. Most of them being multifaceted, dynamic, and rapidly evolving, they are of the utmost interest for social science researchers. One of the special characteristics of these communities is the production of numerical traces

Aspiration for Open Science in Accelerator Beam Physics Modeling
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beyond our immediate HEP community, accelerator and beam modeling shares already many open source project dependencies with other computational science fields for visualization, user-interfaces, hardware acceleration, compilers, I/O, data science visualization, software How can big data help to understand the migration phenomenon In this paper, we try to answer this question through an analysis of various phases of migration, comparing traditional and novel data sources and models at each phase. We concentrate on three aim in this regard is to bring scholars and practitioners closer together from different disciplines and professional fields, or those already working on cross connections of urban sustainability, sustainability science com- plexity science urban science data science and computerIn less-resourced settings, adverse pregnancy outcome rates are unacceptably high. To effect improvement, we need accurate epidemiological data about rates of death and morbidity, as well as social determinants of health and processes of care, and from each

Fingerprinting Data Center Problems with Association Rules
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Cloud management technologies increasingly automate different aspects of data center administration, where the final goal is to make self-driving solutions. Learning fingerprints of KPI-or SLO-impacting performance problems in IT infrastructures is a relevant task towards To investigate the relationship between variables that are not observed simultaneously in the same dataset, multiple-source datasets obtained from different individuals or units must be integrated into a (quasi) single-source dataset , in which all the relevant variables are

Depiction of Connection between Library and Information Science in Articles Published by Universitas Airlanggas Academics
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Sadly, most of the librarians in public universities have low digital literacy and incompetence in processing data into useful information, known as data science (Faltings and Radanovic) To increase their digital literacy, librarians should have skill related to data science

How do academic topics shift across altmetric sources A case study of the research area of Big Data
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involved titles, among which topics related to social media, health care, and science are the Following is # Datascience with frequency around 500, which is also a popular concept in the pro- cesses for extracting and discerning valuable knowledge from complex data as well

SUBSAMPLED DATA BASED ALTERNATIVE REGULARIZED ESTIMATORS.
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Subsampling the data is used in this paper as a learning method about the influence of the data points for drawing inference on the parameters of a fitted logistic regression model. The alternative, alternative regularized, alternative regularized lasso, and alternative regularized

Big DataDriven Materials Science and Its FAIR Data Infrastructure
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This chapter addresses the fourth paradigm of materials research big datadriven materials science . Its concepts and state of the art are described, and its challenges and chances are discussed. For furthering the field, open data and an all-embracing sharing, an efficient data