developing analytic talent becoming a data scientist pdf download

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Learn what it takes to succeed in the the most in-demand tech jobHarvard Business Review calls it the sexiest tech job of the 21st century. Data scientists are in demand, and this unique book shows you exactly what employers want and the skill set that separates the quality data scientist from other talented IT professionals. Data science involves extracting, creating, and processing data to turn it into business value. With over 15 years of big data, predictive modeling, and business analytics experience, author Vincent Granville is no stranger to data science. In this one-of-a-kind guide, he provides insight into the essential data science skills, such as statistics and visualization techniques, and covers everything from analytical recipes and data science tricks to common job interview questions, sample resumes, and source code.The applications are endless and varied: automatically detecting spam and plagiarism, optimizing bid prices in keyword advertising, identifying new molecules to fight cancer, assessing the risk of meteorite impact. Complete with case studies, this book is a must, whether you're looking to become a data scientist or to hire one.Explains the finer points of data science, the required skills, and how to acquire them, including analytical recipes, standard rules, source code, and a dictionary of termsShows what companies are looking for and how the growing importance of big data has increased the demand for data scientistsFeatures job interview questions, sample resumes, salary surveys, and examples of job adsCase studies explore how data science is used on Wall Street, in botnet detection, for online advertising, and in many other business-critical situationsDeveloping Analytic Talent: Becoming a Data Scientist is essential reading for those aspiring to this hot career choice and for employers seeking the best candidates.

... These sets of skills are increasingly derived through tools from computer science, linguistics, econometrics, sociology, and other disciplines [43]. However, data science is more than statistics and data analysesit also involves implementing the algorithms that automatically process data to provide predictions and actions [44]. ...

... For example, a computer scientist who is familiar with the computational complexity of all sorting algorithms, a statistician who knows about singular value decomposition and its numerical stability, a software engineer with years of experience of writing code, a database specialist with strong data modelling, data warehousing, graph database, Hadoop and NoSQL expertise (big data technologies), or a predictive modeler with expertise in Bayesian networks. A vertical data scientist is the by-product of a university system that trains a person to become a computer scientist, a statistician, or an operations researcherbut not all three at the same time [44]. ...

... A horizontal data scientist is a blend of a business analyst, statistician, computer scientist and domain expert. He or she combines vision with technology, participates in the database design and data gathering process, identifies metrics and external data sources useful to maximize value discovery, is an expert in cross-validation, confidence intervals and variance reduction techniques, and deals with descriptive, predictive and prescriptive analytics [44]. ...

Eduan Kotzé

Academic programmes at South African Higher Education Institutions have predominantly educated students in managing and storing data using relational database technology. However, this is no longer sufficient. South Africa as a country will need to educate more students to manage and process structured, semi-structured and unstructured data. The main purpose of this study was to examine the status of data scientists, a role typically associated with managing these new data sets, in South Africa. The study examined the skills, knowledge and qualifications these data scientists require to do their daily tasks, and offers suggestions that ought to be considered when designing a curriculum for an academic programme in data science.

... 51-52) note that today "there is confusion about what exactly is data science, and this confusion could well lead to disillusionment as the concept diffuses into meaningless DTA buzz." Vincent Granville (2014), who is data scientist himself, believes that the term has been much abused and a lot of hype surrounds big data and data science. However, Voulgaris (2014, p. 16) argues that "data science is not a fad, but something that is here to stay and bound to evolve rapidly" and is "a response to the difficulties of working with big data and other data analysis challenges." ...

... storage and preservation of data, classification, indexing, data curation and management, metadata management and data quality). Data science also relies heavily on probability models, data mining, methods and methodology of data visualization and machine learning in order to understand and use the DTA huge amount of data (Provost and Fawcett, 2013;Granville, 2014;Cervone, 2016;Marchionini, 2016;Song and Zhu, 2016). In addition, it depends on specifics of the domains to which it is applied (e.g. ...

... Several frameworks for the education of data professionals have been developed (Waller and Fawcett, 2013;Granville, 2014;Zhu, 2016, 2017). For example, the EU funded EDISON project developed a Data Science Competence Framework (Demchenko et al., 2017). ...

Purpose Data science is a relatively new field which has gained considerable attention in recent years. This new field requires a wide range of knowledge and skills from different disciplines including mathematics and statistics, computer science and information science. The purpose of this paper is to present the results of the study that explored the field of data science from the library and information science (LIS) perspective. Design/methodology/approach Analysis of research publications on data science was made on the basis of papers published in the Web of Science database. The following research questions were proposed: What are the main tendencies in publication years, document types, countries of origin, source titles, authors of publications, affiliations of the article authors and the most cited articles related to data science in the field of LIS? What are the main themes discussed in the publications from the LIS perspective? Findings The highest contribution to data science comes from the computer science research community. The contribution of information science and library science community is quite small. However, there has been continuous increase in articles from the year 2015. The main document types are journal articles, followed by conference proceedings and editorial material. The top three journals that publish data science papers from the LIS perspective are the Journal of the American Medical Informatics Association , the International Journal of Information Management and the Journal of the Association for Information Science and Technology . The top five countries publishing are USA, China, England, Australia and India. The most cited article has got 112 citations. The analysis revealed that the data science field is quite interdisciplinary by nature. In addition to the field of LIS the papers belonged to several other research areas. The reviewed articles belonged to the six broad categories: data science education and training; knowledge and skills of the data professional; the role of libraries and librarians in the data science movement; tools, techniques and applications of data science; data science from the knowledge management perspective; and data science from the perspective of health sciences. Research limitations/implications The limitations of this research are that this study only analyzed research papers in the Web of Science database and therefore only covers a certain amount of scientific papers published in the field of LIS. In addition, only publications with the term "data science" in the topic area of the Web of Science database were analyzed. Therefore, several relevant studies are not discussed in this paper that are not reflected in the Web of Science database or were related to other keywords such as "e-science," "e-research," "data service," "data curation" or "research data management." Originality/value The field of data science has not been explored using bibliographic analysis of publications from the perspective of the LIS. This paper helps to better understand the field of data science and the perspectives for information professionals.

... For example, the text of the job advertisement could be written using the phrases that human resource specialists could consider as understandable by the potential job candidates, without consulting the domain experts in Industry 4.0. For example, Granville (2014) points out that management and human resources sometimes do not fully understand what data science is, or what skills are needed for big data analysis, resulting in job advertisements that are not specific enough or do not list relevant skills that are needed for the data scientists in the Industry 4.0 organization. Due to this, the results of our study could be biased in that they do not represent specific skills and experience expected from job seekers, but instead, represent a basic idea of what skill could be needed by organizations for their Industry 4.0 needs. ...

... Therefore, our dictionary provides a snapshot of the current state in this area. Several authors indicate that the development of data dictionaries based on the text mining of job advertisements would be a useful tool that would allow the tracking of changes in rapidly developing industries (Granville, 2014;Amado et al., 2018). In order to provide a sustainable tool with minimum bias, which could be used by human resources professionals and higher educational institutions, our approach should be repeated over several years, using more online sites that publish job advertisements, in various languages. ...

Since changes in job characteristics in areas such as Industry 4.0 are rapid, fast tool for analysis of job advertisements is needed. Current knowledge about competencies required in Industry 4.0 is scarce. The goal of this paper is to develop a profile of Industry 4.0 job advertisements, using text mining on publicly available job advertisements, which are often used as a channel for collecting relevant information about the required knowledge and skills in rapid-changing industries. We searched website, which publishes job advertisements, related to Industry 4.0, and performed text mining analysis on the data collected from those job advertisements. Analysis of the job advertisements revealed that most of them were for full time entry; associate and mid-senior level management positions and mainly came from the United States and Germany. Text mining analysis resulted in two groups of job profiles. The first group of job profiles was focused solely on the knowledge related to Industry 4.0: cyberphysical systems and the Internet of things for robotized production; and smart production design and production control. The second group of job profiles was focused on more general knowledge areas, which are adapted to Industry 4.0: supply change management, customer satisfaction, and enterprise software. Topic mining was conducted on the extracted phrases generating various multidisciplinary job profiles. Higher educational institutions, human resources professionals, as well as experts that are already employed or aspire to be employed in Industry 4.0 organizations, would benefit from the results of our analysis.

... For example, Davenport et al. (2010) and Seddon et al. (2017) have provided some foundations for differentiating between BA user types. Emerging practitioner resources for data scientists provide useful insights into the skills and capabilities that these professionals require (e.g., Granville, 2014;Harris, Murphy, & Vaisman, 2013;Patil, 2011;Viaene, 2013). Case studies that examine organizational BA adoption and benefit realization provide glimpses into BA use in specific contexts, such as banking (Shollo & Galliers, 2016), healthcare (Wang, Kung, & Byrd, 2018), manufacturing (Dremel, Herterich, Wulf, & vom Brocke, 2018), and software development (Canossa, El-Nasr, & Drachen, 2013;Kim, Zimmermann, DeLine, & Begel, 2016;Tim, Hallikainen, Pan, & Tamm, 2018). ...

... The third literature stream examines the nascent data science profession. Many studies in this stream focus on data science practice (Davenport & Patil, 2012;Granville, 2014;Patil, 2011;Viaene, 2013), though some also examine data science education (Asamoah, Sharda, Hassan Zadeh, & Kalgotra, 2017;Mikalef et al., 2018a). These books and papers provide valuable insights on data scientists' work (and particularly the competencies they require). ...

... In an ever-changing world, therefore, universities must engage in upgrading strategies and policies in order to prepare students with knowledge, skills and aptitudes in line with technological trends and advances: the alignment of academic goals with the business world is thus essential in order to enhance the creation of future professionals (Perera et al., 2017). Davenport and Patil (2012); Fisher et al. (2012); Granville (2014) and Besse and Laurent (2016) on the issue of data scientist tried to describe this professional figure, highlighting the characteristics and the main work tasks they carry out within companies: unfortunately, there is still no clear and shared definition, given the complex set of skills that a data scientist must have to be present in different market sectors. ...

... Some studies have attempted to classify data scientists according to their features. Granville (2014) identifies Vertical and Horizontal data scientists. Vertical data scientists have highly developed technical knowledge and skills. ...

... Data Science is interpreted in a number of ways, due in part to the newness of the job title [7], and also the evolving infrastructure being built to cope with the very large scale, heterogeneous, dynamic data in today's increasingly advanced, technology-driven and dependent world. This has given rise to the need not just for technology experts but also other data literate employees who must work with a range of new tools, using technical and domain or sector-specific, as well as cross-disciplinary skills [10][11][12][13]. ...

... Recent literature on the evolution of the data scientist typically highlight overlapping sets of skills and tools for or commonly used in complex data analysis. Among the most frequently cited as vital for this emerging role is statistics [7,10,11,13] -the second highest skill by frequency of mention in our datasets. Ability to capture and manage large data stores or databases -the most frequently mentioned skill in our store -and carry out sophisticated querying across multiple, distributed stores also rank high [12, among others]. ...

The analysis of increasingly large and diverse data for meaningful interpretation and question answering is handicapped by human cognitive limitations. Consequently, semi-automatic abstraction of complex data within structured information spaces becomes increasingly important, if its knowledge content is to support intuitive, exploratory discovery. Exploration of skill demand is an area where regularly updated, multi-dimensional data may be exploited to assess capability within the workforce to manage the demands of the modern, technology- and data-driven economy. The knowledge derived may be employed by skilled practitioners in defining career pathways, to identify where, when and how to update their skillsets in line with advancing technology and changing work demands. This same knowledge may also be used to identify the combination of skills essential in recruiting for new roles. To address the challenges inherent in exploring the complex, heterogeneous, dynamic data that feeds into such applications, we investigate the use of an ontology to guide structuring of the information space, to allow individuals and institutions to interactively explore and interpret the dynamic skill demand landscape for their specific needs. As a test case we consider the relatively new and highly dynamic field of Data Science, where insightful, exploratory data analysis and knowledge discovery are critical. We employ context-driven and task-centred scenarios to explore our research questions and guide iterative design, development and formative evaluation of our ontology-driven, visual exploratory discovery and analysis approach, to measure where it adds value to users' analytical activity. Our findings reinforce the potential in our approach, and point us to future paths to build on.

... In this paper, the focus is on development of a metric that is defined in the form of an algorithm and is predictive in nature. A predictive metric should fulfill five main criteria [13]: ...

... This means that the final value of G is obtained by applying a function between ordered sets of paired variables. This function preserves the given order and fulfills the condition of a predictive metric given by Granville [13]. The proposed metric does not access linear relationships between the variables. ...

Correlation analysis is an important concept for studying patterns in data and making predictions. There have been many interesting revelations by applying this concept as patterns emerg out of seemingly unrelated data. In this paper, the focus is on exploring the role of correlation analysis in data clustering. We propose an algorithm, that defines an intuitive and accurate correlation coefficient metric known as the general correlation coefficient (G). We then define a framework for agglomerative clustering, based on this metric, called G based agglomerative clustering (GBAC). This framework is validated by performing experiments using synthetic as well as real datasets. The real world dataset is taken from http://databank.worldbank.org, a high dimensional dataset on human development indicators. The objective of these evaluations is to compare the performance of the proposed framework on different types of datasets. Comparative studies are performed in order to validate the proposed metric and the clustering framework. Our approach is found to be better than the existing agglomerative clustering techniques and correlation coefficient based clusterings. It is found to be effective for small, large, as well as high dimensional data. Finally, the clusters generated using this framework are validated against the existing validation measures. It is found that GBAC generates clean, more cohesive clusters. This framework combines the predictive power of correlation coefficients with the ability of finding patterns in data obtained from agglomerative hierarchical clustering. GBAC can be applied on a wide range of clustering based applications such as social network analysis, customer segmentation, collaborative filtering, construction of biological models, etc.

... b Implementation of the TPP computational protocol in the Globus Galaxies environment [205] processing (machine-to-machine communication and distributed manipulation). DataMining-as-a-Service (DMaaS) [178], DecisionScience-as-a-Service (DSaaS) [179], Platform-as-a-Service (PaaS) [180], Infrastructure-as-a-Service (IaaS) [181] and Software-as-a-Service (SaaS) [182] are all examples of cloud-based data, protocol and infrastructure services enabling reliable, efficient and distributed data analytics. R packages [124,147], KNIME [183], WEKA [184], RapidMiner [185] and Orange [186] include hundreds of powerful open-source algorithms and software tools for high-throughput machine learning, data mining, exploration, profiling, analytics and visualization. ...

Ivo D. Dinov

Managing, processing and understanding big healthcare data is challenging, costly and demanding. Without a robust fundamental theory for representation, analysis and inference, a roadmap for uniform handling and analyzing of such complex data remains elusive. In this article, we outline various big data challenges, opportunities, modeling methods and software techniques for blending complex healthcare data, advanced analytic tools, and distributed scientific computing. Using imaging, genetic and healthcare data we provide examples of processing heterogeneous datasets using distributed cloud services, automated and semi-automated classification techniques, and open-science protocols. Despite substantial advances, new innovative technologies need to be developed that enhance, scale and optimize the management and processing of large, complex and heterogeneous data. Stakeholder investments in data acquisition, research and development, computational infrastructure and education will be critical to realize the huge potential of big data, to reap the expected information benefits and to build lasting knowledge assets. Multi-faceted proprietary, open-source, and community developments will be essential to enable broad, reliable, sustainable and efficient data-driven discovery and analytics. Big data will affect every sector of the economy and their hallmark will be 'team science'.

... Já Granville (2014), diz que um CD é um generalista que conhece negócios, estatística, ciência da computação e relaciona alguns conhecimentos e capacidades específicas que o mesmo deve ter, tais como arquitetura de dados, comunicação no ambiente empresarial e outras. Harris, Shetterley, Alter & Schnell (2013:3), são contundentes ao afirmarem que CD é "the most common term for the often PhD-level experts who operate at the frontier of analytics, where data sets are so large and the data so messy that lessskilled analysts using traditional tools cannot make sense of them. ...

... As much as there are myriad views on how a data scientist should be defined, there are also a wide range of knowledge and skills, or the so called "data scientist's tool box," being recognized as essential for the field. A variety of requirements range from statistics [6], computer science [5,7], and business knowledge [8] to creativity [9], passion [10], and patience [11]. However, these references are mostly based on personal experience and insight. ...

John Yohahn Kim
Choong Kwon Lee

A data scientist is a relatively new job title and is not yet fully defined or understood. Little research has been conducted on data scientists and there is still incongruity among its practitioners. Yet, the job market for data scientists is already active with high demand. Many companies have created their own definition of a data scientist based on their own needs. The purpose of this research is to explore the definition of a data scientist by examining how it is accepted in these industries and businesses. A content analysis of 1,240 job ads from various companies recruiting data scientists was conducted to identify what types of knowledge and skills were generally demanded. As a result, we found that data scientists were expected to be highly experienced professionals with advanced degrees. The main requisite areas of profession were statistics, modeling, machine learning, and analysis.

... A model-free confidence interval [58] is used to assess the existence of a biologically-meaningful size asymmetry resulting from single cell division events. We determine whether or not a daughter cell pair is above a defined confidence interval threshold by using an absolute threshold relative to the volume differential between two daughter cells. ...

Embryonic development proceeds through a series of differentiation events. The mosaic version of this process (binary cell divisions) can be analyzed by comparing early development of Ciona intestinalis and Caenorhabditis elegans . To do this, we reorganize lineage trees into differentiation trees using the graph theory ordering of relative cell volume. Lineage and differentiation trees provide us with means to classify each cell using binary codes. Extracting data characterizing lineage tree position, cell volume, and nucleus position for each cell during early embryogenesis, we conduct several statistical analyses, both within and between taxa. We compare both cell volume distributions and cell volume across developmental time within and between single species and assess differences between lineage tree and differentiation tree orderings. This enhances our understanding of the differentiation events in a model of pure mosaic embryogenesis and its relationship to evolutionary conservation. We also contribute several new techniques for assessing both differences between lineage trees and differentiation trees, and differences between differentiation trees of different species. The results suggest that at the level of differentiation trees, there are broad similarities between distantly related mosaic embryos that might be essential to understanding evolutionary change and phylogeny reconstruction. Differentiation trees may therefore provide a basis for an Evo-Devo Postmodern Synthesis.

... In addition to tools for big data there is also a variety of curriculum available for teaching big data concepts in a hands-on approach to cover the five Vs; Volume, Variety, Velocity, Veracity and Value (Granville, 2014). For example, the Volume concept of big data is taught with University of Arkansas hosted datasets that are made available to SAP University Alliances members. ...

Purpose The purposes of this paper are to explore demand for big data and analytics curriculum, provide an overview of the curriculum available from the SAP University Alliances program, examine the evolving usage of such curriculum, and suggests an academic research agenda for this topic. Design/methodology/approach In this work, the authors reviewed recent academic utilization of big data and analytics curriculum in a large faculty-driven university program by examining school hosting request logs over a four-year period. The authors analyze curricula usage to determine how changes in big data and analytics are being introduced to academia. Findings Results indicate that there is a substantial shift toward curriculum focusing on big data and analytics. Research limitations/implications Because this research only considered data from one proprietary software vendor, the scope of this project is limited and may not generalize to other university software support programs. Practical implications Faculty interested in creating or furthering their business process programs to include big data and analytics will find practical information, materials, suggestions, as well as a research and curriculum development agenda. Originality/value Faculty interested in creating or furthering their programs to include big data and analytics will find practical information, materials, suggestions, and a research and curricula agenda.

... Já Granville (2014), diz que um CD é um generalista que conhece negócios, estatística, ciência da computação e relaciona alguns conhecimentos e capacidades específicas que o mesmo deve ter, tais como arquitetura de dados, comunicação no ambiente empresarial e outras. Harris et al. (2013: 3), são contundentes ao afirmarem que CD é "the most common term for the often PhD-level experts who operate at the frontier of analytics, where data sets are so large and the data so messy that less skilled analysts using traditional tools cannot make sense of them. ...

... Desde la pasada década, diversos autores han caracterizado el trabajo estadístico de los científicos de datos como "el trabajo atractivo en los próximos 10 años" (Granville, 2014). La revista Forbes describe el papel del científico de datos como "el nuevo concierto en tecnología"; y que la ciencia de datos es "donde van los geeks" (Marr, 2016). ...

... According to Table 1, almost the majority of the authors agree that business domain knowledge is an important attribute [2], [7]- [13] that a data scientist should have. The ability to derive valuable insights, and science computing skills, as well as effective communication skills, are also attributes of the most important in data science [3], [5]- [9], [11], [12], [14], [15]. Other attributes such as statistical modelling knowledge, data visualisation, mathematics, data management, artificial intelligence knowledge, machine learning, analytical traits, and being curious are much referred in literature. ...

This study has the primary goal to analyze the growth of data science through the main search trends. This study was conducted by defining in high level the concept of data science as well as its main components. Supported in those elements, we identified the main trends. We used mainly data from google trends to determine the evolution of search by topics, research area, or simple expressions. It allowed us to reckon that artificial intelligence (AI) suffered a lack of interest until 2012. Then it became an increasingly popular field since 2014. This is due to the progression of machine learning and data science. Results show a cumulative search of data science since 2012.

... This capability can justify the implementation of IIoT due to the cost reductions and products' quality improvements that can be achieved. It is important to refer that advanced algorithms that are used to process the Big Data (Big Data Analytics) [11][12] can detect hidden correlations between data that can't be found by processing, locally, captured data for a single device, or from a group of devices, eventually working in different MU. In this scenario, diagnostic data from sensors, actuators and PLC (Programmable Logic Controller), as well as historical data trends of the different industrial variables, can provide the required data to establish a successful predictive maintenance, reducing maintenance costs, avoiding superfluous preventive maintenance tasks and costly unscheduled production shutdowns. ...

... Among the authors that assemble data science from longestablished areas of sciences, there is a overall agreement on the fields that feed and grow the tree of data science. For instance, [12] defines data science as the intersection of computer science, business engineering, statistics, data mining, machine learning, operations research, six sigma, automation and domain expertise, whereas [13] states that data science is a multidisciplinary intersection of mathematics expertise, business acumen and hacking skills. For [14], data science requires skills ranging from traditional computer science to mathematics to art and [15] presents a Venn diagram with data science visualized as the joining of a) hacking skills b) math and stats knowledge and c) substantive expertise. ...

Data science has employed great research efforts in developing advanced analytics, improving data models and cultivating new algorithms. However, not many authors have come across the organizational and socio-technical challenges that arise when executing a data science project: lack of vision and clear objectives, a biased emphasis on technical issues, a low level of maturity for ad-hoc projects and the ambiguity of roles in data science are among these challenges. Few methodologies have been proposed on the literature that tackle these type of challenges, some of them date back to the mid-1990, and consequently they are not updated to the current paradigm and the latest developments in big data and machine learning technologies. In addition, fewer methodologies offer a complete guideline across team, project and data & information management. In this article we would like to explore the necessity of developing a more holistic approach for carrying out data science projects. We first review methodologies that have been presented on the literature to work on data science projects and classify them according to the their focus: project, team, data and information management. Finally, we propose a conceptual framework containing general characteristics that a methodology for managing data science projects with a holistic point of view should have. This framework can be used by other researchers as a roadmap for the design of new data science methodologies or the updating of existing ones.

... Additionally, these tools will require proficiency in statistics (Tong, Kumar and Huang, 2011). One possible way for management consultants to retain their strong market position would thus be to collaborate with data scientists, who possess knowledge of both statistics and the advanced analytics tools (Flinn, 2018;Granville, 2014). This way, the benefits of the management consultants, such as business intuition, decision-making abilities and the sense for detecting the right questions to ask, may be combined with the technical expertise of the data scientists. ...

The role of consultants is ever changing. As digitalization continues to progress, the consultants of tomorrow may acquire several technological advantages, while at the same time they may also face new challenges. This theoretical/speculative study draws upon some of the available literature and the authors' own best-practice experiences in exploring some of the most pressing issues of the digitalization process of consulting of today, with an anticipation of how the role and profile of the consultants may come to develop in the near future as digitalization and the digital transformation ensues. The development of analytics tools will be of paramount importance. To this end, four phases of consulting have been identified: (1) the pre-analysis phase, (2) the problem-identification phase, (3) the analysis phase and (4) the implementation phase. The chapter concludes that digitalization will carry the greatest benefit during phase 3, the analysis phase. A risk brought on by the advancement of digitalization is that organizations may be tempted to try optimizing their performance by having in-house data scientists take on more of the consultants' traditional tasks, which may lead to less favorable outcomes. However, going forward, consultants and data scientists will likely need to cooperate and synergize their efforts.

... The emergence of Big Data and Data Intensive Systems as specialized fields within computing has seen the creation and delivery of curricula to provide education in the techniques and technologies needed to distill knowledge from datasets where traditional methods, like relational databases, do not suffice. Within the current literature and these new curricula, there is a seeming lack of a thorough and coherent method for teaching Data Intensive Systems so that students understand the theory and the practice of these systems, allowing them to be effective in the laboratory and, ultimately, as data analysts/scientists [1] [2][3] [4]. One paradigm that has been widely adopted in industry is MapReduce as implemented in open-source tool, Hadoop [3]. ...

... Since the past decade, several authors have described the statistical work of data scientists as the sexiest job of the next 10 years (Granville, 2014). Forbes magazine describes the role of the data scientist as being the new concert in technology; and that Data Science is where geeks go (Marr, 2016). ...

Víctor Lope Salvador
Xhevrie Mamaqi
Javier Vidal Bordes

La datificación creciente de la vida contemporánea en combinación con la inteligencia artificial supone en la práctica la construcción de una nueva realidad que se viene calificando como digital. El presente trabajo explora, a partir del reconocimiento y definición del nuevo paradigma, las siguientes cuestiones: en primer lugar, la necesidad de catalogar las nuevas competencias y habilidades para profesiones emergentes en la economía, la empresa y la comunicación; en segundo lugar, el reconocimiento de una oportunidad histórica para la necesaria innovación teórica y metodológica en Ciencias Sociales y en Humanidades y, en tercer lugar, la aplicación de la inteligencia artificial para la mejora de la calidad en las publicaciones científicas. Estos tres asuntos resultan ser nucleares a juicio de los autores en la medida en que los tres inciden en la necesaria renovación en la formación de las personas que van a tener que gestionar datos de todo tipo que afectan a la los modos de vida de todos los individuos. Por ello, este trabajo, tras detectar las carencias en los sistemas reglados de formación, plantea las oportunidades que el nuevo paradigma digital ofrece en lo teórico y en el terreno de la publicación científica para encarar con nuevas herramientas intelectuales y nuevos métodos los retos ineludibles de la nueva situación.

El presente trabajo explora, a partir del reconocimiento y definición del nuevo paradigma digital, las siguientes cuestiones: la necesidad de catalogar las competencias y habilidades para profesiones emergentes en la economía, la empresa y la comunicación; en segundo lugar, el reconocimiento de una oportunidad histórica para la necesaria innovación teórica y metodológica en Ciencias Sociales y en Humanidades y, en tercer lugar, la aplicación de la Inteligencia Artificial (en adelante IA) para la mejora de la calidad en las publicaciones científicas. Estos tres asuntos resultan ser nucleares, a juicio de los autores, en la medida en que los tres inciden en la necesaria renovación en la formación de las personas que van a tener que gestionar datos de todo tipo que afectan a los modos de vida de todos los individuos. Por ello, este trabajo, tras detectar las carencias en los sistemas reglados de formación, plantea las oportunidades que el nuevo paradigma digital ofrece en lo teórico y en el terreno de la publicación científica para encarar los retos ineludibles de la nueva situación.

... Choosing a value for k by visual inspection can be automated by using the percentage of variance of clusters that determines the optimum number of clusters. This method finds the optimum number of clusters automatically, based on the relationship between consecutive differences among the data points [32]. The algorithm to compute the optimum number of clusters is as follows. ...

L.V. Narasimha Prasad
M.M. Naidu

Decision trees have been found to be very effective for classification in the emerging field of data mining. This paper proposes a new method: CC-SLIQ (Cascading Clustering and Supervised Learning In Quest) to improve the performance of the SLIQ decision tree algorithm. The drawback of the SLIQ algorithm is that in order to decide which attribute is to be split at each node, a large number of Gini indices have to be computed for all attributes and for each successor pair for all records that have not been classified. SLIQ employs a presorting technique in the tree growth phase that strongly affects its ability to find the best split at a decision tree node. However, the proposed model eliminates the need to sort the data at every node of the decision tree; as an alternative the training data uses a k-means clustering data segmentation only once for every numeric attribute at the beginning of the tree growth phase. The CC-SLIQ algorithm inexpensively evaluates split points that are twice the cluster size k and results in a compact and accurate tree, scalable for large datasets as well as classified datasets with a large number of attributes, classes, and records. The classification accuracy of this technique has been compared to the existing SLIQ and Elegant decision tree methods on a large number of datasets from the UCI machine learning and Weather Underground repository. The experiments show that the proposed algorithm reduces the computation of split points by 95.62%, decision rules generated by 56.5% and also leads to better mean classification accuracy of 79.29%, thus making it a practical tool for data mining.

Correlation analysis is an effective mechanism for studying patterns in data and making predictions. Many interesting discoveries have been made by formulating correlations in seemingly unrelated data. We propose an algorithm to quantify the theory of correlations and to give an intuitive, more accurate correlation coefficient. We propose a predictive metric to calculate correlations between paired values, known as the general rank-based correlation coefficient. It fulfills the five basic criteria of a predictive metric: independence from sample size, value between −1 and 1, measuring the degree of monotonicity, insensitivity to outliers, and intuitive demonstration. Furthermore, the metric has been validated by performing experiments using a real-time dataset and random number simulations. Mathematical derivations of the proposed equations have also been provided. We have compared it to Spearman's rank correlation coefficient. The comparison results show that the proposed metric fares better than the existing metric on all the predictive metric criteria.

How can big data be leveraged to create value and what are the main barriers that prevent companies from benefiting from the full potential of data and analytics? This chapter describes the phenomenon of big data and how its use through data science is dramatically changing the basis of competition. The chapter also delves into the main organizational challenges faced by companies in extracting value from data, namely the promotion of a data-driven culture, the design of the internal and external structures, and the acquisition of the technical and behavioral skills required by big data professional roles. The aim and the structure of the book are illustrated. Shedding light on the human side of big data through the lense of emotional intelligence, the book aims to provide an in-depth understanding of the behavioral competencies that big data profiles require in order to achieve a higher performance.

Big data jobs will increase in importance over the next years. However, at the international level, the labor market for these professionals is characterized by a critical skill shortage. What are the big data specialist profiles that are most sought in the market? What are their main differences in terms of tasks and skill requirements? This chapter provides a snapshot of the most in-demand big data jobs, contributing to clarify their boundaries. It also delves into the main characteristics of the specific professional profiles that have received increasing attention in recent years, namely data scientists and data/business analysts. The review of the contributions provided by experts and scholars operating in the data science and analytics domain clarifies the main differences between these roles on the technical side. However, despite the increasing importance of soft skills, the behavioral competency profile of big data jobs is still ill defined.

For organizations using big data, one of the most important element to reach tangible results is exploiting human resources: it is not possible to manage data without using them intelligently. Considering the human intervention in relation to big data, means calling into question the so-called "data scientist". Moving from the above, the main aim of this study is using the linguistic software environment NooJ to process a large corpus of job advertisements for data scientist in Italy collected on the business-networking site LinkedIn. Creating specific linguistic resources with NooJ, we are able to identify the most required skills by companies and organizations.

The goal of this research was to investigate the level of digital divide among selected European countries according to the big data usage among their enterprises. For that purpose, we apply the K-means clustering methodology on the Eurostat data about the big data usage in European enterprises. The results indicate that there is a significant difference between selected European countries according to the overall usage of big data in their enterprises. Moreover, the enterprises that use internal experts also used diverse big data sources. Since the usage of diverse big data sources allows enterprises to gather more relevant information about their customers and competitors, this indicates that enterprises with stronger internal big data expertise also have a better chance of building strong competitiveness based on big data utilization. Finally, the substantial differences among the industries were found according to the level of big data usage.

With the advent of big data, the search for respective data experts has become more intensive. This study aims to discuss data scientist skills and some topical issues that are related to data specialist profiles. A complex competence model has been deployed, dividing the skills into three groups: hard, soft, and analytical skills. The primary focus is on analytical thinking as one of the key competences of the successful data scientist taking into account the trans-discipline nature of data science. The chapter considers a new digital divide between the society and this small group of people that make sense out of the vast data and help the organization in informed decision making. As data science training needs to be business-oriented, the curricula of the Master's degree in Data Science is compared with the required knowledge and skills for recruitment.

While always integral to scientific activity, data work has recently emerged as a key set of processes within societal activities of all kinds. While data work presents new opportunities for discovery, value creation, and decision making, its emergence also raises significant ethical issues, including those of ownership, privacy, and trust. This article presents a review of data work, and how negotiating a trade‐off between its value and risks requires locating its processes within the contexts of its conditions and consequences. These include international, national, and sectoral conditions of law, policy, and regulation at a macro level; organizational conditions of information and data governance that aim to address the value and risks of data work at a meso level; along with attention to the everyday contexts of data and information handling by data information and other professionals at a micro level. In conclusion, a conceptual framework is presented that locates the processes of data work within the matrix of its macro meso and micro conditions, its consequences for individuals, organizations, and society, and the relations between them. Suggestions are given for how research into the study of data work—its value risks and governance— can be advanced by using this framework.

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