Advanced Data Science and Analytics with Python

The development of information technology encourages the government to digitize business processes. It is generated with a large and varied volume from various data sources so that advanced data analytics (DA) is required to overcome this to support organization's data driven decision making. It's necessary to prepare DA based on DA readiness model so that the implementation of DA can run successfully. Whereas currently, there is limited study and no standard model for DA readiness. The focus of this study is to propose model readiness of implementing data analytics that is suitable in Indonesian government. The model refers to DA readiness model based on literature review on 15 papers relevant to DA readiness. Then it's verified by 7 experts. Furthermore, online survey was conducted to test the model that affects the readiness of implementing data analytics in Indonesian government. The survey results were analyzed using factor analysis. As a result, DA readiness model contains 4 dimensions, 11 factors, and 78 indicators where its dimensions consist of information system, organizational and cultural, organization structure and resource readiness. This model can describe 85% of the data analysis readiness requirements in the Indonesian government. In order to implement data analytics successfully, the government needs to improve the readiness of information systems, organizational and cultural, organizational structures, and resources.

Data analytics is a key factor to make fully informed and data-driven decisions in modern manufacturing companies. With increasing international competition, classical approaches to data analytics have to be enhanced toward advanced data analytics to create smarter products, production processes and customer services. This leverages the full potential of the data to quickly gain insights and make decisions. Application areas include predictive analytics, augmented analytics and real-time analytics. In this paper an advanced data analytics platform (ADAP) for manufacturing companies is presented and verified on a real-world production line. ADAP combines process data obtained in a physical environment with domain knowledge from experts to create data-driven models in a digital environment. In addition, ADAP is well scalable and provides a comprehensible data structure throughout the entire data workflow. ADAP is verified on two different advanced data analytics applications on an electrocoating plant. In order to relate the applications to real production requirements, the needs of local manufactures from the region Ostwestfalen-Lippe (OWL) are taken into account.

In the rapidly evolving landscape of data mining, the effective extraction of valuable insights from large datasets is paramount. This survey paper investigates the pivotal roles of data visualization and analytics in the intricate process of data mining abstraction. We delve into the symbiotic relationship between these two components, examining how they synergistically contribute to the extraction, representation, and interpretation of meaningful patterns and trends within complex datasets. The survey begins by elucidating the fundamental concepts of data mining abstraction and the significance of distilling actionable knowledge from raw data. It subsequently explores the multifaceted benefits of data visualization, elucidating its role in pattern identification, insight generation, and seamless communication of findings to diverse stakeholders.In parallel, the paper navigates through the landscape of data analytics, unraveling its diverse methods such as descriptive analytics, predictive analytics, and prescriptive analytics. Emphasis is placed on how these analytical techniques enhance the abstraction process, providing statistical rigor and predictive power to unveil hidden insights.The integration of data visualization and analytics is a focal point, showcasing their collective impact on various stages of data mining. From exploratory data analysis (EDA) for initial dataset understanding to the evaluation of mining models, the survey illuminates the collaborative nature of these components. Interactive dashboards emerge as a powerful tool, allowing users to dynamically explore datasets, visualize trends, and perform real-time analytics. Key Words:Data visualization , Data analytics ,Data mining , Tools and Software , Metrics and Key Performance Indicators (KPIs).

The importance of data has been gradually acknowledged by fashion professionals to improve sales and margins because fashion brands and retailers need to develop, manufacture, and sell styles that resonate with consumers. Lately, advancements in data analytics, machine learning, and computing power, the value of utilizing artificial intelligence (AI)-based software or applications has been well acknowledged by fashion brans and retailers who want to apply a data-driven decision-making approach to develop more efficient fashion design, merchandising, and marketing strategies. In this research, it is intended to review the roles and importance of data analytics in the fashion merchandising process. AI-powered data analytics applications or services that are currently available in the fashion industry are also introduced. Finally, a need for developing courses or programs focusing on fashion- specific data analytics in higher education is addressed as more and more fashion brands and retailers are trying to hire fashion data analysts. Collaboration with technology partners who are providing AI-powered data analytics services to fashion brands and retailers is needed to educate fashion students with practical knowledge and skills

Recent advances in Additive Manufacturing (AM), particularly in production scenarios, have been largely driven by insights achieved through data analytics. AM has greatly benefited from the increasingly large amounts of data generated during the design to product transformation. Despite the large amounts of data that can be generated from each build, the variations that can occur between builds create challenges in data reuse. Advances in data analytics are coming in the form of advanced machine learning algorithms, and are often associated with deep learning, where large amounts of data are analyzed, and interpretations are made. While such algorithms are increasingly adept at solving complex problems, solutions are highly dependent on the data used to train the algorithms and thus often subject to unwanted bias. Contextualization of data can help limit unintended bias and improve the probability of attaining viable results. Data contextualization often comes through domain context. This work describes the development of an AM ontology to support context-aware data analytics. The Additive Manufacturing Data Analytics Ontology, or AMDA Ontology, is developed to facilitate the contextualization of AM data through the representation of explicit AM concepts throughout the design to product transformation and the encoding of inhering relationships within the concepts. The AM concepts are accompanied by a suite of concepts representative of the necessary modeling, simulation, and analytics terms to create links between AM data, AM data analytics opportunities, and appropriate machine learning algorithms. The early results indicate that AMDA ontology has the ability to facilitate key correlations between AM data and the analytic opportunities to enhance the design to product transformation of AM parts.

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.

Data analytics is the future of getting ahead for engineers and geoscientists in the exploration and production (E&P) business. Three executives of large companies recently played up the growing importance of data-driven employee development, from data science boot camps at BP to the Citizen Data Scientist program at ConocoPhillips. “We have got a data science community that is growing by leaps and bounds,” said Michael Rowley, director of technology and innovation at BP, during a presentation at the recent Data Science Convention 2018 put on by the Data Analytics Study Group of the SPE Gulf Coast Section. “The most powerful way for us to pro-mote data analytics is to give our technical people training so they are fully armed with data analytics tools,” said Greg Leveille, chief technology officer of ConocoPhillips. Those willing to learn and apply these new tools “will have a disproportional impact on each of your companies,” Leveille said, adding “our industry will be populated by people who know and who can do data analytics well.” In a tough job market, data skills could offer a critical edge. If two people are interviewing for a job in unconventional exploration, and one has analytics skills and experience and the other does not, “guess who gets the job,” said Andy Flowers, director of advanced analytics for Marathon Oil. One sign of the times was the number of data scientists asking questions at the gathering. Another was the 400-person crowd at the first-ever gathering. They are building and maintaining the industry’s new data gathering and analysis infrastructure, putting large databases and analytical tools in the hands of engineers, geoscientists, and financial managers. They are a small part of the E&P workforce, are hard to find, and costly to hire. “We feel like we won the lottery when we get a new person in” for the data science team, Flowers said. But engineers and geoscientists will remain central to E&P companies because “you cannot outsource knowledge of the oil industry,” Flowers said. They will be asked to use their experience to focus the work on the most important problems, and apply methods drawing on both traditional and statistical approaches. “People with a good grounding and understanding of physics, combined with analytics, can solve problems that are very hard to solve” with first principles physics, Leveille said. Increasingly, the data-driven tools will also integrate the economic aspects of decision making. Engineering decisions need to be aligned with current corporate goals. An application integrating technical and economic information could help an engineer tailor a gas project development plan based on predicted demand. “Not everything is about geology and physics,” Rowley said. Another priority is to figure out “how to connect up data in the financial space,” he said.

The integration of Artificial Intelligence (AI) and data science into healthcare program management offers transformative potential for enhancing patient care pathways. By leveraging advanced algorithms and data analytics, healthcare organizations can streamline processes, personalize treatment, and improve outcomes. This research paper explores the application of AIdriven data science in optimizing patient care pathways, examines current strategies in program management, and provides recommendations for implementing these technologies to achieve more effective and efficient patient care.

One of the major contributors to the total operating costs of domestic nuclear fleet of reactors today is the operation and maintenance (O&M) costs. These include labor-intense preventive maintenance programs involving manually-performed inspection, calibration, testing, and maintenance of plant assets at periodic frequency and time-based replacement of assets at periodic frequency, irrespective of their conditions. This has resulted in a labor-centric business model to achieve high capacity factors. To build an optimal maintenance program, it’s time to transition from this labor-centric business model to a technology-centric business model. Fortunately, there are technologies (advanced sensor, data analytics, and risk assessment methodologies) that will support this transition. The technology-centric business model will result in significant plant life extension and reduction of time-based maintenance activities. This will drive down O&M costs as labor is a rising cost and technology is a declining cost. This approach will lay the foundation for real-time condition assessment of plant assets, allowing condition-based maintenance to enhance plant safety, reliability, and economics of operation. The goal of this project is to address challenges in the area of digital monitoring, i.e., the application of advanced sensor technologies (particularly wireless sensor technologies) and science-based data analytic capabilities to advance online monitoring and predictive maintenance in nuclear plants to improve plant performance (efficiency gain and economic competitiveness). To achieve the project goal, in partnership with Exelon Generating Company (Exelon), researchers from Idaho National Laboratory (INL) and Oak Ridge National Laboratory (ORNL) are performing research and development (R&D) to demonstrate application of wireless sensors using the distributed antenna system and advanced data analytics to achieve predictive maintenance. In the report, wireless vibration sensors, vibration data and its indicator are described. The wireless vibration sensors presented in this report support three types of wireless communication, namely, Wi-Fi, cellular, and 900 MHz. These wireless vibration sensors are considered by partner plant site for installation on plant asset to enable online vibration monitoring to replace periodic measurements. These vibration data along with other plant process data will be utilized to develop diagnostic and prognostic models.

Data science and analytics in aviation

Numerous statutes and common law doctrines conceive of a dividing line between work time and nonwork time and delineate the activities that must be compensated as work. While technological innovations and increasing desires for workplace flexibility have begun to erode this divide, it persists, in part, because of the ways in which the division protects employers and employees alike. Nonetheless, the explosion of data analytics programs that allow employers to monitor and rely upon a worker's off‐duty conduct will soon weaken the dividing line between work and nonwork in dramatically greater and more troubling ways than ever before. The emergence of programs allowing employers to track, predict, rely upon, and possibly control nonwork activities, views, preferences, and emotions represents a major blurring of the line between work and nonwork. This article contends that these advances in data analytics suggest a need to reexamine the notion of work versus nonwork time and to question whether existing protections adequately consider a world in which these lines are so significantly muddled. As a society, we need to acknowledge the implications of the availability of massive quantities of employees’ off‐duty data and to decide whether and how to regulate its use by employers. Whether we, as a society, decide to allow market forces to dictate acceptable employer behavior, choose to regulate and restrict the use of off‐duty data for adverse employment decisions, or find some middle ground that requires disclosure and consent, we should choose our own course rather than allowing the technology to be the guide.

Technological advances in data analytics, robotic process automation (RPA), and artificial intelligence (AI) are transforming businesses and redefining what skill sets are needed in the accounting profession. To prepare students, academic accounting departments must develop strategies to integrate data analytics and emerging technologies into the curriculum. This paper discusses the design process used in a graduate-level advanced data analytics course that incorporates RPA and AI at a public university in the northeastern region of the United States. The paper concludes with a statistical analysis of precourse/postcourse student reflections and feedback surveys as part of a quality assurance initiative. The results suggest strong evidence of student learning related to the course learning objectives.

The collection of high-quality data provides a key competitive advantage to companies in their decision-making process. It helps to understand customer behavior and enables the usage and deployment of new technologies based on machine learning. However, the process from collecting the data, to clean and process it to be used by data scientists and applications is often manual, non-optimized and error-prone. This increases the time that the data takes to deliver value for the business. To reduce this time companies are looking into automation and validation of the data processes. Data processes are the operational side of data analytic workflow.DataOps, a recently coined term by data scientists, data analysts and data engineers refer to a general process aimed to shorten the end-to-end data analytic life-cycle time by introducing automation in the data collection, validation, and verification process. Despite its increasing popularity among practitioners, research on this topic has been limited and does not provide a clear definition for the term or how a data analytic process evolves from ad-hoc data collection to fully automated data analytics as envisioned by DataOps.This research provides three main contributions. First, utilizing multi-vocal literature we provide a definition and a scope for the general process referred to as DataOps. Second, based on a case study with a large mobile telecommunication organization, we analyze how multiple data analytic teams evolve their infrastructure and processes towards DataOps. Also, we provide a stairway showing the different stages of the evolution process. With this evolution model, companies can identify the stage which they belong to and also, can try to move to the next stage by overcoming the challenges they encounter in the current stage.

Bridging systems theory and data science: A unifying review of dynamic latent variable analytics and process monitoring

A Performance Indicator Ontology for Predictive Maintenance in Smart Manufacturing