How We Became Instrumentalists (Again)

There is an increasing demand for data scientists in the current job market. Hence, many two-year and four-year colleges and universities started to offer Data Science degrees in the recent decade. In this paper, we describe an undergraduate Data Science curriculum that focuses on computational skills and mathematical foundations, with inclusion of a domain in business analytics. We expect this paper to be used by institutions as a guideline while planning their Data Science undergraduate degree. We reviewed around 100 undergraduate Data Science programs in the U.S. and summarized their common approaches and we also reviewed several Data Science curriculum development guidelines. Then, we developed our interdisciplinary undergraduate Data Science program that consists of (1) mathematics and statistics foundation courses covering discrete mathematics, linear algebra, introductory statistics, analysis of variance, and regression, (2) computer science foundation courses covering two programming languages (namely Python and Java), data structures, and database management, (3) core data science courses covering data science and visualization, statistical machine learning, data mining, and machine learning, and finally (4) courses from the business domain covering business intelligence analytics and predictive analytics. At the end of the degree program, we include a choice among a senior capstone course, a statistical consulting course, or an internship. We also discuss the collaboration between departments and colleges for this program.

Abstract: Data analysis, data mining, predictive analytics, machine learning, data science, and artificial intelligence have affected how for-profit organizations make decisions for many years now, and even decades, for some methods. The nonprofit industry, specifically nonprofit fundraising, has been trying to catch up. This article lists various applications of analytics in nonprofit fundraising as found in the literature. I use the term “analytics” to capture the various methods used in data mining, predictive analytics, machine learning, data science, and artificial intelligence. This article is structured as follows: a brief review of analytics, followed by a review of the literature by decades, and then a summary of the findings and predictions on future work.

The rapid advancement of technology, particularly artificial intelligence (AI), is transforming the world of management. This chapter delves into the futuristic trends in management, focusing on the integration of AI into various managerial aspects, its potential benefits, challenges, and strategies for successful adoption. AI-driven decision-making aids managers in data-driven processes, identifying patterns, trends, and insights through AI algorithms. AI's impact on human resources management includes AI-enabled talent acquisition and recruitment, enhanced employee experience through AI-driven content, and AI-driven operations management. AI-driven supply chain optimization, process automation, and customer relationship management (CRM) are also explored. However, ethical implications of AI in management include addressing biases and fairness concerns, ensuring transparency and accountability, and navigating privacy and data security challenges. Managing the human-AI collaboration involves building a culture that embraces AI while valuing human expertise, fostering a learning mindset, encouraging continuous skill development, and mitigating potential job displacement and promoting AI-human synergy. The chapter emphasizes the importance of fostering a learning mindset, encouraging continuous skill development, and mitigating potential job displacement. AI's integration into management practices has the potential to revolutionize various aspects of organizations, including data management, resource allocation, personalization, risk assessment, supply chain management, and employee productivity. AI-driven tools enable efficient data management, identification of trends, correlations, and actionable insights from complex datasets. They can optimize financial resources, human capital, or physical assets, enhance operational efficiency, and provide personalized customer experiences. AI-driven decision-making aids managers in making informed decisions by leveraging capabilities such as data processing, pattern recognition, real-time insights, and predictive analytics. These capabilities help managers segment customers, analyze market trends, and predict future demand. AI also enhances predictive and prescriptive analytics by providing recommendations for optimal performance. AI's impact on human resources management includes AI-enabled talent acquisition and recruitment. AI-powered tools can streamline the traditional recruitment process by scanning online platforms, screening resumes, and conducting assessments and skill evaluations. AI helps mitigate bias in the hiring process through blind hiring, objective evaluation, and data-driven decisions. However, challenges and ethical considerations include data privacy and security, transparency and explainability, algorithmic bias, and candidate experience. In conclusion, AI's integration into management practices has the potential to revolutionize various areas, including data management, resource allocation, personalization, risk assessment, supply chain management, and employee performance. However, HR professionals must address ethical concerns such as data privacy, transparency, algorithmic bias, and candidate experience to ensure the success and competitiveness of AI-based recruitment. AI can significantly enhance the employee experience by providing personalized learning and development programs, enhancing performance evaluations, building employee engagement strategies, and predicting potential attrition risks. AI-powered tools can assess employees' existing skills, knowledge gaps, and learning preferences, enabling the creation of personalized development plans. AI-driven learning platforms can adjust the difficulty and content of training materials based on individual progress, fostering a culture of continuous learning. AI-based performance evaluations can provide valuable insights to enhance the performance evaluation process, offering real-time tracking and 360-degree feedback analysis. AI-driven employee engagement strategies can bolster employee satisfaction, tailoring benefits, rewards, and recognition programs to individual needs. Predictive attrition analysis and chatbots for employee support can also help retain valuable employees. AI can also optimize supply chain operations by accurately predicting demand and managing inventory. AI-driven systems can dynamically adjust inventory levels based on real-time demand fluctuations and lead times, maintaining optimal resource utilization and minimizing excess inventory. AI-powered logistics and route optimization can revolutionize logistics and transportation management by optimizing routes and enhancing overall efficiency. AI-driven process automation can transform various business operations by identifying suitable processes, analyzing high-volume data processing, streamlining workflow and resource allocation, and addressing workforce concerns and upskilling needs amid automation. By embracing AI in operations management, businesses can achieve unprecedented levels of efficiency, resilience, and responsiveness to market demands. In conclusion, AI can transform the employee experience, drive productivity, and foster a dynamic workforce. However, careful consideration of ethical principles and continuous monitoring of AI systems are essential for responsible implementation. AI can significantly improve customer satisfaction and loyalty through real-time feedback analysis, chat sentiment analysis, and personalized loyalty programs. AI-powered customer support includes chatbots and virtual assistants, which provide instant and round-the-clock assistance. AI platforms, such as Natural Language Processing (NLP), enable chatbots to understand and respond to customer queries in a conversational manner. However, AI integration also presents challenges, such as addressing biases and fairness concerns in AI algorithms, ensuring transparency and accountability in AI systems, and managing privacy and data security challenges. Adhering to data protection regulations and implementing robust data governance frameworks are essential for safeguarding customer and organizational data. Managing the human-AI collaboration involves building a culture that embraces AI while valuing human expertise. This can be achieved through change management and training, promoting a collaborative environment, fostering a learning mindset, upskilling and reskilling initiatives, emphasizing creativity and critical thinking, and mitigating potential job displacement and promoting AI-human synergy. In conclusion, embracing AI in management offers numerous opportunities to enhance organizational efficiency, productivity, and competitiveness. It is crucial for management professionals and corporate leaders to understand and harness the power of AI responsibly. By being proactive in addressing challenges and aligning AI initiatives with organizational values, businesses can leverage futuristic trends in management to thrive in the dynamic and ever-evolving landscape.

Artificial intelligence as an emerging diagnostic approach in paediatric pulmonology.

The rapid evolution of cyber threats and the exponential growth of data-driven applications have necessitated the advancement of predictive analytics techniques in cybersecurity and data science. Machine learning (ML) and deep learning (DL) have emerged as powerful tools for detecting, analyzing, and mitigating cyber threats while also enhancing decision-making processes in data science applications. This paper explores state-of-the-art ML and DL methodologies for predictive analytics, emphasizing their role in proactive security measures and intelligent data analysis. Traditional security approaches often struggle to keep pace with the increasing complexity and volume of cyber threats. The integration of ML and DL offers dynamic, adaptive, and automated solutions that can identify anomalies, predict potential attacks, and strengthen defensive mechanisms. Supervised, unsupervised, and reinforcement learning models have been widely adopted for various cybersecurity applications, including intrusion detection, malware classification, fraud detection, and threat intelligence. Meanwhile, DL architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers have demonstrated superior performance in feature extraction and pattern recognition, enabling advanced predictive analytics in cybersecurity. Beyond security applications, ML and DL play a crucial role in data science, enabling predictive modeling across diverse industries, such as healthcare, finance, and smart cities. Predictive analytics in data science leverages vast datasets to forecast trends, optimize decision-making, and drive innovation. However, challenges such as data privacy, model interpretability, adversarial attacks, and computational complexity must be addressed to ensure the reliability and ethical deployment of AI-driven solutions. This study presents a comprehensive review of the latest advancements in ML and DL for predictive analytics, examining their applications, benefits, and limitations. It also explores hybrid approaches that combine multiple techniques for enhanced accuracy and robustness. The paper further discusses emerging trends, including federated learning for privacy-preserving analytics, explainable AI (XAI) for model transparency, and quantum-enhanced ML for accelerated computations. Through extensive analysis and comparative evaluation, this research highlights the transformative potential of ML and DL in securing digital infrastructures and optimizing predictive analytics. The findings underscore the need for continuous innovation in algorithm design, data handling strategies, and cybersecurity frameworks to counter evolving cyber threats and maximize the utility of AI-driven predictive models. Ultimately, this study contributes to advancing the intersection of ML, DL, cybersecurity, and data science, paving the way for resilient, intelligent, and efficient digital ecosystems.

Data mining is an emerging field gaining acceptance in research and industry. This is evidenced by an increasing number of research publications, conferences, journals and industry initiatives focused in this field in the recent past. Data mining aims to solve an intricate problem faced by a number of application domains today with the deluge of data that exists and is continually collected, typically, in large electronic databases. That is, to extract useful, meaningful knowledge from these vast data sets. Human analytical capabilities are limited, especially in its ability to analyse large and complex data sets. Data mining provides a number of tools and techniques that enables analysis of such data sets. Data mining incorporates techniques from a number of fields including statistics, machine learning, database management, artificial intelligence, pattern recognition, and data visualisation. A number of definitions for data mining are presented in literature. Some of them are listed below: • “Data mining is the process of discovering meaningful new correlations, patterns and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques” (Gartner Group, 1995). • “Data mining is the analysis of (often large) observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner” (Hand et al., 2001). • “Data mining is an interdisciplinary field bringing together techniques from machine learning, pattern recognition, statistics, databases, and visualization to address the issue of information extraction from large data bases” (Cabena et al., 1998). • “The extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data” (Han & Kamber, 2001). We present application of data mining (also known as “Data Mining Applications”) as an “experiment” carried out using data mining techniques that result in gaining useful knowledge and insights pertaining to the application domain. Figure 1 below depicts this process.

Assessing perspectives on artificial intelligence applications to gastroenterology

Predictive analytics, driven by artificial intelligence (AI), is revolutionizing the understanding and forecasting of market trends, particularly in the realm of consumer behavior. This study explores the application of AIpowered predictive analytics to anticipate market dynamics and consumer preferences, offering insights that enable businesses to make informed strategic decisions. By leveraging vast datasets, AI algorithms analyze historical data, detect patterns, and predict future trends with remarkable accuracy. This capability is especially pertinent in today's fastpaced market environment, where consumer behavior is increasingly influenced by diverse factors ranging from economic conditions to social media trends. The study examines various AI techniques such as machine learning, natural language processing, and deep learning, highlighting their roles in enhancing predictive accuracy. Machine learning algorithms, for instance, can process complex and largescale data to uncover hidden correlations and forecast consumer demand. Natural language processing enables the analysis of textual data from social media, reviews, and other sources, providing a deeper understanding of consumer sentiments and emerging trends. Deep learning models, with their advanced neural networks, further refine predictions by learning intricate patterns in data. Several case studies are presented to illustrate the practical applications and benefits of AI in predictive analytics. For example, retail companies utilize AI to predict inventory needs and optimize stock levels, thereby reducing costs and improving customer satisfaction. Similarly, the study discusses how ecommerce platforms analyze browsing and purchasing patterns to personalize recommendations, enhancing user engagement and boosting sales. However, the implementation of AIdriven predictive analytics also presents challenges. Data quality and integration, privacy concerns, and the need for specialized skills in data science and AI are significant hurdles that businesses must overcome. The study emphasizes the importance of addressing these challenges to fully harness the potential of AI in predictive analytics. In conclusion, predictive analytics using AI offers transformative capabilities for understanding and forecasting market trends. By providing precise and actionable insights into consumer behavior, it enables businesses to stay ahead of the competition and cater effectively to evolving market demands. The study underscores the need for continued research and development to further enhance the accuracy and applicability of AIdriven predictive analytics in diverse market contexts.

Introduction Data mining is the process of discovering useful and previously unknown information and relationships in large data sets (Campos, Stengard, & Milenova, 2005; Tan, Steinbach, & Kumar, 2006). Accordingly, data mining is the purposeful use of information technology to implement algorithms from machine learning, statistics, and artificial intelligence to analyze large data sets for the purpose of decision support. The field of data mining grew out of limitations in standard data analysis techniques (Tan et al., 2006). Advancements in machine learning, pattern recognition, and artificial intelligence algorithms coupled with computing trends (CPU power, massive storage devices, high-speed connectivity, and software academic initiatives from companies like Microsoft, Oracle, and IBM) enabled universities to bring data mining courses into their curricula (Jafar, Anderson, & Abdullat, 2008b). Accordingly, Computer Science and Information Systems programs have been aggressively introducing data mining courses into their curricula (Goharian, Grossman, & Raju, 2004; Jafar, Anderson, & Abdullat 2008a; Lenox & Cuff, 2002; Saquer, 2007). Computer Science programs focus on the deep understanding of the mathematical aspects of data mining algorithms and their efficient implementation. They require advanced programming and data structures as prerequisites for their courses (Goharian et al., 2004; Musicant, 2006; Rahal, 2008). Information Systems programs on the other hand, focus on the data analysis and business intelligence aspects of data mining. Students learn the theory of data mining algorithms and their applications. Then they use tools that implement the algorithms to build mining models to analyze data for the purpose of decision support. Accordingly, a first course in programming, a database management course, and a statistical data analysis course suffice as prerequisites. For Information Systems programs, a data centric, algorithm understanding and process-automation approach to data mining similar to Jafar et al. (2008a) and Campos et al. (2005) is more appropriate. A data mining course in an Information Systems program has an (1) analytical component, (2) a tools-based, hands-on component ,and (3) a rich collection of data sets. (1) The analytical component covers the theory and practice of the lifecycle of a data mining analysis project, elementary data analysis, market basket analysis, classification and prediction (decision trees, neural networks, naive Bayes, logistic regression, etc.), cluster analysis and category detection, testing and validation of mining models, and finally the application of mining models for decision support and prediction. Textbooks from Han and Kamber (2006) and Tan et al. (2006) provide a comprehensive coverage of the terminology, theory, and algorithms of data mining. (2) The hands-on component requires the use of tools to build projects based on the algorithms learned in the analytical component. We chose Microsoft Excel with its data mining add-in(s) as the front-end and Microsoft's Cloud Computing and SQL Server 2008 data mining computing engines as the back-end. Microsoft Excel is ubiquitous. It is a natural front-end for elementary data analysis and presentation of data. Its data mining add-in(s) are available as a free download. The add-in(s) are automatically configured to send data to Microsoft's Cloud Computing engine server. The server performs the necessary analysis and receives analysis results back into Excel to present them in tabulated and chart formats. Using wizards, the add-in(s) are easily configured to connect to a SQL Server 2008 running analysis services to send data and receive analysis results back into Excel for presentation. The add-in(s) provide a rich wizard-based, uniform graphical user interface to manage the data, the data mining models, the configurations, and the pre and post view of data and mining models. …

Covering the spectrum of machine learning frameworks and their applications in predictive analytics, this chapter provides insights into how AI and Python are used to forecast trends and behaviors within vast datasets. It reviews various machine learning algorithms, their implementation in Python, and the role of cloud computing and AI in enhancing predictive analytics. Case studies on intelligent networks and industry-specific applications highlight the practical implications of these technologies in real-world scenarios.This chapter from "Intelligent Data: Revolutionizing Analytics with AI and Python" delves into the application of machine learning (ML) frameworks in predictive analytics, emphasizing the synergy between artificial intelligence (AI), Python, and cloud computing to process and forecast complex data trends. It explores a variety of ML algorithms, their implementation in Python, and the augmentation of their capabilities via cloud computing for scalable predictive analytics. Through comparative analysis, conceptual framework development, and practical case studies, the chapter showcases the practical use of AI and ML in predicting trends and behaviors in vast datasets across various industries. It reviews key tools such as Scikit-learn and Apache Spark, alongside Python's visualization libraries, to demonstrate the advancements and applications of ML frameworks in real-world scenarios. The analysis provides insights into the integration of AI and Python in data science, highlighting the technological evolution and its impact on data analytics. Keywords:Predictive Analytics,Machine Learning Frameworks,Artificial Intelligence,Python Programming,Cloud Computing,Data Analysis,Scikit-learn Apache Spark,Visual Analytics,Comparative Analysis,Conceptual Framework and Real-world Applications.

Guest Editorial Special Issue on Mobile Biometrics

Predictive analytics embraces an extensive range of techniques including statistical modeling, machine learning, and data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline. Primary uses have been in data cleaning, exploratory analysis, and diagnostics. For example, scatterplots and bar charts are used to illustrate class distributions and responses. More recently, extensive visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent‐specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end‐users to understand and engage with the modeling process. In this state‐of‐the‐art report, we catalogue recent advances in the visualization community for supporting predictive analytics. First, we define the scope of predictive analytics discussed in this article and describe how visual analytics can support predictive analytics tasks in a predictive visual analytics (PVA) pipeline. We then survey the literature and categorize the research with respect to the proposed PVA pipeline. Systems and techniques are evaluated in terms of their supported interactions, and interactions specific to predictive analytics are discussed. We end this report with a discussion of challenges and opportunities for future research in predictive visual analytics.

Data science is concerned with the extraction of knowledge and insight, and ultimately societal or economic value, from data. It complements traditional statistics in that its object is data as it presents itself in the wild (often complex and heterogeneous, noisy, loosely structured, biased, etc.), rather than well-structured data sampled in carefully designed studies. It also has a strong computer science focus, and is related to popular areas such as big data, machine learning, data mining and knowledge discovery. Data science is becoming increasingly important with the abundance of big data, while the number of skilled data scientists is lagging. This has raised the question as to whether it is possible to automate data science in several contexts. First, from an artificial intelligence perspective, it is interesting to investigate whether (data) science (or portions of it) can be automated, as it is an activity currently requiring high levels of human expertise. Second, the field of machine learning has a long-standing interest in applying machine learning at the meta-level, in order to obtain better machine learning algorithms, yielding recent successes in automated parameter tuning, algorithm configuration and algorithm selection. Third, there is an interest in automating not only the model building process itself (cf. the Automated Statistician) but also in automating the preprocessing steps (data wrangling). This Dagstuhl seminar brought together researchers from all areas concerned with data science in order to study whether, to what extent, and how data science can be automated.

On behalf of the organizing committee, it is our great pleasure to welcome you to the historic city of London for the 24th ACM Conference on Knowledge Discovery and Data Mining - KDD 2018. These are very exciting times for our community. The terms "Data Science", "Artificial Intelligence", "Machine Learning", "Data Mining" and "Big Data" have, in the last few years, grown out of research labs and gained presence in the media and in everyday conversations. We hear these terms on social media and from decision makers at various level, both in governments and corporations. The impact of these technologies is felt in almost every walk of life with novel applications in self driving cars, AI assistants and in the discovery of new cures. Importantly, the current rapid progress in data science is facilitated by the timely sharing of newly discovered approaches across research and industry. It is the hallmark of KDD conferences in the past that they have been the bridge between theory and practice, a great facilitator and catalyst for this exchange. Researchers and practitioners meet and interact in person over several days. Our program, with its keynotes and interactive tutorials, is designed to bring these two groups together. It is also a very exciting time for London, which has recently been named as the "the AI capital of Europe". We could have chosen no better place to host this year's conference. London is home to more than 750 AI companies, operating in more than 30 industrial sectors, with almost half of these enterprises having a non-UK founder, and about a third with founders from a minority background. It is also home to many world-leading academic institutions and research centers. This confirms London's international and open nature as a leading hub for innovation and technology. The conference this year continues with its tradition of a strong engaging and hands-on program including a full day of tutorials on Sunday and plenty of cutting edge workshops on Monday. The final three days are devoted to peer reviewed contributed technical papers, describing both novel, important research contributions, and applied, innovative solutions. Four stellar keynote talks, by British Academy Fellow David Hand, Nobel Laureate Alvin E. Roth, Columbia Univ. Data Science Director Jeannette M. Wing and Oxford University Professor Yee Whye Teh, will touch on some of the important, emerging issues in the field of data mining. With a growing industry around AI, our KDD Panel brings together experts to spawn discussions and exchange ideas about how AI can be used for social good. We have an outstanding lineup of industry speakers sharing their experiences and expertise in deploying industrial data mining solutions. Thanks to a strong hands-on tutorial program, participants will learn how to use practical data science tools. KDD 2018 puts a strong emphasis on AI development with mainstream applications featured by KDD Cup of Fresh Air with 4173 teams around the globe participating in a challenge to predict air quality in cities like London and Beijing; a unique Deep Learning day, with world class research leaders addressing the frontiers in deep learning research and applications; and a Global AI Initiatives Session where major government initiatives in AI will be presented by representatives from various countries including UK, USA, China etc. We hope that the content and the professional networking opportunities at KDD 2018 will help you to succeed professionally, identify new technology trends, learn from contributed papers, presentations, and posters, discover new tools, processes and practices, identify new job opportunities and hire new team members. KDD 2018 awarded a record USD 145k for student travel and set aside USD 25k to enable smaller startups to attend. Of particular interest is our "Social Impact" program, which has been an integral part of KDD for years. Its work to highlight the impact of data science on projects of broad social relevance included relevant scientific papers as well as the development of programs such as data science for social good and projects that help NGO's and administrations to use data science to enhance life quality. As part of the Impact Awards program, 7 proposals for projects that bring together academia and social partners from different parts of the world, have been awarded a one-year grant, renewable based on their impact, scale and promise. We specially encourage the participation of underrepresented and resource-constrained parts of society so that the benefits of technologies are shared and available more broadly. We are therefore confident that KDD 2018 will be a wonderful place for researchers, practitioners, funding agencies and investors willing to create new algorithmic solutions and maximize their economic and societal impact.

Data analytics is used to make the decision by analyzing the raw data. Data analytics is categorized into four categories: descriptive, diagnostic, prescriptive, and predictive analytics. Predictive analysis is the most powerful tool for data analytics and involves advanced statistical, modeling, data mining, artificial intelligence, deep learning to dig into data, and allows analytics to make predictions. Predictive analysis can be classified into two types, namely regression analysis and classification. When the dependent variables in the form of binary, logistic regression is the best method to perform. Regression analysis is an effective mathematical tool in which the association between two or more elements of interest may be analyzed by the researcher. An efficient data classification mechanism is also valuable because it can help the system decide the required degree of control in order to protect the security and credibility of the data. To achieve the statistics for predictive analysis, machine learning acts as the core principle for it. Machine learning is used to help a computer to interpret information, comprehend connections, and use observations to solve problems and/or enrich information. Predictive analytics extensively used machine learning for data modeling due to its ability to accurately process a vast amount of data and recognize patterns.