Modern Data Science with R

This chapter explores the similarities and differences between classical statistics, mathematical analysis, and modern data science in educational leadership and management. It defines classical statistics, covering descriptive statistics, regression, hypothesis testing, time series, and clustering while emphasizing mathematical analysis as a tool for refining models, optimizing solutions, and enhancing interpretability. The chapter examines data science practices, assessing their integration into quantitative analysis. It discusses the philosophical foundations of quantitative analysis, the principles behind inference, and technological advances in data science. Finally, it evaluates data science's role in management, emphasizing AI, big data, and multidisciplinary approaches. Mathematical analysis enhances algorithm stability, predictive modeling, and decision-making, reinforcing the synergy between these fields. Their integration fosters sophisticated and effective analyses in education and management.

The power of randomization in the computational context is one of the fundamental discoveries of computer science; furthermore, statistical and probabilistic reasoning is indispensable for modern-day data analysis and data science. In this significantly-expanded second edition, the authors (continuing to assume exposure to just basic discrete probability) present a unified treatment of probabilistic concepts useful for modern computer science and data science.

<p><b>In today’s finance industry, data-driven decision-making is essential. <i>Financial Data Science with Python: An Integrated Approach to Analysis, Modeling, and Machine Learning</i> bridges the gap between traditional finance and modern data science, offering a comprehensive guide for students, analysts, and professionals.</b></p><p>This book equips readers with the tools to analyze complex financial data, build predictive models, and apply machine learning techniques to real-world financial challenges.</p><p>Beginning with foundational Python concepts, the author covers essential topics like data structures, object-oriented programming, and key libraries such as NumPy and Pandas. The book advances into more complex areas, including financial data processing, time series analysis with ARIMA and GARCH models, and both supervised and unsupervised machine learning methods tailored to finance. Practical techniques like regression, classification, and clustering are explored in a financial context.</p><p>A key feature is the hands-on approach. Through real-world examples, projects, and exercises, readers will apply Python to tasks like risk assessment, market forecasting, and financial pattern recognition. All code examples are provided in Jupyter Notebooks, enhancing interactivity.</p><p>Whether you’re a student building foundational skills, a financial analyst enhancing technical expertise, or a professional staying competitive in a data-driven industry, this book offers the knowledge and tools to succeed in financial data science.</p>

This lightning talk presents early work surrounding undergraduate students' experiences in data science and computing classrooms that shape their identities. Identity in learning spaces has been studied extensively in STEM disciplines, but little research has focused on undergraduate students majoring in data science. Since data science sits at the intersection of several disciplines, such as mathematics, statistics, and computer science, it is unclear how data science students navigate those individual discipline identities and/or construct a unique identity that is greater than the sum of those parts. As Michigan State University's Computational Mathematics, Science, and Engineering department offers data science degrees, it is of particular interest to understand the classroom messaging and experiences that shape our students' sense of self in relation to data science and computing. The project will build on various identity frameworks understand how students' data science identities are developed or stifled through classroom experiences. From previous research, we know that prototypical identities of STEM center cisgender, heterosexual, white male students and can exclude students from different demographics and lived experiences. To address this, I will utilize a critical lens to interrogate possible inequity, bias, and inaccessibility that may emerge from students' identity shaping experiences in these classrooms. At the time of this lightning talk, a thematic analysis of the first seven interviews I have conducted will be presented. All are welcome to attend the presentation and provide feedback and insight on initial observations, synthesis, and plans for the future.

The field of dynamical systems is being transformed by the mathematical tools and algorithms emerging from modern computing and data science. First-principles derivations and asymptotic reductions are giving way to data-driven approaches that formulate models in operator-theoretic or probabilistic frameworks. Koopman spectral theory has emerged as a dominant perspective over the past decade, in which nonlinear dynamics are represented in terms of an infinite-dimensional linear operator acting on the space of all possible measurement functions of the system. This linear representation of nonlinear dynamics has tremendous potential to enable the prediction, estimation, and control of nonlinear systems with standard textbook methods developed for linear systems. However, obtaining finite-dimensional coordinate systems and embeddings in which the dynamics appear approximately linear remains a central open challenge. The success of Koopman analysis is due primarily to three key factors: (1) there exists rigorous theory connecting it to classical geometric approaches for dynamical systems; (2) the approach is formulated in terms of measurements, making it ideal for leveraging big data and machine learning techniques; and (3) simple, yet powerful numerical algorithms, such as the dynamic mode decomposition (DMD), have been developed and extended to reduce Koopman theory to practice in real-world applications. In this review, we provide an overview of modern Koopman operator theory, describing recent theoretical and algorithmic developments and highlighting these methods with a diverse range of applications. We also discuss key advances and challenges in the rapidly growing field of machine learning that are likely to drive future developments and significantly transform the theoretical landscape of dynamical systems.

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contribution:</i> This article presents evidence that electrical engineering, computer science, and data science students, participating in introduction to machine learning (ML) courses, fail to interpret the performance of ML algorithms correctly, since they fail to consider the application domain. This phenomenon is referred to as the domain neglect bias and is discussed in this article from the cognitive psychology perspective. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Background:</i> ML algorithms are common components in electrical engineering, computer science, and data science curricula. Learning ML is challenging not only due to its complex mathematical and algorithmic aspects, but also due to: 1) the complexity of using these algorithms in the context of real-life situations correctly and 2) the comprehension of related social and ethical issues. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Research Question:</i> To what extent do students fail to correctly interpret the performance of ML algorithms due to the domain neglect bias and how can this bias be explained? <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Methodology:</i> A questionnaire containing two questions was designed, in which the participants were asked to compare the performance of ML algorithms in the contexts of different application domains. The questions were designed to trigger the domain neglect bias which in turn, allowed quantification of its prevalence. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Findings:</i> One third of the students failed to recognize the outperforming ML algorithm due to the domain neglect bias. Educators should note this bias and develop new pedagogical methods for teaching ML performance in order to mitigate the bias.

Scientific research processes are accompanied by burgeoning amounts of research data that serves as findings of the research and provides a platform for the results to be verifiable. Scientific researchers are under pressure to make their data openly available to other researchers for use. Access to scientific data and its curation has become a dominant theme in scientific policy discussions. Besides the funder requirements, researchers are making their data openly available for a number of reasons that include, raising the profiles of their research outputs, to increase their citations, to validate their research outputs and for their authenticity and integrity as researchers. However, not all researchers possess the skills to curate their own data and make it openly available for use in an open environment that is FAIR (Findable, Accessible, Interoperable and Reusable). The primary objective of this paper is to raise awareness of the strategic role of data scientists in the scientific research process. The study seeks to contribute towards a growing and emergent discourse of data science in HEIs. It also seeks to contribute towards an educational model for data scientists in HEIs in South Africa. In-order to realise these objectives the paper asks the following research questions:How are the emergent roles of data scientists conceptualised in HEIs in South Africa?What is the strategic role of a data scientist in the scientific research process?.This study analysed data science programs offered by universities in South Africa to understand how the role of data scientists is conceptualised and to delineate the scope of this emergent field. A qualitative content analysis survey method is used to analyse courses offered by HEIs in South Africa. Preliminary findings from the research revealed that data scientists are drawn from fields like mathematics, statistics, information science, and computer science. The practical jobs of data scientists as conceptualised by the majority of universities employs theories drawn from the information science, computer science and statistics. The data scientists are expected to work closely with the researchers where the research is done. Most universities are offering data science programs at post-graduate level as professional courses for learners coming in from diverse backgrounds. The study recommends that the next generation of data scientists in a South African context should possess a cocktail of skills that enable them to conduct data analysis and enquiry to enable others to work with digital data. Presentation at the UCT E-Research Conference 2019: DataScience4D: Explore the practice, principles and opportunities of data science for development15 – 18 April 2019 held at the University of Cape Town

The 2nd International Conference on Computing and Data Science (CONF-CDS 2021) was held in Stanford, CA, USA, on January 21-23, 2021. CONF-CDS 2021 is a leading conference on computing technology, machine learning, computer science and data science hosted by Eliwise Academy. CONF-CDS 2021 is open to international participants. It provides an excellent opportunity for scholars interested in computing and data science to share their findings and achievements, thus promoting communication and cooperation.The goal of this conference is to bring together researchers and practitioners from academia and industry to highlight the importance of computing technologies and data science as well as establish new collaborations in these areas. The conference looks for significant contributions to computing, data mining and data science in theoretical and practical aspects.The submission will first be reviewed for its topic and length, then go through an originality check. The peer-review process will begin soon after the paper is found to be qualified. The paper will be sent to have a double-blind peer review by 3 reviewers. They will judge the paper based on the theme, coverage, innovation, integrity, depth, and language. If the paper is accepted, notification of acceptance and review comments will be sent to the author via email. The actual review time may be different under certain situations.Due to the prevention and control of COVID-19 and travel restrictions worldwide, CONF-CDS 2021 adopted the format of virtual conference through network technology to provide a forum for experts and scholars to share real-time information and discuss. The conference included keynote speeches and online discussion, and each presenter has 15-25 minutes (including Q&A).With warmest regards,CONF-CDS 2021 Organizing CommitteeStanford, CA, USA

Data science is a discipline that provides principles, methodology, and guidelines for the analysis of data for tools, values, or insights. Driven by a huge workforce demand, many academic institutions have started to offer degrees in data science, with many at the graduate, and a few at the undergraduate level. Curricula may differ at different institutions, because of varying levels of faculty expertise, and different disciplines (such as mathematics, computer science, and business) in developing the curriculum. The University of Massachusetts Dartmouth started offering degree programs in data science from Fall 2015, at both the undergraduate and the graduate level. Quite a few articles have been published that deal with graduate data science courses, much less so dealing with undergraduate ones. Our discussion will focus on undergraduate course structure and function, and specifically, a first course in data science. Our design of this course centers around a concept called the data science life cycle. That is, we view tasks or steps in the practice of data science as forming a process, consisting of states that indicate how it comes into life, how different tasks in data science depend on or interact with others until the birth of a data product or a conclusion. Naturally, different pieces of the data science life cycle then form individual parts of the course. Details of each piece are filled up by concepts, techniques, or skills that are popular in industry. Consequently, the design of our course is both “principled” and practical. A significant feature of our course philosophy is that, in line with activity theory, the course is based on the use of tools to transform real data to answer strongly motivated questions related to the data.

We are excited to announce the twelve recipients of the ACM SIGHPC Computational and Data Science Fellowships for 2020. The program is intended to increase the diversity of students pursuing graduate degrees in data science and computational science, including women as well as students from racial and ethnic backgrounds that have been historically underrepresented in the computing field. The fellowship provides $15,000 annually for study anywhere in the world.

This article describes a collaborative project across multiple institutions to compare two versions of a regression activity: (a) a website version that collected student data through an online simulation with corresponding apps for visualization and (b) a traditional paper handout version that had a static dataset. This allowed us to evaluate the differences in student learning and attitudes when students have a stronger connection with the data through an interactive—and hopefully fun—data-collection process than they do when they are given a worksheet with a pre-collected dataset. While student and instructor responses indicated they liked the activity in both formats, the PreTest and PostTest results showed evidence of a higher gain in knowledge with the website version. Finally, we discuss how the website version connects with pedagogical strategies for the development of modern statistics and data science courses.

This chapter presents a brief overview of the most recent optimization algorithms. The chapter starts by showing the difference between classical methods and modern methods. And then, some special types of problems solved by optimization algorithms are presented, including some open problems. After that, the initial modern optimization algorithms are listed; the recent optimization algorithms are shown with their main features. This section starts with the algorithms inspired by quantum mechanics and their solutions to intelligent systems. Also, this section shows other modern approaches merging intelligent techniques, neuroscience, data science, and machine learning.

Copolymerization is a powerful tool for tuning the properties of synthetic polymers. In chain-growth polymerization, copolymer microstructure is controlled by the relative reactivity of comonomers, quantified by copolymer reactivity ratios. Yet, the complex macromolecular reactivity captured by reactivity ratios precludes the prediction of the resultant microstructures by intuition, and the established theory to understand reactivity ratios (i.e., Q-e scheme) has not been updated substantially since the development of quantum mechanics. Here, we combine modern computation, data science, and experimental validation to gain a detailed structure–reactivity understanding of the electronic, energetic, and steric parameters that govern comonomer reactivity. Using >450 reactivity ratios identified from primary literature sources, a combination of parametrization from density functional theory and data science methods identified discrete structure–reactivity relationships in free radical copolymerization. We demonstrate that the electronics of the radical species and the sterics of the monomers, neither of which were captured in the previous Q-e scheme, are pivotal to understanding the copolymerization reactivity. Experimentally measured reactivity ratios of a previously unstudied monomer combination validated the models, demonstrating the potential predictive power of this work to understand the comonomer sequence a priori. This work provides a framework to understand polymer microstructure from first-principles by relating specific and interpretable attributes of monomer structure to comonomer reactivity.

Abstract. Globally, the absolute number of war deaths has been declining since 1946. And yet, conflict and violence are currently on the rise, with many conflicts today waged between non-state actors such as political militias, criminal, and international terrorist groups. Unresolved regional tensions, a breakdown in the rule of law, absent or co-opted state institutions, illicit economic gain, and the scarcity of resources exacerbated by climate change, have become dominant drivers of conflict (UN. A new era of conflicts, 2022).In the ear of modern technology, data science, machine learning, and AI, the available shall be used to analyze, understand and possibly predict the possibility of conflicts outbreaks in various parts of the world. Moreover, it should provide tools for political scientists to a deeper understanding of political processes and enhance their decision-making processes.This paper focuses on applying data science techniques to process and analyze data in three various data analysis domains: Semantic, Geospatial, and Temporal Analysis. It provides the possible sources of the conflict and other datasets used for the analytics mentioned above. The data is used for research and experimental purposes only. These analytical processes provide the mechanisms to discover the historical data and identify the potential causes of the conflicts.

Confronted with the increasing dynamism and complexity of educational and teaching activities, the traditional budget management model faces significant challenges in terms of accuracy, foresight, and responsiveness. The development of Artificial Intelligence technology provides a critical pathway for paradigm innovation in instructional budget management. This study systematically explores the application of Artificial Intelligence technology in instructional budget management. Firstly, it constructs an intelligent theoretical framework integrating modern budget theory, data science, and management decision science, analyzing three enabling mechanisms-data-driven processes, workflow reconstruction, and decision augmentation-and designs a hierarchical, decoupled system architecture. Subsequently, the study elaborates on the intelligent application pathways for key stages such as budget formulation, execution monitoring, and performance evaluation, elucidating the shift of management processes toward real-time, precise, and proactive prediction under data-driven approaches. Finally, the research anticipates future trends, including the deepening of human-machine collaboration, the evolution of decision-making models toward predictive and prescriptive modes, and the development of system platforms toward ecosystem-based and self-adaptive directions. This study aims to provide theoretical reference and application guidance for educational institutions in constructing intelligent budget management systems.