Advancing Data Science Competencies for Nursing PhD Students.

Given the evolving importance of data science approaches in nursing research, we developed a 3-credit, 15-week course that is integrated into the second year PhD curriculum at Columbia University School of Nursing. As a complement to didactic content, the students address a research question of their choice using a big data source, Jupyter Notebook, and R programming language. The course evolved over time with generative AI tools being added in 2023. Student self-evaluations of their data science competencies improved from baseline. This case study adds to the evolving body of literature on data science and AI competences in nursing.

Providing insights into health data science education through artificial intelligence.

BACKGROUND: Health Data Science (HDS) is a novel interdisciplinary field that integrates biological, clinical, and computational sciences with the aim of analysing clinical and biological data through the utilisation of computational methods. Training healthcare specialists who are knowledgeable in both health and data sciences is highly required, important, and challenging. Therefore, it is essential to analyse students' learning experiences through artificial intelligence techniques in order to provide both teachers and learners with insights about effective learning strategies and to improve existing HDS course designs. METHODS: We applied artificial intelligence methods to uncover learning tactics and strategies employed by students in an HDS massive open online course with over 3,000 students enrolled. We also used statistical tests to explore students' engagement with different resources (such as reading materials and lecture videos) and their level of engagement with various HDS topics. RESULTS: We found that students in HDS employed four learning tactics, such as actively connecting new information to their prior knowledge, taking assessments and practising programming to evaluate their understanding, collaborating with their classmates, and repeating information to memorise. Based on the employed tactics, we also found three types of learning strategies, including low engagement (Surface learners), moderate engagement (Strategic learners), and high engagement (Deep learners), which are in line with well-known educational theories. The results indicate that successful students allocate more time to practical topics, such as projects and discussions, make connections among concepts, and employ peer learning. CONCLUSIONS: We applied artificial intelligence techniques to provide new insights into HDS education. Based on the findings, we provide pedagogical suggestions not only for course designers but also for teachers and learners that have the potential to improve the learning experience of HDS students.

Design, implementation and evaluation of a spiral module combining data science, digital health and evidence-based medicine in the undergraduate medical curriculum: A mixed methods study.

BACKGROUND: Digital health, data science and health informatics are increasingly important in health and healthcare, but largely ignored in undergraduate medical training. METHODS: In a large UK medical school, with staff and students, we co-designed a new, 'spiral' module (with iterative revisiting of content), covering data science, digital health and evidence-based medicine, implementing in September 2019 in all year groups with continuous evaluation and improvement until 2022. RESULTS: In 2018/19, a new module, 'Doctor as Data Scientist', was co-designed by academic staff (n = 14), students (n = 23), and doctors (n = 7). The module involves 22 staff, 120 h (43 sessions: 22 lectures, 15 group and six other) over a 5-year curriculum. Since September 2019, 5,200 students have been taught with good attendance. Module student satisfaction ratings were 92%, 84%, 84% and 81% in 2019, 2020, 2021 and 2022 respectively, compared to the overall course (81%). CONCLUSIONS: We designed, implemented and evaluated a new undergraduate medical curriculum that combined data science and digital health with high student satisfaction ratings.

Motivation, inclusivity, and realism should drive data science education.

Data science education provides tremendous opportunities but remains inaccessible to many communities. Increasing the accessibility of data science to these communities not only benefits the individuals entering data science, but also increases the field's innovation and potential impact as a whole. Education is the most scalable solution to meet these needs, but many data science educators lack formal training in education. Our group has led education efforts for a variety of audiences: from professional scientists to high school students to lay audiences. These experiences have helped form our teaching philosophy which we have summarized into three main ideals: 1) motivation, 2) inclusivity, and 3) realism. 20 we also aim to iteratively update our teaching approaches and curriculum as we find ways to better reach these ideals. In this manuscript we discuss these ideals as well practical ideas for how to implement these philosophies in the classroom.

Pandemic response policies' democratizing effects on online learning.

The COVID-19 pandemic has changed peoples' lives in unexpected ways, especially how they allocate their time between work and other activities. Demand for online learning has surged during a period of mass layoffs and transition to remote work and schooling. Can this uptake in online learning help close longstanding skills gaps in the US workforce in a sustainable and equitable manner? We answer this question by analyzing individual engagement data of DataCamp users between October 2019 and September 2020 (n = 277,425). Exploiting the staggered adoption of actions to mitigate the spread of COVID-19 across states, we identify the causal effect at the neighborhood level. The adoption of nonessential business closures led to a 38% increase in new users and a 6% increase in engagement among existing users. We find that these increases are proportional across higher- and lower-income neighborhoods and neighborhoods with a high or low share of Black residents. This demonstrates the potential for online platforms to democratize access to knowledge and skills that are in high demand, which supports job security and facilitates social mobility.

Hands-on training about overfitting.

Overfitting is one of the critical problems in developing models by machine learning. With machine learning becoming an essential technology in computational biology, we must include training about overfitting in all courses that introduce this technology to students and practitioners. We here propose a hands-on training for overfitting that is suitable for introductory level courses and can be carried out on its own or embedded within any data science course. We use workflow-based design of machine learning pipelines, experimentation-based teaching, and hands-on approach that focuses on concepts rather than underlying mathematics. We here detail the data analysis workflows we use in training and motivate them from the viewpoint of teaching goals. Our proposed approach relies on Orange, an open-source data science toolbox that combines data visualization and machine learning, and that is tailored for education in machine learning and explorative data analysis.

CE: Nursing Orientation to Data Science and Machine Learning.

ABSTRACT: Nurses collect, use, and produce data every day in countless ways, such as when assessing and treating patients, performing administrative functions, and engaging in strategic planning in their organizations and communities. These data are aggregated into large data sets in health care systems, public and private databases, and academic research settings. In recent years the machines used in this work (computer hardware) have become increasingly able to analyze large data sets, or "big data," at high speed. Data scientists use machine learning tools to aid in analyzing this big data, such as data amassed from large numbers of electronic health records. In health care, predictions for patient outcomes has become a focus of research using machine learning. It's important for nurses and nurse administrators to understand how machine learning has changed our ways of thinking about data and turning data into knowledge that can improve patient care. This article provides an orientation to machine learning and data science, offers an understanding of current challenges and opportunities, and describes the nursing implications for nurses in various roles.

An integrated, modular approach to data science education in microbiology.

We live in an increasingly data-driven world, where high-throughput sequencing and mass spectrometry platforms are transforming biology into an information science. This has shifted major challenges in biological research from data generation and processing to interpretation and knowledge translation. However, postsecondary training in bioinformatics, or more generally data science for life scientists, lags behind current demand. In particular, development of accessible, undergraduate data science curricula has the potential to improve research and learning outcomes as well as better prepare students in the life sciences to thrive in public and private sector careers. Here, we describe the Experiential Data science for Undergraduate Cross-Disciplinary Education (EDUCE) initiative, which aims to progressively build data science competency across several years of integrated practice. Through EDUCE, students complete data science modules integrated into required and elective courses augmented with coordinated cocurricular activities. The EDUCE initiative draws on a community of practice consisting of teaching assistants (TAs), postdocs, instructors, and research faculty from multiple disciplines to overcome several reported barriers to data science for life scientists, including instructor capacity, student prior knowledge, and relevance to discipline-specific problems. Preliminary survey results indicate that even a single module improves student self-reported interest and/or experience in bioinformatics and computer science. Thus, EDUCE provides a flexible and extensible active learning framework for integration of data science curriculum into undergraduate courses and programs across the life sciences.

Big-Data Skills: Bridging the Data Science Theory-Practice Gap in Healthcare.

Demand for big-data scientists continues to escalate driving a pressing need for new graduates to be more fluent in the big-data skills needed by employers. If a gap exists between the educational knowledge held by graduates and big data workplace skills needed to produce results, workers will be unable to address the big data needs of employers. This survey explores big-data skills in the classroom and those required in the workplace to determine if a skills gap exists for big-data scientists. In this work, data was collected using a national survey of healthcare professionals. Participant responses were analyzed to inform curriculum development, providing valuable information for academics and the industry leaders who hire new data talent.

Systematic review of education and practical guidance on regression modeling for medical researchers who lack a strong statistical background: Study protocol.

In the last decades, statistical methodology has developed rapidly, in particular in the field of regression modeling. Multivariable regression models are applied in almost all medical research projects. Therefore, the potential impact of statistical misconceptions within this field can be enormous Indeed, the current theoretical statistical knowledge is not always adequately transferred to the current practice in medical statistics. Some medical journals have identified this problem and published isolated statistical articles and even whole series thereof. In this systematic review, we aim to assess the current level of education on regression modeling that is provided to medical researchers via series of statistical articles published in medical journals. The present manuscript is a protocol for a systematic review that aims to assess which aspects of regression modeling are covered by statistical series published in medical journals that intend to train and guide applied medical researchers with limited statistical knowledge. Statistical paper series cannot easily be summarized and identified by common keywords in an electronic search engine like Scopus. We therefore identified series by a systematic request to statistical experts who are part or related to the STRATOS Initiative (STRengthening Analytical Thinking for Observational Studies). Within each identified article, two raters will independently check the content of the articles with respect to a predefined list of key aspects related to regression modeling. The content analysis of the topic-relevant articles will be performed using a predefined report form to assess the content as objectively as possible. Any disputes will be resolved by a third reviewer. Summary analyses will identify potential methodological gaps and misconceptions that may have an important impact on the quality of analyses in medical research. This review will thus provide a basis for future guidance papers and tutorials in the field of regression modeling which will enable medical researchers 1) to interpret publications in a correct way, 2) to perform basic statistical analyses in a correct way and 3) to identify situations when the help of a statistical expert is required.

Introductory College Mathematics for the Life Sciences: Has Anything Changed?

This paper focuses on issues concerning the introductory college mathematics sequence with an emphasis on students interested in the life sciences, and concentration on the time after the publication of BIO2010 (BIO2010 in Transforming Undergraduate Education for Future Research Biologists, National Academies of Science, Medicine and Engineering, Washington, 2003). It also explores the potential uses of books targeted at introductory mathematics courses for life science majors today. As relevant background, we look at the evolution of the way that calculus has been taught over the past 50 years, including at the high school level. We also explore the implications of changes in technology and course delivery, such as online education. As we discuss different books and introductory course ideas, we focus on the needs of biology students, the inclusion of real-world problems and models, the role of technology, and the impact of data science. The paper is organized as follows: Sect. 1 provides some personal background with calculus dating back to the 1970s, and changes in calculus prior to BIO2010. Section 2 introduces goals for an introductory mathematics sequence and evaluates the calculus sequence in light of those goals. Sections 3-7 discuss various issues that will help to understand issues and challenges for introductory mathematics for the life sciences: Calculus in high school (Sect. 3), equity issues relative to calculus and other math topics (Sect. 4), the impact of online education (Sect. 5), math as a stumbling block for college students (Sect. 6), and the increasing importance and value of teaching data science (Sect. 7). Section 8 reviews the development of books in light of these issues and challenges. The last section (Sect. 9) summarizes conclusions.

Ad hoc efforts for advancing data science education.

With increasing demand for training in data science, extracurricular or "ad hoc" education efforts have emerged to help individuals acquire relevant skills and expertise. Although extracurricular efforts already exist for many computationally intensive disciplines, their support of data science education has significantly helped in coping with the speed of innovation in data science practice and formal curricula. While the proliferation of ad hoc efforts is an indication of their popularity, less has been documented about the needs that they are designed to meet, the limitations that they face, and practical suggestions for holding successful efforts. To holistically understand the role of different ad hoc formats for data science, we surveyed organizers of ad hoc data science education efforts to understand how organizers perceived the events to have gone-including areas of strength and areas requiring growth. We also gathered recommendations from these past events for future organizers. Our results suggest that the perceived benefits of ad hoc efforts go beyond developing technical skills and may provide continued benefit in conjunction with formal curricula, which warrants further investigation. As increasing numbers of researchers from computational fields with a history of complex data become involved with ad hoc efforts to share their skills, the lessons learned that we extract from the surveys will provide concrete suggestions for the practitioner-leaders interested in creating, improving, and sustaining future efforts.

[Training Medical Staff with Basic Skills for Data Science in Genomic Medicine].

The development of specialized training programs for medical personnel, particularly nurses, clinical laboratory technicians, and pharmacists, is considered critical for the promotion of genomic medicine throughout Japan. Specifically, medical personnel skilled at analyzing and understanding high-throughput genomic data are in high demand. In this symposium, we will introduce the basic knowledge and skills necessary for processing genomic data.

The State of Data Science in Genomic Nursing.

Nurse scientists are generating, acquiring, distributing, processing, storing, and analyzing greater volumes of complex omics data than ever before. To take full advantage of big omics data, to address core biological questions, and to enhance patient care, however, genomic nurse scientists must embrace data science. Intended for readership with limited but expanding data science knowledge and skills, this article aims to provide a brief overview of the state of data science in genomic nursing. Our goal is to introduce key data science concepts to genomic nurses who participate at any stage of the data science lifecycle, from research patient recruitment to data wrangling, preprocessing, and analysis to implementation in clinical practice to policy creation. We address three major components in this review: (1) fundamental terminology for the field of genomic nursing data science, (2) current genomic nursing data science research exemplars, and (3) the spectrum of genomic nursing data science roles as well as education pathways and training opportunities. Links to helpful resources are included throughout the article.

Data Science and Graduate Nursing Education: A Critical Literature Review.

BACKGROUND: The emergence of big data and data science offers unprecedented opportunities for accelerating scientific advances in nursing, yet current nursing curricula are not adequate to prepare students to leverage those opportunities. PURPOSE: The purpose of this review was to describe current strategies that can be used to educate graduate nurses about data science methods as well as facilitators and challenges to adopting those strategies. METHOD: We conducted a critical literature review of papers addressing data science and graduate nursing education. RESULTS: Ten articles were included in this review. The most common strategy was the integration of data science methods into existing courses throughout the graduate nursing curricula. A major facilitator was interdisciplinary collaboration between nursing faculty and colleagues in other disciplines. CONCLUSION: The findings provide strategies that can be used to prepare graduate nurses to work in data science teams to shape big data research and optimize patient outcomes.

Certification or Advanced Degrees.

The value of training for a data sciences professional is in the eye of the beholder. And dependent on the scope and breadth of that training and the cost and time frame of that training. Value for the employee may differ from value for the employer. The lens is different and value may depend on what lens you look through. Training can be online or on-site, short term with specific focus or longer term with greater breadth and less depth. Career goals should also be considered when determining value. Certification in Spark is not valuable if you do not want to work with Spark. A PhD in management psychology is not as valuable if you do not want to manage people. The fact that training (both certification and degree programs) is valuable is not debatable. Maximizing that value for both employee and employer is always a preferable option. But is it realistic?