Data Science Skills Research Articles

Health data science course for clinicians: Time to bridge the skills gap?

Data science skills are highly relevant for clinicians working in an era of big data in healthcare. However, these skills are not routinely taught, representing a growing unmet educational need. This education report presents a structured short course that was run to teach clinicians data science and the lessons learnt. A 1-day introductory course was conducted within a tertiary hospital in London. It consisted of lectures followed by facilitated pair programming exercises in R, an object-oriented programming language. Feedback was collated and participant responses were graded using a Likert scale. The course was attended by 20 participants. The majority of participants (69%) were in higher speciality cardiology training. While more than half of the participants (56%) received prior training in statistics either through formal taught programmes (e.g., a Master's degree) or online courses, the participants reported several barriers to expanding their skills in data science due to limited programming skills, lack of dedicated time, training opportunities and awareness. After the short course, there was a significant increase in participants' self-rated confidence in using R for data analysis (mean response; before the course: 1.69 ± 1.0, after the course: 3.2 ± 0.9, p = .0005) and awareness of the capabilities of R (mean response; before the course: 2.1 ± 0.9, after the course: 3.6 ± 0.7, p = .0001, on a 5-point Likert scale). This proof-of-concept study demonstrates that a structured short course can effectively introduce data science skills to clinicians and supports future educational initiatives to integrate data science teaching into medical education.

A modular curriculum to teach undergraduates ecological forecasting improves student and instructor confidence in their data science skills

Abstract Data science skills (e.g., analyzing, modeling, and visualizing large data sets) are increasingly needed by undergraduates in the life sciences. However, a lack of both student and instructor confidence in data science skills presents a barrier to their inclusion in undergraduate curricula. To reduce this barrier, we developed four teaching modules in the Macrosystems EDDIE (for environmental data-driven inquiry and exploration) program to introduce undergraduate students and instructors to ecological forecasting, an emerging subdiscipline that integrates multiple data science skills. Ecological forecasting aims to improve natural resource management by providing future predictions of ecosystems with uncertainty. We assessed module efficacy with 596 students and 26 instructors over 3 years and found that module completion increased students’ confidence in their understanding of ecological forecasting and instructors’ likelihood to work with long-term, high-frequency sensor network data. Our modules constitute one of the first formalized data science curricula on ecological forecasting for undergraduates.

Fostering the Development of Earth Data Science Skills in a Diverse Community of Online Learners: A Case Study of the Earth Data Science Corps

Today’s data-driven world requires earth and environmental scientists to have skills at the intersection of domain and data science. These skills are imperative to harness information contained in a growing volume of complex data to solve the world’s most pressing environmental challenges. Despite the importance of these skills, Earth and Environmental Data Science (EDS) training is not equally accessible, contributing to a lack of diversity in the field. This creates a critical need for EDS training opportunities designed specifically for underrepresented groups. In response, we developed the Earth Data Science Corps (EDSC) which couples a paid internship for undergraduate students with faculty training to build capacity to teach and learn EDS using Python at smaller Minority Serving Institutions. EDSC faculty participants are further empowered to teach these skills at their home institutions which scales the program beyond the training lead by our team. Using a Rasch modeling approach, we found that participating in the EDSC program had a significant impact on undergraduate learners’ comfort and confidence with technical and nontechnical data science skills, as well as their science identity and sense of belonging in science, two critical aspects of recruiting and retaining members of underrepresented groups in STEM. Supplementary materials for this article are available online.

How does algorithm-based HR predict employees’ sentiment? Developing an employee experience model through sentiment analysis

Purpose This study aims to develop a conceptual model of positive employee experience using sentiment analysis within algorithm-based human resource (HR) strategies. Its goal is to enhance HR professionals’ understanding of employee experiences and enable data-driven decision-making to create a positive work environment, thereby contributing to the originality of HR research. Design/methodology/approach The study conducts sentiment analysis – a text mining technique – to assess employee reviews and extract distinct positive experience factors. The employed data-driven methodology serves to fortify the reliability and objectivity of the analysis, ultimately resulting in a more refined depiction of the conveyed sentiment. Findings Utilizing sentiment analysis, the authors identified 135 keywords that signify positive employee experiences. These keywords were then categorized into four clusters aligned with factors influencing employee experience: work, relationships, organizational system and organizational culture, employing an inductive approach. The framework outlines the process of nurturing positive employee experiences throughout the employee life cycle, incorporating insights from the affective events theory and cognitive appraisal theory. Practical implications Data-driven insights empower HR professionals to enhance employee satisfaction, engagement and productivity. HR managers implementing AI-assisted HR ecosystems need digital and data science skills. Additionally, these insights can offer practical support in accentuating diversity and ethical considerations within the organizational culture. Candid employee data can enhance leadership and support diversity in organizational culture. Managers play a crucial communication role, ensuring flexible access to personalized HR solutions. Originality/value Applying sentiment analysis through opinion mining allows for the collection of unstructured data, reflecting authentic employee perceptions. This innovative approach expedites issue identification and targeted actions, enhancing employee satisfaction. Textual reviews, integral to employee feedback, offer comprehensive insights. Additionally, considering subjectivity and review length in online employee reviews adds value to understanding experiences (Zhao et al., 2019). This study surpasses prior research by directly identifying key factors of employee experience through the analysis of actual employee review texts, addressing a gap in understanding beyond previous attempts.

THE AI REVOLUTION IN IT EDUCATION

Artificial intelligence (AI) is rapidly transforming the IT landscape, demanding a paradigm shift in IT education. This paper explores the impact of AI on the educational process for aspiring IT specialists. The goals of transformation. Shifting focus of the curriculum is transitioning from a purely technical focus to preparing students for human-AI collaboration. Develop essential skills in data science, machine learning, and understanding the ethical implications of AI become central to the IT education framework. Orientation on lifelong learning for create adaptability and continuous learning skills to navigate the evolving field. Forming soft skills in communication, collaboration, and creativity gain prominence alongside technical expertise. The benefits are job market preparedness with graduates equipped by AI skills professionals become more competitive in the workforce. Enhanced problem-solving with understanding and applying AI tools improves students’ ability to analyze problems and develop innovative solutions. Increased Efficiency by leverage AI to automate tasks, optimize workflows, and achieve greater productivity. Fostering innovation with Integrating AI into the curriculum encourages students to explore new applications of AI technology. AI revolution create some challenges. Educational institutions need to adapt their curriculum and train faculty to effectively deliver AI-related content. Ensuring all students have equal access to the resources required for learning AI is crucial. Integrating discussions about AI ethics prepares students to use AI responsibly. The results are the integration of artificial intelligence (AI) in IT education is transforming the landscape of learning and teaching. This revolution is reshaping how students engage with content, how educators deliver instruction, and how institutions operate. Here are the key areas where AI is making a significant impact in IT education.

Teaching classical machine learning as a graduate-level course in chemical engineering: An algorithmic approach

The demand for engineering graduates with technical skills in data science, machine learning (ML), and artificial intelligence (AI) is now growing. Chemical engineering (ChemE) departments around the world are currently addressing this skills gap by instituting AI or ML elective courses in their program. However, designing such a course is difficult since the issue of which ML models to teach and the depth of theory to be discussed remains unclear. In this paper, we present a graduate-level ML course particularly designed such that students will be able to apply ML for research in ChemE. To achieve this, the course intends to cover a wide selection of ML models with emphasis on their motivations, derivations, and training algorithms, followed by their applications to ChemE-related data sets. We argue that this algorithmic approach to teaching ML can help broaden the capabilities of students since they can judge for themselves which tool to use when, even for problems outside the process industries, or they can modify the methods to test novel ideas. We found that students remain engaged in the mathematical details as long as every topic is properly motivated and the gaps in the required statistical and computer science concepts are filled. Hence, this paper also presents a roadmap of ML topics, their motivations, and bridging topics that can be followed by instructors. Lastly, we report anonymized student feedback on this course which is being offered at the Department of Chemical Engineering, University of the Philippines, Diliman.

Hands-On Neuroinformatics Education at the Crossroads of Online and In-Person: Lessons Learned from NeuroHackademy.

NeuroHackademy ( https://neurohackademy.org ) is a two-week event designed to train early-career neuroscience researchers in data science methods and their application to neuroimaging. The event seeks to bridge the big data skills gap by introducing participants to data science methods and skills that are often ignored in traditional curricula. Such skills are needed for the analysis and interpretation of the kinds of large and complex datasets that have become increasingly important to neuroimaging research due to concerted data collection efforts. In 2020, the event rapidly pivoted from an in-person event to an online event that included hundreds of participants from all over the world. This experience and those of the participants substantially changed our valuation of large online-accessible events. In subsequent events held in 2022 and 2023, we have developed a "hybrid" format that includes both online and in-person participants. We discuss the technical and sociotechnical elements of hybrid events and discuss some of the lessons we have learned while organizing them. We emphasize in particular the role that these events can play in creating a global and inclusive community of practice in the intersection of neuroimaging and data science.

Updated core competencies in pharmacoepidemiology to inform contemporary curricula and training for academia, government, and industry.

The first paper to specify the core content of pharmacoepidemiology as a profession was published by an ISPE (International Society for Pharmacoepidemiology) workgroup in 2012 (Jones JK et al. PDS 2012; 21[7]:677-689). Due to the broader and evolving scope of pharmacoepidemiology, ISPE considers it important to proactively identify, update and expand the list of core competencies to inform curricula of education programs; thus, better positioning pharmacoepidemiologists across academic, government (including regulatory), and industry positions. The aim of this project was to update the list of core competencies in pharmacoepidemiology. To ensure applicability of findings to multiple areas, a working group was established consisting of ISPE members with positions in academia, industry, government, and other settings. All competencies outlined by Jones et al. were extracted from the initial manuscript and presented to the working group for review. Expert-based judgments were collated and used to identify consensus. It was noted that some competencies could contribute to multiple groups and could be directly or indirectly related to a group. Five core domains were proposed: (1) Epidemiology, (2) Clinical Pharmacology, (3) Regulatory Science, (4) Statistics and data science, and (5) Communication and other professional skills. In total, 55 individual competencies were proposed, of which 25 were new competencies. No competencies from the original work were dropped but aggregation or amendments were made where considered necessary. While many core competencies in pharmacoepidemiology have remained the same over the past 10 years, there have also been several updates to reflect new and emerging concepts in the field.

Advancing Data Science Among the Federal Public Health Workforce: The Data Science Upskilling Program, Centers for Disease Control and Prevention.

Data can guide decision-making to improve the health of communities, but potential for use can only be realized if public health professionals have data science skills. However, not enough public health professionals possess the quantitative data skills to meet growing data science needs, including at the Centers for Disease Control and Prevention (CDC). The Data Science Upskilling (DSU) program increases data science literacy among staff and fellows working and training at CDC. The DSU program was established in 2019 as a team-based, project-driven, on-the-job applied upskilling program. Learners, within interdisciplinary teams, use curated learning resources to advance their CDC projects. The program has rapidly expanded from upskilling 13 teams of 31 learners during 2019-2020 to upskilling 36 teams of 143 learners during 2022-2023. All 2022-2023 cohort respondents to the end-of-project survey reported the program increased their data science knowledge. In addition, 90% agreed DSU improved their data science skills, 93% agreed it improved their confidence making data science decisions, and 96% agreed it improved their ability to perform data science work that benefits CDC. DSU is an innovative, inclusive, and successful approach to improving data science literacy at CDC. DSU may serve as an upskilling model for other organizations.

Normalizing Large Scale Sensor-Based MWD Data: An Automated Method toward A Unified Database.

In the context of geo-infrastructures and specifically tunneling projects, analyzing the large-scale sensor-based measurement-while-drilling (MWD) data plays a pivotal role in assessing rock engineering conditions. However, handling the big MWD data due to multiform stacking is a time-consuming and challenging task. Extracting valuable insights and improving the accuracy of geoengineering interpretations from MWD data necessitates a combination of domain expertise and data science skills in an iterative process. To address these challenges and efficiently normalize and filter out noisy data, an automated processing approach integrating the stepwise technique, mode, and percentile gate bands for both single and peer group-based holes was developed. Subsequently, the mathematical concept of a novel normalizing index for classifying such big datasets was also presented. The visualized results from different geo-infrastructure datasets in Sweden indicated that outliers and noisy data can more efficiently be eliminated using single hole-based normalizing. Additionally, a relational unified PostgreSQL database was created to store and automatically transfer the processed and raw MWD as well as real time grouting data that offers a cost effective and efficient data extraction tool. The generated database is expected to facilitate in-depth investigations and enable application of the artificial intelligence (AI) techniques to predict rock quality conditions and design appropriate support systems based on MWD data.

Surgical Sabermetrics: A Scoping Review of Technology-enhanced Assessment of Nontechnical Skills in the Operating Room.

To evaluate the current evidence for surgical sabermetrics: digital methods of assessing surgical nontechnical skills and investigate the implications for enhancing surgical performance. Surgeons need high-quality, objective, and timely feedback to optimize performance and patient safety. Digital tools to assess nontechnical skills have the potential to reduce human bias and aid scalability. However, we do not fully understand which of the myriad of digital metrics of performance assessment have efficacy for surgeons. A systematic review was conducted by searching PubMed, EMBASE, CINAHL, and PSYCINFO databases following PRISMA-ScR guidelines. MeSH terms and keywords included "Assessment," "Surgeons," and "Technology". Eligible studies included a digital assessment of nontechnical skills for surgeons, residents, and/or medical students within an operative context. From 19,229 articles screened, 81 articles met the inclusion criteria. The studies varied in surgical specialties, settings, and outcome measurements. A total of 122 distinct objective, digital metrics were utilized. Studies digitally measured at least 1 category of surgical nontechnical skill using a single (n=54) or multiple objective measures (n=27). The majority of studies utilized simulation (n=48) over live operative settings (n=32). Surgical Sabermetrics has been demonstrated to be beneficial in measuring cognitive load (n=57), situation awareness (n=24), communication (n=3), teamwork (n=13), and leadership (n=2). No studies measured intraoperative decision-making. The literature detailing the intersection between surgical data science and operative nontechnical skills is diverse and growing rapidly. Surgical Sabermetrics may provide a promising modifiable technique to achieve desirable outcomes for both the surgeon and the patient. This study identifies a diverse array of measurements possible with sensor devices and highlights research gaps, including the need for objective assessment of decision-making. Future studies may advance the integration of physiological sensors to provide a holistic assessment of surgical performance.

Integrated Risk Management and Artificial Intelligence in Hospital

The topic revolves around the integration of Artificial Intelligence (AI) in Hospital Integrated Risk Management (IRM). AI offers significant advantages in enhancing risk identification, assessment, and mitigation across various areas of hospital operations. It can contribute to patient safety by enabling early detection of critical conditions, improving clinical risk management, and enhancing decisionmaking processes. AI also plays a vital role in information security and privacy, operational risk management, regulatory compliance, and human resources in hospitals. However, the use of AI in Hospital IRM comes with certain disadvantages and risks that need to be mitigated. These include data quality and bias, interpretability and transparency challenges, privacy and security concerns, reduced human oversight, ethical considerations, and implementation challenges. Mitigating these risks requires robust data governance, addressing bias in AI algorithms, ensuring transparency and accountability, implementing strong cybersecurity measures, and upholding ethical guidelines. To achieve successful implementation, hospitals should prioritize employee competencies, such as domain knowledge, data literacy, AI and data science skills, critical thinking, collaboration, adaptability, and ethical awareness. By developing these competencies and adhering to best practices, hospitals can optimize the use of AI in IRM, improve patient outcomes, enhance operational efficiency, and mitigate risks effectively.

Coding Code: Qualitative Methods for Investigating Data Science Skills

Despite the elevated importance of Data Science in Statistics, there exists limited research investigating how students learn the computing concepts and skills necessary for carrying out data science tasks. Computer Science educators have investigated how students debug their own code and how students reason through foreign code. While these studies illuminate different aspects of students’ programming behavior or conceptual understanding, a method has yet to be employed that can shed light on students’ learning processes. This type of inquiry necessitates qualitative methods, which allow for a holistic description of the skills a student uses throughout the computing code they produce, the organization of these descriptions into themes, and a comparison of the emergent themes across students or across time. In this article we share how to conceptualize and carry out the qualitative coding process with students’ computing code. Drawing on the Block Model to frame our analysis, we explore two types of research questions which could be posed about students’ learning. Supplementary materials for this article are available online.

Data science skills for the next generation of statisticians

This paper analyses the future prospects of statistics as a profession and how data science will change it. Indeed, according to Hadley Wickham, Chief Scientist at Rstudio, “a data scientist is a useful statistician”, establishing a strong connection between data science and applied statistics. In this direction, the aim is to look to the future by proposing a structural approach to future scenarios. Some possible definitions of data science are then discussed, considering the relationship with statistics as a scientific discipline. The focus then turns to an assessment of the skills required by the labor market for data scientists and the specific characteristics of this profession. Finally, the phases of a data science project are considered, outlining how these can be exploited by a statistician.

Data science training needs in sub-Saharan Africa: Implications for biomedical research and therapeutics capacity

Data use is becoming increasingly valuable worldwide and has positively improved decision-making in various sectors. Data science has revolutionized the study of epidemiology and disease control. Despite the unlimited potential of data science, Africa lags in its innovation and technological advancements. Although sub-Saharan Africa (SSA) has not taken full advantage of data science in healthcare delivery and biomedical research, attempts have been made to harness this opportunity. This article reviews the current level, potentials, and gaps in data science, genomics, and bioinformatics in SSA. We also identified needs associated with developing tools and building capacity in the region, while also acknowledging advances made in these areas to date. We identified opportunities in health data science in SSA, including benefits in combating diseases that burden this region. Applying data science has the potential to create comprehensive healthcare reports and convert them into relevant critical insights that can then be used to provide better care, reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases, and improve quality of life in general. In order to achieve this, artificial intelligence and machine learning to build and apply models are some of the common mechanisms applied in all domains of medical research, alongside computational tools and models to simulate systems or expedite biomedical research like bioinformatics and computational biology. Data science, a body of knowledge that uses modern tools and techniques for data management and utilization of big data for innovative interventions and biomedical research is upcoming in Sub-Saharan Africa. Its importance in understanding epidemiology of diseases, public health, and surveillance as well as other domains in allied medical disciplines were highlighted in this review. The dearth in data science skills, expertise, and institutions engaged in training data science needs urgent intervention to optimize the potential uses to improve medicine and healthcare delivery in SSA.

Challenges in AutoML and Declarative Studies Using Systematic Literature Review

Machine Learning (ML) technologies have become essential tools, transforming industries and unlocking incredible potential in various fields. ML is now widely used for data-driven decision-making and predictive analytics across fields like healthcare, finance, transportation, and more. However, building and implementing ML models can be complex and time-consuming, often requiring programming proficiency and data science skills. Despite significant progress in ML, non-experts often struggle with selecting algorithms, optimizing models, and deploying ML solutions. This paper conducts a systematic literature review to explore challenges in the area of machine learning based on multiple categories involving features engineering and data extraction, learning model structure and activities, learning-based analysis and visualization, analysis algorithms in data-based systems, machine learning algorithms and systems development, and declarative ML-based prediction. Addressing these challenges underlines the importance of following AutoML and Declarative ML strategies in simplifying the ML process.

The African Society of Human Genetics successfully launches global data science workshops

To accelerate the impact of African genomics on human health, data science skills and awareness of Africa’s rich genetic diversity must be strengthened globally. We describe the first African genomics data science workshop, implemented by the African Society of Human Genetics (AfSHG) and international partners, providing a framework for future workshops.

Using Competition to Improve Students’ Learning in a Project-Based Learning Course

In this article, we presented our findings regarding an online project-based learning course, delivered to 64 students from the Federal University of Sao Paulo, Brazil, during the COVID-19 pandemic, in the second semester of 2021. The course had the goal of teaching Project Management by means of a competition (the Data Science Olympics). Our goal was to investigate the systemic impacts of the competition on learning. Data was collected by means of a questionnaire and from comments posted on the teams’ websites. We followed a convergent parallel mixed methods approach. We analyzed the data using a causal loop diagram to connect the insights gained with quantitative and qualitative results. Our findings were as follows: 1)The use of competition in a project-based learning centered course helped the students to develop project management and data science skills, and fostered metacognition and knowledge sharing opportunities. 2)The Data Science Olympics increased the students’ intrinsic motivation to learn. 3)The project-based teaching practices (scaffolding the students’ learning, giving meaningful feedback to the students, and managing the activities) facilitated the students' learning. 4)The problems the students faced throughout the Project (dropouts, communication problems, lack of commitment, difficulty scheduling online team meetings) impacted negatively on the students' motivation.

Implementing Centralized Error Handling for Software Systems through the Integration of Machine Learning Techniques

Based on the increase of data volumes in the current world, modern software solutions' complexities and transaction volume make it imperative to establish more efficient and robust error-handling approaches. Traditional strategies have struggled to cope with the dynamics of voluminous transactions known to be decentralized and ad-hoc. This has led to operational disruptions and diverse software efficacy disruptions. The central prism of this paper points to how to leverage a proposed central error-handling system (CEHS). The proposition encapsulates how machine learning techniques can be leveraged to address these challenges. Discussing the limitations of current error-handling methods and highlighting benefits stemming from a centrist approach are embedded in this discourse. Consequently, this thread explores integrating ML algorithms as a prerequisite for identifying anomalies, triggering remedial actions, and predicting errors that could stem from the scenario. The remit of this discourse is to present a framework for CEHS implementation and discuss the potential impact regarding the reliability and performance of high transactional volumes through the prism of novel approaches in ML like data science skills and software engineering skills.

Expectations, competencies and domain knowledge in data- and machine-driven finance

Expectations about the economy and financial markets are often cast as figments of imaginaries of the future. While the sociology of finance have predominantly dealt with expectation formation in relation to calculative devices used in practices of valuation and prediction, this paper concerns the expectations finance professionals form about their work in data- and machine-driven finance. We examine how high-skilled professionals reflexively form expectations about their work and argue that techno-centric imaginaries of the future of finance tend to create an emphasis on domain-independent data science skills over financial domain knowledge. However, we show that such imaginaries do not necessarily perform the work-related expectations of financial professionals, but are instead challenged and nuanced in reflections about the value of practice-bound domain knowledge and expertise.