Designing and Developing a Data Science Programme in Bhutan

Crowd-Sourcing for Data Science and Quantifiable Challenges: Optimal Contest Design

Because of the growing involvement of communities from various disciplines, data science is constantly evolving and gaining popularity. The growing interest in data science-based services and applications presents numerous challenges for their development. Therefore, data scientists frequently turn to various forums, particularly domain-specific Q&A websites, to solve difficulties. These websites evolve into data science knowledge repositories over time. Analysis of such repositories can provide valuable insights into the applications, topics, trends, and challenges of data science. In this article, we investigated what data scientists are asking by analyzing all posts to date on DSSE, a data science-focused Q&A website. To discover main topics embedded in data science discussions, we used latent Dirichlet allocation (LDA), a probabilistic approach for topic modeling. As a result of this analysis, 18 main topics were identified that demonstrate the current interests and issues in data science. We then examined the topics' popularity and difficulty. In addition, we identified the most commonly used tasks, techniques, and tools in data science. As a result, "Model Training", "Machine Learning", and "Neural Networks" emerged as the most prominent topics. Also, "Data Manipulation", "Coding Errors", and "Tools" were identified as the most viewed (most popular) topics. On the other hand, the most difficult topics were identified as "Time Series", "Computer Vision", and "Recommendation Systems". Our findings have significant implications for many data science stakeholders who are striving to advance data-driven architectures, concepts, tools, and techniques.

This present research aims to determine the growing needs and challenges faced by Data Science and Cyber Security students at Al Istiqlal University's Faculty of Information Technology when learning the English language. It also tries to ascertain whether gender and level of competence affected their requirements and difficulties in mastering the English language. 35 cadets who are specializing in Data Science and Cyber Security make up the sample. The researcher gave out 39 questionnaire items divided into eight domains. The results show that cadets in Data Science and Cyber Security did not undergo any guidance regarding how to utilize English in the discipline while engaging in the analysis of data or cyber security keywords. Additionally, the study demonstrates that cadets majoring in data science and cyber security did not receive any guidance on how to learn to communicate in English, and the teaching activities in the English programs they had taken did not match their notions of the standards for expert English. Moreover, English proficiency requirements for cadets enrolling in Data Science and Cyber Security courses should be taken into consideration. Additionally, no statically meaningful differences in the demands for key competencies and barriers faced by Data Science and Cyber Security cadets are found when gender and competency traits are taken into consideration.

In the recent decade, several technologies have boomed up due to recent development in many technologies. These technologies have changed the life of human being and are increasing the profit for individual, organisations like NetFlix, Alibaba, Flipkart, etc. Today in this world, maximum people are surrounded by smart objects or using smart objects to make their life easier to live and convenient. But, on another side life is being made easier to live by analytics companies/ industries. For example, which user like which type of plays, movies, songs, etc., things are extracted by many companies. Such recommendations are being made and being provided improved services to the respective users. Such jobs are being done by Data Scientist. Whereas, Data science (a future of Artificial Intelligence) is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured (labelled) and unstructured (unlabelled) data. Moreover this, Big Data Analytics are the analysis mechanism used in Data Science by Data Scientist. Several tools like Hadoop, etc., are used to analysis the large amount of data and used in predicting the valuable information/ making decisions. But, in analysing we faces several concerns like complexity, scalability, privacy leaking and trust. So, this article discusses about such concerns, challenges (rising) in this emerging field in detail (with a comparative analysis/ taxonomy).

Visa is the payments technology that forms the backbone of the world's financial systems by handling more than 7 trillion dollars of payments annually and our data reflects how the world spends money. We will describe technical achievements we have made in the area of fraud and cover some open challenges in data science.

The connected vehicles will generate huge amount of pervasive and real time data, at very high frequencies. This poses new challenges for Data science. How to analyse these data and how to address short-term and long-term storage are some of the key barriers to overcome.

Big Data and Data Science in Critical Care

In this paper, we explore the integration of the Women in Data Science (WiDS) Challenge into an Exploratory Data Analysis (EDA) course as a strategic approach to elevate the skills of rapid learners. The WiDS Challenge, a globally recognized data science competition, provides students with a unique opportunity to apply theoretical knowledge to real-world problems in a collaborative and competitive environment. The WiDS Datathon offers a real-world data science global challenge that allows students to apply EDA techniques in a practical, competitive setting. Data science is crucial in education. A course on this subject aids student in comprehending data, performing analysis, building models, and drawing inferences from results. These skills are applicable in real-world scenarios such as healthcare, climate change, agriculture, and finance. By embedding the challenge within the EDA curriculum, students are exposed to hands-on data analysis, model development, and problem-solving exercises that reinforce classroom concepts. The approach fosters rapid skill development, critical thinking, and teamwork, while also promoting diversity and inclusion in the field of data science. The results indicate that the Datathon significantly boosts rapid learners' analytical capabilities and practical knowledge, demonstrating its value as an effective and engaging pedagogical tool for advanced EDA students. This study underscores the potential of project-based learning in enhancing the educational experience and preparing students for real-world data challenges. Students earned the credits because it was integrated into the course, which contributed to the teams' strong performance. As a result, the top three teams from our university improved their global rankings to 10th, 14th, and 47th, respectively, compared to the previous year.

IntroductionSocietal and individual benefits of data-intensive science are substantial but raise challenges of balancing individual privacy and public good, while building appropriate governance and socio-technical systems to support data-intensive science. We set out to define a new field of inquiry to move collective interests forward.
 Objectives and ApproachOur objectives were: 1. To create a concise definition of the emerging field of Population Data Science; 2. To highlight the characteristics and challenges of Population Data Science; 3. To differentiate Population Data Science from existing fields of data science and informatics; and 4. To discuss the implications and future opportunities for Population Data Science. Objectives 1 and 2 were met largely through International Population Data Linkage Network (IPDLN) member engagement, Objective 3 was evaluated via literature review, and Objective 4 was achieved through iterative and collective work on a peer-reviewed position paper.
 ResultsWe define Population Data Science succinctly as the science of data about people. It is related to, but distinct from, the fields of data science and informatics. A broader definition includes four characteristics of: i) data use for positive impact on individuals and populations; ii) bringing together and analyzing data from multiple sources; iii) identifying population-level insights; and iv) developing safe, privacy-sensitive and ethical infrastructure to support research. One implication of these characteristics is that few individuals or organisations possess all of the requisite knowledge and skills comprising Population Data Science, so this is by nature a multi-disciplinary “team science” field. There is a need to advance various aspects of science, such as data linkage technology, various forms of analytics, and methods of public engagement.
 Conclusion/ImplicationsThese implications are the beginnings of a research agenda for Population Data Science, which if approached as a collective field, will catalyze significant advances in our understanding of society, health, and human behavior and increase the impact of our research.

What really matters in research? What practices and behaviours lead to the highest quality? How can we recognise and incentivise those practices? These are some of the questions we hope to answer at Imperial College London in a pioneering research culture project entitled ‘Beyond Open Research: Understanding What Matters for Quality and Reliability.’ Our vision is a research culture that: recognises the public interest in high-quality, reliable research, and incentivises the behaviours that lead to it. Activities to support a positive research culture are distributed across many functional areas of the university, some exhibiting best practice, some unaware and most unrecognised. Moreover, it’s not clear what practices engender quality/reliability. In this phase, we unpick these challenges in data science, research data management and research software engineering. There are two ways the project goes Beyond Open Research. Firstly, recognising that openness doesn’t by itself imbue research with quality, we address gaps in scrutiny of data, software and AI. Secondly, we examine the inputs, practices and outputs that matter – to researchers, staff and the public – to refocus recognition towards quality and reliability. Through a participatory approach engaging research, academic, clinical and professional staff and the public, we’re: identifying the status of Open Research within Imperial exploring the challenges, questions and concerns of researchers and staff in engaging with responsible research practices around research data, software and AI co-creating practical, stakeholder-led recommendations to embed transparency, reliability and trustworthiness. Semi-structured interviews with experts will enrich the workshop findings, and a network of postgraduate Research Software Champions is raising the profile of software and development best practice among researchers. To the best of our knowledge, this ambitious approach has never been attempted before at a British university. We have a rare opportunity to try to break free from the culture of publish-or-perish, in the interests of researchers and society. We present the context and ambition of our vision, explore the value of meaningful engagement and share interim findings.

SkyPortal is an open-source software package designed to discover interesting transients efficiently, manage follow-up, perform characterization, and visualize the results. By enabling fast access to archival and catalog data, crossmatching heterogeneous data streams, and the triggering and monitoring of on-demand observations for further characterization, a SkyPortal-based platform has been operating at scale for >2 yr for the Zwicky Transient Facility Phase II community, with hundreds of users, containing tens of millions of time-domain sources, interacting with dozens of telescopes, and enabling community reporting. While SkyPortal emphasizes rich user experiences across common front-end workflows, recognizing that scientific inquiry is increasingly performed programmatically, SkyPortal also surfaces an extensive and well-documented application programming interface system. From back-end and front-end software to data science analysis tools and visualization frameworks, the SkyPortal design emphasizes the reuse and leveraging of best-in-class approaches, with a strong extensibility ethos. For instance, SkyPortal now leverages ChatGPT large language models to generate and surface source-level human-readable summaries automatically. With the imminent restart of the next generation of gravitational-wave detectors, SkyPortal now also includes dedicated multimessenger features addressing the requirements of rapid multimessenger follow-up: multitelescope management, team/group organizing interfaces, and crossmatching of multimessenger data streams with time-domain optical surveys, with interfaces sufficiently intuitive for newcomers to the field. This paper focuses on the detailed implementations, capabilities, and early science results that establish SkyPortal as a community software package ready to take on the data science challenges and opportunities presented by this next chapter in the multimessenger era.

The National Academies recommend academic institutions foster a basic understanding of data science in all undergraduates. However, data science education is not currently a graduation requirement at most colleges and universities. As a result, many graduates lack even basic knowledge of data science. To address the shortfall, academic institutions should incorporate introductory data science into general education courses. A general education IT course provides a unique opportunity to integrate data science education. Modules covering databases, spreadsheets, and presentation software, already present in many survey IT courses, teach concepts and skills needed for data science. As a result, a survey IT course can provide comprehensive introductory data science education by adding a data science module focused on modeling and evaluation, two key steps in the data science process. The module should use data science software for application, avoiding the complexities of programming and advanced math, while enabling an emphasis on conceptual understanding. We implemented a course built around these ideas and found that the course helps develop data savvy in students.

Prompted by the skyrocketing demand for data scientists, progress made by the ACM Data Science Task Force on defining data science competencies, and inquiries about data science accreditation, ABET is in the process of developing accreditation criteria for undergraduate data science programs. The effort is led by members of a joint data science criteria subcommittee appointed by ABET's Computing Accreditation Commission (CAC) and CSAB (the lead society for computing accreditation). Establishing data science accreditation criteria is a notable milestone in the maturing data science discipline, indicating the presence of an accepted body of knowledge, standards of practice, and ethical codes for practitioners. This position paper motivates the effort and discusses prior work towards defining data science education requirements. It describes the ongoing process for creating and obtaining approval of the accreditation criteria, and how feedback was and will be solicited from the computing and statistical communities. The current draft data science criteria, which was approved in July 2020 by the relevant ABET bodies for a year of public review and comment, is presented. These criteria emphasize the three pillars of data science: computing foundations, mathematical/statistical foundations, and experience in at least one data application domain. This report thus serves both to inform and to stimulate the academic discussion needed to finalize appropriate data science accreditation by ABET.

A novel firefly algorithm approach for efficient feature selection with COVID-19 dataset

The use of program code as a data source is increasingly expanding among data scientists. The purpose of the usage varies from the semantic classification of code to the automatic generation of programs. However, the machine learning model application is somewhat limited without annotating the code snippets. To address the lack of annotated datasets, we present the Code4ML corpus. It contains code snippets, task summaries, competitions, and dataset descriptions publicly available from Kaggle—the leading platform for hosting data science competitions. The corpus consists of ~2.5 million snippets of ML code collected from ~100 thousand Jupyter notebooks. A representative fraction of the snippets is annotated by human assessors through a user-friendly interface specially designed for that purpose. Code4ML dataset can help address a number of software engineering or data science challenges through a data-driven approach. For example, it can be helpful for semantic code classification, code auto-completion, and code generation for an ML task specified in natural language.