Designing and Delivering a Curriculum for Data Science Education across Europe

Data is currently being produced at an incredible rate globally, fuelled by the increasing ubiquity of the Web, and stoked by social media, sensors, and mobile devices. However, as the amount of available data continues to increase, so does the demand for professionals who have the necessary skills to manage and manipulate this data. This paper presents the European Data Science Academy (EDSA), an initiative for bridging the data science skills gap across Europe and training a new generation of world-leading data scientists. The EDSA project has established a rigorous process and a set of best practices for the production and delivery of curricula for data science. Additionally, the project’s efforts are dedicated to linking the demand for data science skills with the supply of learning resources that offer these skills

Evaluation of EDISON\u27s Data Science Competency Framework Through a Comparative Literature Analysis

During the emergence of Data Science as a distinct discipline, discussions of what exactly constitutes Data Science have been a source of contention, with no clear resolution. These disagreements have been exacerbated by the lack of a clear single disciplinary \u27parent.\u27 Many early efforts at defining curricula and courses exist, with the EDISON Project\u27s Data Science Framework (EDISON-DSF) from the European Union being the most complete. The EDISON-DSF includes both a Data Science Body of Knowledge (DS-BoK) and Competency Framework (CF-DS). This paper takes a critical look at how EDISON\u27s CF-DS compares to recent work and other published curricular or course materials. We identify areas of strong agreement and disagreement with the framework. Results from the literature analysis provide strong insights into what topics the broader community see as belonging in (or not in) Data Science, both at curricular and course levels. This analysis can provide important guidance for groups working to formalize the discipline and any college or university looking to build their own undergraduate Data Science degree or programs

Unraveling the Skillsets of Data Scientists: Text Mining Analysis of Dutch University Master Programs in Data Science and Artificial Intelligence

The growing demand for data scientists in the global labor market and the Netherlands has led to a rise in data science and artificial intelligence (AI) master programs offered by universities. However, there is still a lack of clarity regarding the specific skillsets of data scientists. This study aims to address this issue by employing Correlated Topic Modeling (CTM) to analyse the content of 41 master programs offered by seven Dutch universities. We assess the differences and similarities in the core skills taught by these programs, determine the subject-specific and general nature of the skills, and provide a comparison between the different types of universities offering these programs. Our findings reveal that research, data processing, statistics and ethics are the predominant skills taught in Dutch data science and AI master programs, with general universities emphasizing research skills and technical universities focusing more on IT and electronic skills. This study contributes to a better understanding of the diverse skillsets of data scientists, which is essential for employers, universities, and prospective students

Making the Most of Interim Assessment Data: Lessons from Philadelphia

Under No Child Left Behind, urban school districts have increasingly turned to interim assessments, administered at regular intervals, to help gauge student progress in advance of annual state exams. These assessments have spawned growing debate among educators, assessment experts, and the testing industry: are they worth the significant investment of money and time? In Making the Most of Interim Assessment Data: Lessons from Philadelphia, Research for Action (RFA) weighs in on this issue. The School District of Philadelphia (SDP) was an early adopter of interim assessments, implementing the exams in 2003. Unlike teachers in some other regions, Philadelphia elementary and middle grades teachers rated these 'Benchmark' assessments highly. However, the study found that enthusiasm did not necessarily correlate with higher rates of student achievement. What did predict student success were three factors -- instructional leadership, collective responsibility, and use of the SDP's Core Curriculum. The report underscores the value of investment in ongoing data interpretation that emphasizes teachers' learning within formal instructional communities, such as grade groups of teachers. This research was funded by the Spencer Foundation and the William Penn Foundation

Personal Food Computer: A new device for controlled-environment agriculture

Due to their interdisciplinary nature, devices for controlled-environment agriculture have the possibility to turn into ideal tools not only to conduct research on plant phenology but also to create curricula in a wide range of disciplines. Controlled-environment devices are increasing their functionalities as well as improving their accessibility. Traditionally, building one of these devices from scratch implies knowledge in fields such as mechanical engineering, digital electronics, programming, and energy management. However, the requirements of an effective controlled environment device for personal use brings new constraints and challenges. This paper presents the OpenAg Personal Food Computer (PFC); a low cost desktop size platform, which not only targets plant phenology researchers but also hobbyists, makers, and teachers from elementary to high-school levels (K-12). The PFC is completely open-source and it is intended to become a tool that can be used for collective data sharing and plant growth analysis. Thanks to its modular design, the PFC can be used in a large spectrum of activities.Comment: 9 pages, 11 figures, Accepted at the 2017 Future Technologies Conference (FTC

Computing Competencies for Undergraduate Data Science Curricula: ACM Data Science Task Force

At the August 2017 ACM Education Council meeting, a task force was formed to explore a process to add to the broad, interdisciplinary conversation on data science, with an articulation of the role of computing discipline-specific contributions to this emerging field. Specifically, the task force would seek to define what the computing/computational contributions are to this new field, and provide guidance on computing-specific competencies in data science for departments offering such programs of study at the undergraduate level. There are many stakeholders in the discussion of data science – these include colleges and universities that (hope to) offer data science programs, employers who hope to hire a workforce with knowledge and experience in data science, as well as individuals and professional societies representing the fields of computing, statistics, machine learning, computational biology, computational social sciences, digital humanities, and others. There is a shared desire to form a broad interdisciplinary definition of data science and to develop curriculum guidance for degree programs in data science. This volume builds upon the important work of other groups who have published guidelines for data science education. There is a need to acknowledge the definition and description of the individual contributions to this interdisciplinary field. For instance, those interested in the business context for these concepts generally use the term “analytics”; in some cases, the abbreviation DSA appears, meaning Data Science and Analytics. This volume is the third draft articulation of computing-focused competencies for data science. It recognizes the inherent interdisciplinarity of data science and situates computing-specific competencies within the broader interdisciplinary space

Bridging the demand and the offer in data science

During the last several years, we have observed an exponential increase in the demand for Data Scientists in the job market. As a result, a number of trainings, courses, books, and university educational programs (both at undergraduate, graduate and postgraduate levels) have been labeled as “Big data” or “Data Science”; the fil‐rouge of each of them is the aim at forming people with the right competencies and skills to satisfy the business sector needs. In this paper, we report on some of the exercises done in analyzing current Data Science education offer and matching with the needs of the job markets to propose a scalable matching service, ie, COmpetencies ClassificatiOn (E‐CO‐2), based on Data Science techniques. The E‐CO‐2 service can help to extract relevant information from Data Science–related documents (course descriptions, job Ads, blogs, or papers), which enable the comparison of the demand and offer in the field of Data Science Education and HR management, ultimately helping to establish the profession of Data Scientist.publishedVersio

Education for Citizenship in For-Profit Charter Schools?

Most Americans and many residents of other democratic countries hold public schools to the social and political goal of preparing children to be good citizens. This goal is being challenged by some new forms of schooling promoted through popular education reform movements, especially in the US. This article reveals potentially insurmountable conflicts between the beliefs and practices of one of those forms of schools, for-profit charter schools, and their public task of educating for citizenship. This study begins by exploring the public nature and purposes of public schools, especially their role in creating particular types of citizens. This understanding of public schooling and good citizenship, then, becomes the theoretical lens for analysing the practices of for-profit charter schools. A critical discourse analysis was conducted of school materials such as websites, curricula, investor relation materials, proposals for new charter schools, and interviews with charter school founders. That analysis was used to indicate aspects of support for and incompatibility with quality citizenship education and to assess the overall likelihood that for-profit schools can educate citizens well

Innovation and failure in mechatronics design education

Innovative engineering design always has associated with it the risk of failure, and it is the role of the design engineer to mitigate the possibilities of failure in the final system. Education should however provide a safe space for students to both innovate and to learn about and from failures. However, pressures on course designers and students can result in their adopting a conservative, and risk averse, approach to problem solving. The paper therefore considers the nature of both innovation and failure, and looks at how these might be effectively combined within mechatronics design education