Data Science Courses Research Articles

Teaching data science to undergraduate translation trainees: Pilot evaluation of a task-based course

The advancement in technology has changed the workflow and the role of human translator in recent years. The impact from the trend of technology-mediated translation prompted the ratification of technology literacy as a major competence for modern translators. Consequently, teaching of translation technology including but not limited to Computer-aided Translation (CAT) and Machine Translation (MT) became part of comprehensive curricula for translation training programs. However, in many institutions, the teaching of translation technology was haunted by issues such as: narrow scope of curriculum design, outdated technologies, and unbalance between theories and practices in teaching. The study was the pilot evaluation of a tailored course to foster translation trainees’ knowledge and abilities of data science. The course was designed to be a fundamental step toward sophisticated translation technologies. During the pilot evaluation of the 8-week course, 85 students (n = 85) were recruited as participants. The study adopted a mix-method design by employing a survey to investigate student’s level of satisfaction toward the course and focus group discussion to understand students’ attitudes and perceptions of key aspects of the course. By interpreting the results from statistical analysis of the survey (5.39/7) and thematic analysis of the focus group discussion, the course of data science for translators was well received among participants. The evaluation project manifested the feasibility and effectiveness of a translator-oriented data science course.

Writing goals in U.S. undergraduate data science course outlines: A textual analysis

AbstractInstructors at postsecondary institutions have designed a myriad of data science classes to keep up with the rise of big data. Businesses and companies have become increasingly interested in hiring people with strong data acquisition, management, and communication skills. Since data science as a field of study is relatively new, though it has deep connections to statistical studies, there are few comprehensive analyses of data science classes, majors, programs, and curricular goals. Through this research, we analyze how writing and communication are taught in undergraduate data science classes in the United States. We analyze the presence of writing and communication learning goals from course descriptions and course syllabi. These results show that most data science courses emphasize technical, computing skills over writing, and communication skills. We conclude with a set of actionable heuristics that emphasize integrating writing and communication into data science courses so that students are prepared to use these skills as responsible citizens.

“I LOVE MATH ONLY IF IT'S CODING”: A CASE STUDY OF STUDENT EXPERIENCES IN AN INTRODUCTION TO DATA SCIENCE COURSE

Many important voices--including The National Council for Teachers of Mathematics (NCTM), the Dana Center’s Launch Years initiative, and others--advocate for expanding the traditional course offerings in high school mathematics and statistics to include courses such as the Introduction to Data Science (IDS). To date, the research on the IDS course has primarily focused on pedagogy, professional learning for teachers, and the curriculum. This mixed-methods case study expands our understanding by analyzing the perspective of IDS students at a California public high school. Self-determination theory provides a useful frame for interpreting how these students experience the IDS course. The theory focuses on conditions for students to engage in meaningful learning: competence (self-efficacy), autonomy (agency), and relatedness (a sense of belonging). The findings from this case study suggest the IDS students feel confident, empowered, and part of a vibrant community, unlike previous mathematics and statistics courses they may have completed; and use specific language to describe their joy in problem-solving and the accessibility of the course. These findings have implications for the development and refinement of any high school data science course, including IDS.

5Ws AND 1H OF TERM PROJECTS IN THE INTRODUCTORY DATA SCIENCE CLASSROOM

Many data science applications involve generating questions, acquiring data and preparing it for analysis—be it exploratory, inferential, or modeling focused—and communicating findings. Most data science curricula address each of these steps as separate units in a course or as separate courses. Open-ended term projects, on the other hand, allow students to put each of these steps into practice, sequentially and iteratively. In this paper we discuss what we mean by data science projects, why they are crucial in introductory data science courses, who works on these projects and how, when in the term they can be implemented, and where they can be shared.

TEACHING AND LEARNING DATA-DRIVEN MACHINE LEARNING WITH EDUCATIONALLY DESIGNED JUPYTER NOTEBOOKS

This study examines modelling with machine learning. In the context of a yearlong data science course, the study explores how upper secondary students apply machine learning with Jupyter Notebooks and document the modelling process as a computational essay incorporating the different steps of the CRISP-DM cycle. The students’ work is based on a teaching module about decision trees in machine learning and a worked example of such a modelling process. The study outlines the students’ performance in carrying out the machine learning technically and reasoning about bias in the data, different data preparation steps, the application context, and the resulting decision model. Furthermore, the context of the study and the theoretical backgrounds are presented.

Opinionated Practices for Teaching Reproducibility: Motivation, Guided Instruction and Practice

In the data science courses at the University of British Columbia, we define data science as the study, development and practice of reproducible and auditable processes to obtain insight from data. While reproducibility is core to our definition, most data science learners enter the field with other aspects of data science in mind, for example predictive modeling, which is often one of the most interesting topics to novices. This fact, along with the highly technical nature of the industry standard reproducibility tools currently employed in data science, present out-of-the gate challenges in teaching reproducibility in the data science classroom. Put simply, students are not as intrinsically motivated to learn this topic, and it is not an easy one for them to learn. What can a data science educator do? Over several iterations of teaching courses focused on reproducible data science tools and workflows, we have found that providing extra motivation, guided instruction and lots of practice are key to effectively teaching this challenging, yet important subject. Here we present examples of how we motivate, guide, and provide ample practice opportunities to data science students to effectively engage them in learning about this topic.

Research on the Construction of Application-Oriented Undergraduate Data Science and Big Data Technology Courses

In order to conduct research and analysis on the construction of application-oriented undergraduate data science and big data technology courses, the professional development characteristics of universities and enterprises should be taken into consideration, the development trend of the big data industry should be scrutinized, and professional application-oriented talents should be cultivated in line with job requirements. This paper expounds the demand for capacity-building professional development in application-oriented undergraduate data science and big data technology courses, conducts research and analysis on the current situation of professional development, and puts forward strategies in hope to provide reference for capacity-building professional development.

An educator’s perspective of the tidyverse

Computing makes up a large and growing component of data science and statistics courses. Many of those courses, especially when taught by faculty who are statisticians by training, teach R as the programming language. A number of instructors have opted to build much of their teaching around use of the tidyverse. The tidyverse, in the words of its developers, "is a collection of R packages that share a high-level design philosophy and low-level grammar and data structures, so that learning one package makes it easier to learn the next". These shared principles have led to the widespread adoption of the tidyverse ecosystem. A large part of this usage is because the tidyverse tools have been intentionally designed to ease the learning process and make it easier for users to learn new functions as they engage with additional pieces of the larger ecosystem. Moreover, the functionality offered by the packages within the tidyverse spans the entire data science cycle, which includes data import, visualisation, wrangling, modeling, and communication. We believe the tidyverse provides an effective and efficient pathway for undergraduate students at all levels and majors to gain computational skills and thinking needed throughout the data science cycle. In this paper, we introduce the tidyverse from an educator's perspective. We provide a brief introduction to the tidyverse, demonstrate how foundational statistics and data science tasks are accomplished with the tidyverse, and discuss the strengths of the tidyverse, particularly in the context of teaching and learning.

Data Science and Energy: Some Lessons from Europe on Higher Education Course Design and Delivery

Data science is seen as a key enabler for technologies that help decarbonize global energy use. However, the energy sector continues to struggle to attract and train enough data scientists. The primary reason for this is the lack of emphasis on data science in most graduate programs in energy engineering, and the high barriers of entry for data scientists from other sectors. In this article, we present a snapshot of the data science–related curriculum being taught in graduate energy programs in four different European universities as well as include feedback we received from students and alumni of these programs. While knowledge of data science remains low across the board, students in these programs already recognize data science as an important element of their future professional careers. We also present findings from running three separate iterations of an energy data science course we developed in light of this feedback—one of these iterations was offered only in KU Leuven (Belgium), while the other two were accessible to students at all four universities. In the article, we also discuss challenges and opportunities arising from designing and delivering courses in a cross-university context. This foundational course and others like it are seen as a necessary means to enable students to take more specialized courses in data science, and eventually contribute toward realizing a sustainable energy transition and meeting climate change mitigation objectives.

A Fresh Shot at Statistics in the Classroom: Three Perspectives Using World Cup Soccer Player Data

In this article, we outline several activities revolving around soccer players who participated in the 2018 FIFA World Cup and 2019 FIFA Women’s World Cup. Classroom activities are described from different perspectives, useful for a range of different statistics courses. In a first semester probability theory course, students investigate the counter-intuitive birthday paradox empirically and theoretically. For an introductory data science course, students practice their data wrangling skills using the statistical software R. Additional activities are shared for those instructors interested in emphasizing multivariable thinking in a second semester applied statistical modeling course. The activities shared will provide a range of opportunities for instructors to incorporate the FIFA World Cup data in their own courses. Supplementary files for this article are available online.

Educating Consumers and Producers of Data: Review of <em>Making Sense of Numbers</em> by Jane E. Miller (2022)

Author Jane E. Miller has brought her considerable experience writing and teaching about numerate communication to a new textbook, Making Sense of Numbers. Miller uses clear prose, timely and authentic examples, and thought-provoking exercises to educate the next generation of consumers and producers of data, students in introductory quantitative reasoning, research methods, or data analysis courses. While the textbook does not fit the mold of a "typical" quantitative literacy course, creative instructors may find ways to use it in innovative quantitative literacy, data literacy, or introductory data science courses.

VLDB Panel Summary

Database education is at an inflection point. With the surge of interest in all things "data", enrollments in traditional database courses are at an all time high. At the same time, the rise of Data Science as a discipline has led to the creation of new courses whose content significantly overlaps that of an introductory database course (e.g. data preparation, cleaning, SQL). Students from all across campus aspire to take data science courses, even with limited Computer Science backgrounds. This juxtaposition of content and proliferation of audiences is causing many database educators to question what we should be teaching in our data-oriented courses, and what resources we should use to teach them.

From blended to online due to the COVID outbreak: the case study of a data science course

ABSTRACT Different approaches exist in delivering courses. The traditional face-to-face, the blended approach that combines the strengths of face-to-face with the application of technologies, and the online approach, which is the form of learning that takes place over the Internet. This article reports on the impact of the forced transformation of a data science course, previously provided through a blended approach, into a fully online course, due to the COVID-19 outbreak. The impact is reported in terms of the effort required by the teacher, the students’ feedback and the didactic outcomes. The students’ feedback and the didactic outcomes were compared with the students of the 2018/2019 academic year and between students directly involved or not in the management of the outbreak. The results show an increase in the perceived didactic quality, in the engagement and in the didactic outcomes (only the latter not statistically significant). Also, a specific tool used for the blended approach was very much appreciated by the students and we collected useful feedback for its improvement. The paper ends by summarising the main results, also discussed with colleagues, in order to consider our results alongside other experiences.

Using Team-Based Learning to Teach Data Science

ABSTRACT Data science is collaborative and its students should learn teamwork and collaboration. Yet it can be a challenge to fit the teaching of such skills into the data science curriculum. Team-Based Learning (TBL) is a pedagogical strategy that can help educators teach data science better by flipping the classroom to employ small-group collaborative learning to actively engage students in doing data science. A consequence of this teaching method is helping students achieve the workforce-relevant data science learning goals of effective communication, teamwork, and collaboration. We describe the essential elements of TBL: accountability structures and feedback mechanisms to support students collaborating within permanent teams on well-designed application exercises to do data science. The results of our case study of using TBL to teach a modern, introductory data science course indicate that the course effectively taught reproducible data science workflows, beginning R programming, and communication and collaboration. Students also reported much room for improvement in their learning of statistical thinking and advanced R concepts. To help the data science education community adopt this appealing pedagogical strategy, we outline steps for deciding on using TBL, preparing and planning for it, and overcoming potential pitfalls when using TBL to teach data science.

Writing to Learn: A Framework for Structuring Writing Assignments to Support Analytics Course Learning Goals

Drawing on the scholarship of writing and learning, this article motivates the use of writing assignments in analytics courses and develops a framework for instructional design that advances both writing skills and discipline-specific learning. We translate a best practices set of foundational writing concepts into a matrix of design levers for analytics instructors and propose an instructional design process that balances discipline-specific learning goals with foundational writing concepts through specific writing activities. We summarize our experience applying the framework to a particular data science course and present some early evidence for favorable outcomes. The positive effect we observe extends beyond learning course concepts and includes increased student engagement and contributions to group work.

Remembering Robert Lue: Giving Students “Not a Data Science Course, but a Data Science Life”

Remembering Robert Lue: Giving Students “Not a Data Science Course, but a Data Science Life”

Toward data‐scientific thinking

AbstractThe growth of the data culture has led to calls for improving data literacy among primary and secondary students and their teachers. One approach to improving data literacy is to teach a course devoted to data science but, given the lack of consensus over the term “data science,” just what should an introductory data science course include? The author argues that at the secondary level, an introductory data science course should strive to teach data‐scientific thinking, which has statistical thinking at its core, blended with some computational thinking, and with a dash of mathematics.

Computational skills by stealth in introductory data science teaching

AbstractIn 2010, Nolan and Temple Lang proposed “integration of computing concepts into statistics curricula at all levels.” The unprecedented growth in data and emphasis on data science has provided an impetus to finally realizing full implementations of this in new statistics and data science programs and courses. We discuss a proposal for the stealth development of computational skills in students' exposure to introductory data science through careful, scaffolded exposure to computation and its power. Our intent is to support students, regardless of interest and self‐efficacy in coding, in becoming data‐driven learners, who are capable of asking complex questions about the world around them, and then answering those questions through the use of data‐driven inquiry. Reference is made to the computer science and statistics consensus curriculum frameworks the International Data Science in Schools Project (IDSSP) recently published for secondary school data science or introductory tertiary programs, designed to optimize data‐science accessibility.

Quantitative skills in undergraduate neuroscience education in the age of big data

For over a half-Century, the mathematics requirement for graduation at most undergraduate colleges and universities has been one year of calculus and a semester of statistics. Many universities and colleges offer a neuroscience major that may or may not add additional mathematics, statistics, or data science requirements. Today in the age of Big Data and Systems Neuroscience, many students are ill-equipped for the future without the tools of computational competency that are necessary to tackle the large data sets generated by contemporary neuroscience research. Required courses in statistics still focus on parametric statistics based on the normal distribution and do not provide the computational tools required to analyze big data sets. Undergraduates in STEM fields including neuroscience need to enroll in the Data Science courses that are required in the social sciences (e.g., economics, political science and psychology). Contemporary systems neuroscience is routinely done by interdisciplinary research teams of statisticians, engineers, and physical scientists. Emerging “NeuroX-omics” such as connectomics have emerged along with genomics, proteomics, and transcriptomics, all of which deploy systems analysis techniques based on mathematical graph theory. Connectomics is the 21st Century’s functional neuroanatomy. Whole brain connectome research appears almost monthly in the Drosphila, zebra fish, and mouse literature, and human brain connectomics is not far behind. The techniques for connectomics rely on the tools of data science. Undergraduate neuroscience students are already squeezed for credit hours given the high-prescribed science curriculum for biology majors and premedical students, in addition to required courses in social sciences and humanities. However, additional training in mathematics, statistics, computer science, and/or data science is urgently needed for undergraduate neuroscience majors just to understand the contemporary research literature. Undoubtedly, the faculty who teach neuroscience courses are acutely aware of the problem and most of them freely acknowledge the importance of quantitative analytical skills for their students. However, some faculty members may feel that their own math and statistics knowledge or other analytical skills have atrophied beyond recall or were never fulfilled in the first place. In this commentary I suggest that this problem can be ameliorated, though not solved, through organizing workshops, journal clubs, or independent studies courses in which the students and the instructors learn and teach each other in short-course format. In addition, web-available teaching materials such as targeted video clips are plentifully available on the internet. To attract and maintain student interest, qauntitative instruction and learning should occur in neuroscience context.

Teaching Responsible Data Science: Charting New Pedagogical Territory.

Although an increasing number of ethical data science and AI courses is available, with many focusing specifically on technology and computer ethics, pedagogical approaches employed in these courses rely exclusively on texts rather than on algorithmic development or data analysis. In this paper we recount a recent experience in developing and teaching a technical course focused on responsible data science, which tackles the issues of ethics in AI, legal compliance, data quality, algorithmic fairness and diversity, transparency of data and algorithms, privacy, and data protection. Interpretability of machine-assisted decision-making is an important component of responsible data science that gives a good lens through which to see other responsible data science topics, including privacy and fairness. We provide emerging pedagogical best practices for teaching technical data science and AI courses that focus on interpretability, and tie responsible data science to current learning science and learning analytics research. We focus on a novel methodological notion of the object-to-interpret-with, a representation that helps students target metacognition involving interpretation and representation. In the context of interpreting machine learning models, we highlight the suitability of “nutritional labels”—a family of interpretability tools that are gaining popularity in responsible data science research and practice.