by Mike Phipps

For students on our International Foundation Programme (IFP), technology and artificial intelligence (AI) have transformed their learning experiences. Translation tools can reduce language barriers, web programmes can provide worked solutions to maths problems, and for those who might flirt with academic misconduct, ChatGPT can even write whole assignments. For some, though, AI and the related domain of Data Science are not just a study tool but also an increasingly popular degree choice. We have seen this on our IFP, and in part to support them, we are developing a foundation module in Python programming.

An interesting question to ask as we seek to better support these students is, ‘why are these students applying to study Data Science when they could study similar, but more established degrees, such as Maths or Statistics or Computer Science?’ A simple answer would be that there are more Data Science and AI careers on offer and so naturally, more students want to study these specific topics in a degree. However, the existence of a future career does not, by itself, explain a degree choice. For a student to make a deliberate choice to embark on a journey towards a Data Science career they must, in some way, be attracted to it.

Discussing international students’ desires for an English language education, Chowdhury and Phan (2014) describe how students are ‘hailed’ into a subject position that promises them status and prestige. Similarly, a Data Science degree promises access to ‘The Sexiest Job of the 21^st Century’ (Davenport & Patil, 2012, 2022). Put together, this suggests that our IFP data science students might be pursuing a dream of a glamorous and currently hot career. That dream itself, to use the language of Foucault (1980), belongs to a discourse in society that produces desires in people and creates ways of understanding the world. The remainder of this blog reports on a small-scale study that begins to ask what those desires and ways of understanding are. Future research will then aim to unravel further how and why they come into being and why it matters.

In the Summer of 2023, I gave four IFP students a short questionnaire, containing five questions, that in different ways asked why they had chosen to apply for Data Science. As seen in the quotes below, the students made reference to the interdisciplinary nature of Data Science and also in some way to opportunities in the Data Science job market.

‘I believe that data will hold a crucial role in real life. It is believed that data scientists with capable domain knowledge and strong data interpretation skills could become irreplaceable manpower in the future.’

‘I chose data science instead of other disciplinaries like software engineering or mechanical engineering is because it’s the most emerging field and its [sic] also highest in demand.’

‘Data science is an integrated subject, so i guess i just want to give myself more options and see what i really like.’

‘i found out that data science is an interdisciplinary course as in i can learn many knowledges.’

The students seem to have chosen to study Data Science because it promises them good job prospects and gave them a wider variety of skills than what they expected would be taught in more established degree programmes. An interesting question to consider as we support our students is the extent to which this career plan can be borne out in reality. Bristol’s MSc in Data Science, for example, is advertised as producing graduates who are ‘keenly sought after in roles such as lead data scientists or lead data engineers’ (University of Bristol, 2023); however, in reality, the job market may well be in a ‘trough of disillusionment’ where the range and levels of jobs is increasingly fragmented (Gift, 2019). Should our IFP programme, therefore, be preparing students to enter the higher levels of Data Science that they have heard about and which they desire, or is there scope to teach other skills that cover a wider range of the job market? Skills such as business analytics, visual analytics and client management – all of which Saltz et al. (2018) have identified in different Data Science programmes.

As well as pursuing high-level careers, the students’ questionnaire responses showed they were expecting to learn a particular set of skills. One of the students made particular reference to ‘machine learning’ algorithms and ‘big data’. He wrote,

‘Studying courses involving mathematics and statistics will not develop all-rounded skills to analyze and interpret big data…I hope I can apply artificial intelligence and machine learning to process large volumes of real-time demographic data, ultimately making an impact on governance.’

The student appears to be alluding to the idea that, in a time of massive amounts of unstructured data, the scientific method for understanding the world is redundant and what is needed are computational algorithms that can find meaning in data in ways that theoretical models cannot (Anderson, 2008). If this is the career goal for those progressing to Data Science, then what should our IFP look like? Our foundation courses typically use small-scale, structured data that can be analysed using simple mathematical models or inferential statistics, both of which the student believes are insufficient. Perhaps our Foundations of Statistics course could be updated to include larger-scale data sets. Or maybe our new module in Python programming could introduce students to the computational analysis of unstructured data. Whilst Data Science students are not the only ones who take these courses, the kinds of developments suggested here are certainly worth including in our discussions.

The four students in this study appear to be in pursuit of a high-level career that uses AI and Data Science tools to make sense of and influence the world. This post has begun to ask how we might be able to better support our IFP students in this journey. But what does this journey do to our students? How does a Data Science degree change their ways of seeing the world? What are the implications in society for an increasing number of graduates who are normalised into thinking about the word algorithmically? These and related questions can help to qualify what it means to ‘better’ support our IFP students.

 

References

 

Anderson, C. (2008). The End of Theory: The Data Deluge Makes the Scientific Method Obsolete. Wired magazine. https://www.wired.com/2008/06/pb-theory/

Chowdhury, R., & Phan, L. H. (2014). Desiring TESOL and international education: market abuse and exploitation. Multilingual Matters.

Davenport, T. H., & Patil, D. (2012). Data Scientist: The Sexiest Job of the 21st Century. Harvard Business Review, 90(5), 70-76.

Davenport, T. H., & Patil, D. (2022). Is Data Scientist Still the Sexiest Job of the 21st Century? Harvard Business Review.

Foucault, M. (1980). Power-knowledge: selected interviews and other writings, 1972-1977. Harvester Press.

Gift, N. (2019). Why There Will Be No Data Science Job Titles By 2029. Forbes. https://www.forbes.com/sites/forbestechcouncil/2019/02/04/why-there-will-be-no-data-science-job-titles-by-2029

Saltz, J., Armour, F., & Sharda, R. (2018). Data science roles and the types of data science programs. Communications of the Association for Information Systems, 43(1).

University of Bristol. (2023). MSc Data Science. Retrieved 15 December 2023 from https://www.bristol.ac.uk/study/postgraduate/taught/msc-data-science/