Presentation given at BRIDGES2022 in Gandia, Spain 2022

1. SHAPING UP:
How data fellowships are helping create
diverse talent pipelines from the social
sciences into graduate data careers
Jackie Carter T: @JackieCarter E: jackie.carter@manchester.ac.uk
Professor of Statistical Literacy, National Teaching Fellow, One in Twenty Woman in Data 2020
University of Manchester
#Bridges2022 Conference Sept 16

2. Recent publications
informing this presentation
Two open access papers in Statistical Journal
of the IAOS, vol. 37, no. 3, pp. 935-950, and
pp. 1009-1021, 2021
• Carter J. Developing a future pipeline of applied
social researchers through experiential learning:
the case of a data fellows programme.
• Carter J., Méndez-Romero RA., Jones P, Higgins V
& Samartini, ALS EmpoderaData: Sharing
a successful work-placement data skills training
model within Latin America, to develop capacity
to deliver the SDGs.

3. Imagine …..

4. The Q-Step
Programme
£20m investment in developing quantitative skills in
social science university education; 2013-2021 in
eighteen UK Universities
“Design surveys and experiments and understand how to
analyse the data they generate
Analyse and interpret data (social media, government
departments, longitudinal cohort studies)
Evaluate the quality of data collection and analysis …and
how you can use data to make decisions”
Grundy, 2015, p5.

5. External evaluation of Q-Step (Nuffield, 2022)
https://www.nuffieldfoundation.org/publications/q-step-evaluation

6. Work
placements
&
internships
Graduate employers value work experience (Shury et al,
2017), and experiential learning literature acknowledges
the importance of internships and work placements
(Carter (2021), Roberts (2018), Aliix (2011)).
However, participation in work placements differs by
socioeconomic background with a 7% difference
undergraduate students from working-class backgrounds
(36%) undertaking work placements compared to their
middle-class peers (43%) (Montacute et. al, 2021)
Some researchers call for the banning of unpaid and
unadvertised internships (Friedman and Laurison, 2019).
Others recommend that internships longer than 4-weeks
duration should be paid at least the minimum wage
(Cullinane and Montacute 2018).

7. The Data Fellows model developed at The
University of Manchester
• Paid 8-week long data-driven research projects in host organisations
• Eligibility criteria
• Competitive and rigorous selection process
• 330 students have undertaken data fellowships
• 70+% female
• 25% from historically under-represented backgrounds

8. Example host
organisations
Public,
private and
not-for-profit
sectors
Prestigious
organisations

9. Two-tier eligibility criteria
Humanities and Social Sciences Degree
course
Social Science
Sociology
Criminology
Politics and Int Relations
Social Anthropology
Politics, Phil & Economics
English Language and Linguistics
BA [….] with Data Analytics
Course units/modules - examples
• First year
• Compulsory methods courses
• Measuring Inequalities
• Making Sense of Politics
• Making Sense of Criminological Data
• Second year
• The Survey Method in Social Research
• Market Research
• Essentials of Survey Design and
Analysis

10. Social Science Graduates
Prof Bobby Duffy, Chair Campaign for Social Science
Social sciences are an extremely broad church, and the opportunities they open up are just
as wide-ranging. The three core assets that I see in social science graduates are a deep
curiosity about how things work, the desire to enable change and learned skills to do this
sensitively and systematically. As is so clear from the employer perspectives in this book,
these are seen as vital abilities by employers, including that growth mindset, that can
build on a core of quantitative and qualitative skills that will be directly applied in the real
world. Most of the best answers to big challenges require a combination of a range of
skills, and social scientists have this embedded throughout their education.

11. SHAPE graduates: The Right Skills Report (2017)
Hetan Shah, Chief Executive of the British Academy
The skills and attributes that SHAPE (social science, humanities and arts for people and
the economy) disciplines develop are highly valued by employers and open up a wide
range of options across the private, public and third sectors. Graduates who study SHAPE
disciplines are highly employable.
Work-based experiences can help students see how the skills they acquire through
studying SHAPE are relevant to the workplace. Work placements and internships allow
students to strengthen their skills by using them in a practical way, as well as helping
them find a career pathway in which they can thrive.

12. Research skills Analytical skills
R: Designing research and collecting
evidence
A: Undertaking the analysis
R1: Formulating a research question A1: Ability to manipulate, analyse and
filter information
R2: Deciding what evidence is needed to
answer the question
A2: Ability to interpret and synthesise
information using qualitative and
quantitative research methods and
appropriate technology
R3: Determining how evidence can be
collected
A3: Detecting partial or ambiguous
information
R4: Understanding the ethics of undertaking
the research
A4: Understanding the consequences of
using unreliable data and information
sources
R5: Organising the information, selecting
relevant information and identifying gaps in
the evidence
A5: Drawing conclusions based on
critically assessing the evidence and
findings
A6: Appreciating the need to be open-
minded and reflect on the evidence-
base and conclusions drawn
Top seven professional skills (PS)
sought by employers LinkedIn (2018),
McKinsey (2019)
PS1 Communication
PS2 Collaboration and teamwork
PS3 Time management
PS4 Creativity
PS5 Persuasion
PS6 Adaptability
PS7 Networking
Table 7.2 Carter (2021) p. 167

13. My Analytical and Research Skills Personal
Development Plan (MARS PDP)

14. My Professional
Skills Personal
Development
Plan (MPS PDP)
• Example filled in by one of the
PhD case study students in the
book

15. Developing
reflection
as a
practice
Beginning, middle and end of the
Data Fellowship
Collect on Google document, private
to me and the student
Scaffold the reflection using the
framework discussed

16. Reflection 1

17. Reflection 2

18. Reflection 3

19. Self-reflection and Posters all
available at
www.manchester.ac.uk/q-
step/student-stories/

20. Reflections: Ana
• ..I wanted to learn more about
programming. But I've never really
considered a long-term career in technology
and data analytics. And now I feel like I have
the confidence to consider jobs in tech or
data analytics, jobs in research, and maybe
jobs in programming. … the data fellowship
has been overall very positive because I had
the opportunity to meet people my age with
the same worries and aspirations, who were
doing very interesting projects as well.

21. Reflection
Giorgia
I can confidently say that being a Data Fellow
matched my initial expectations – which were mainly
improving confidence and skills in a STEM sector and
getting work experience in a positive and inspiring
environment – and greatly exceeded them
I’ve always been fascinated by the idea of pursuing a
career as data analyst, but I have always felt a bit
intimidated by the fact of being a foreign-speaking
young girl … my fellowship experience has hugely
helped me to break this confidence barrier and
motivated me even more to pursue this kind of
career, given how much I’ve enjoyed my job

22. Reflections:
Confidence
gained
through
experience
Having a mentor is one of the perks of this
internship. My mentor and project leaders
helped me build my confidence up – talking
to them and completing various tasks
I feel a lot more confident in myself, my
degree and my abilities …... the skills you
have and learn are way more important
than the title of your degree
I am a lot more confident compared to the
beginning. I have a belief in what I am doing
… it is experience for me [and] my work is
useful to the organisation

23. Alumni stories
https://sites.manchester.ac.uk/sossteachlearn/2022/07/25/pathways-into-policy-social-
science-alumni-stories/

24. Reflecting
on the
outcomes
• Why 70:30 split F:M?
• Co-producing a questionnaire with the Data
Fellows to explore this
• What do we mean by ‘Data Skills’ anyway?

25. Further
research and
next steps
What to measure – Positive graduate destinations?
Salary levels? Data skills? Confidence?
Interviews with alumni and employers
Next book ‘On becoming a woman in data’

26. Gracias – and recommended books and resources