UK Data Service Workshop 28 Sept 2016

1. From the classroom to the
workplace: how data skills develop
better social researchers
Jackie Carter @jackiecarter
@UoMQStep

2. 20 top tips from DataFest2016
1. Be organised
2. Show creativity
3. Take calculated risks
4. Stretch yourself
5. Be a data champion ‘pay it
forward’
6. Be prepared
7. Be different
8. Be resilient
9. Demonstrate your
experience
10. Don’t suck
11. Be positive
12. Be observant
13. Ask questions
14. Keep connected
15. Be yourself
16. Don't be afraid of data!
17. Have confidence in your
abilities
18. Practice makes perfect
19. Benefit from those more
knowledgeable
20. Check, check and check

3. Applied learning: Q-Step internships

4. The Q-Step internships
3 cohorts (2014-2016)
• It’s competitive
• End of 2nd year
• 8 week long data-driven
research project
• Must be quantitative
• Public, private and
voluntary sector
• Paid at living wage
Applied learning
• Descriptive statistics to
statistical modelling
• Data-driven
– Data cleaning
– Data collection
– Data analysis
– Report writing
– Presenting
• Celebrate learning in the
autumn

5. Three years of Q-Step internships
0
2
4
6
8
10
12
14
2016
2015
2014
(47)
(48)
(19)

6. Sociology student posters

7. Where are they placed?
• National government
departments
• Local government
• Polling companies
• Market research
• Social research
consultancies (incl. first
international)
• Public research libraries
• Charities
• Banks
• International Statistical
Organisations (The
World Bank)
• Think tanks
• Digital agencies
• Social enterprises
• Universities

8. 3 case studies
• All studied Sociology
• All took the Survey Method in Social Research
module
• All did internships in 2014
• Varying degrees of maths/stats background
• Interned in different types of organisations

9. • Placed in Manchester City Council’s Age
Friendly research team
“I liked that it was a vocational way of using social
sciences and at that point I was thinking about what
I was going to do with my degree”

10. • Had avoided studying statistics as he’d found it ‘a bit
scary’
• “We didn’t do a lot of analysis…it was a good
opportunity to learn about some of the difficulties in
working with secondary data. It led to quite a few
problems about the comparability of data”
• “There’s a lot of sociologists but not a lot with good
quants skills. I feel more discerning now when it comes
to statistics and numbers.”
• Described data analysis as “how to use methods to
solve social puzzles.”

11. • Placed in Integrity Research Consultancy
(London) undertaking research in fragile, post-
conflict countries
“I thought it would be very well suited to what I can actually
do and what would be important for me to learn, unlike some
big companies who put you into a predetermined role that I
may not necessarily know anything about.”

12. • “I worked on a big body of qualitative interviews … and I
became the go-to person for sampling techniques….the things
I worked on were more related to methodology and design
than actual analysis”
• “doing these things, the sampling, the methodology, that’s
what I want to be doing for my career, ..…. and knowing I have
a sector I am passionate about and doing something I’m good
at and something I can get better at is good to know ”
• “I’ve always thought it’s good to be good at maths, it helps
you, not necessarily applying the maths but the thinking
involved”

13. • Placed in academic unit in University College
London: Centre for Sexual Health and HIV
Research
“I thought it might be fun to do an internship, have a job; it
wasn’t the statistics or quants that drew me in, it was more
going somewhere to work as I’d never done that before. ”

14. • “This internship was my dream. I saw it and thought ‘yes this is the
perfect internship’ as I love studying sex and sexuality”
• “I did simple crosstabs, regression, measured change over time, so
I had to combine the two datasets which was incredibly difficult. It
took me weeks. And I never want to do it again but it was
satisfying.”
• “It’s not something to be scared of and it’s good practice”
• “which seems scary but actually it’s not. And it took me a couple of
days to get my head around it … but I wasn’t frightened [On
learning to use Stata and the command line]
• “You don’t have to be brilliant at maths. If you’re fairly logical in the
way you think it will make sense to you. Even if you have to read it a
few times.”

15. The employers (2014)
“I don’t think there were any analysis tasks that we assigned her that
she wasn’t able to perform to a high level of quality. The concepts of
doing the academic theory and then trying to put it into practice in a
company like ours is exactly what the balance should be to prepare
somebody for the workplace and apply it commercially”
– They went on the take on more interns and pay them living wage
“ from their applications we were not expecting them to be as strong
as they actually were. I was told don’t expect them to come and do
much complicated analysis and then they came and they really could.
So all of this is analysing Natsal, and it’s complex. And they whizzed
through [what we’d planned]. They were really keen, they were really
interested in adjusting for confounders and thinking about them ..
They picked that up really quickly”
– Both students went on the be co-authors on academic papers

16. What they did next?
• Pete – completed Master’s in Social Research Methods
and Statistics just started a PhD in social statistics and
survey methods
• Bella – working for a commercial company in Big Data
analysis and now studying on Master’s in Social
Research Methods and Statistics
• Natassia - completed Master’s in Social Research
Methods and Statistics and hopes to move into social
research (possibly in the US). Co-authored an academic
paper.

17. Anna ..…

18. Key themes
• Attitude to maths
• Motivation for applying
• Eager to be stretched and challenged
• Confidence develops
• Appreciation of quantitative methods in the
workplace and/or research as a practical skill
• Provided a stepping stone for all 3 students

19. Challenging the skills deficit narrative
• Our students demonstrate creativity, passion
for their subject, willingness to learn,
eagerness to apply knowledge
• The ‘skills gap’ mantra is not always helpful
• We need to show how they can do data
analysis starting often from a low base
• Embedded and applied learning is fast
becoming a successful route to securing good
students at graduate level

20. Q&A – ask away