Code Division

Women in Tech: Talent Pipeline

Overview

There is a massive digital and data skills gap in Scotland and an issue with women being under represented in tech. This project intends to look at the traditional routes into tech; schools, colleges and universities and whether there are issues or obstacles to girls/women accessing these courses.

School Data

As a team, we decided that individuals would be responsible for specific areas of the project. Neha and Ryan were responsible for collating the school data. Once we received the data, we used both Excel and Python to clean and manipulate the data.

The data sets used are from SQA https://www.sqa.org.uk/sqa/57518.html. Following national qualifications are used for data analysis:

  • National 5 (SQCF Level 5)
  • Higher (SQCF Level 6) 
  • Advanced Higher (SQCF Level 7)

Years chosen The statistics used are from year 2015 till 2019. There were no exams conducted in 2020.

Using the data received, we created a number of data visualisations for Nat 5, Higher and Advanced Higher from 2015 to 2020. The two visualisations shown here are for Nat 5 2019 and Higher 2019. Other visualisations and projects can be found in our GitHub detailed further down the page.

In the Nat 5 table, Computing Science has the 5th largest gender imbalance for Males/Females and the highest of any major subject. In the Higher table, Computing Science is the 2nd largest and Physics is 3rd. This is important since University entry is sometimes dependant on Computing Science and Physics. These 2 subjects are often asked for entry into University tech courses.

We also looked to see if there was a difference in drop off rate for Females for Nat 5 to Higher. What we found was that there was no significant difference in Computing Science between Nat 5 and Higher. We are now investigating if there is a significant difference between voluntary drop off (ie Students pass the Nat 5 and choose not to do Higher).

University Data

Kelsey was responsible for the University data.

Some issues with the analysis
A new subject coding system – the Higher Education Classification of Subjects (HECoS) – was been implemented from 2019/20. This replaced the old system – the Joint Academic Coding System (JACS). Also the datasets are for enrolments to Scottish Universities regardless of the students domicile, i.e., where they consider home. This means students from other parts of the UK and abroad are included in this analysis.

Analysis

Computing has the fifth largest number in enrolments, behind Engineering and techonology, Social sciences, Subjects allied to medicine, and Business and management. Despite this, females only made up 18.82% of total enrolments in Computing. Similarly, only 20.57% of students enrolling in Engineering and techonology were female.

For more data on enrolments, part time and post graduate courses check out my GitHub.

The Plotly visualisations in two of the notebooks can’t be rendered in Git because they aren’t static. Here’s the links for viewing them in nbviewer: Scottish Universities Jupyter Book and Computing Enrolments Scotland.

College Data

Ester Was responsible for the college data. 

The data is taken from statistics held by the Scottish Funding Council. To access the data, use the Infact Database of students and courses at Scotland’s colleges.

The trends over the last 5 years show a slight increase in Software Development and fluctuations in the number of students in Web Development. As Technical Support decreased in numbers, Networking initially increased and is now also decreasing.

Analysis

Only 10 Colleges in Scotland offer HND Software Development and only 6 Colleges offer Web Development. This means that for large areas of the country the 2 most relevant HNDs for gaining employment in the Tech sector are not available to potential students. In 2021 there are currently no Colleges offering Data Science either at HND or PDA level.

The statistics for females in HND courses was so low that the figures were rounded up to 5 in a sizable amount of cases. In order to estimate female students we subtracted the number of male students from the total students.

Over the last 10 years women have been under represented in all of the HNDs in Colleges. Web Development on average has performed better with 20% of the students being female. The worst have been in the technical support Networking which has averaged 6-8%. In 2016 the SFC developed an Action plan to address the gender gap in Computer Science in colleges and universities. 

Group Members

neha

I was always scared of developing scripts for automation. It was generally the guys in my team who did it. Since completing the course I have already automated some of the project work using the skills I learnt.

I tried few Python courses on LinkedIn before but I did not feel the excitement to continue them. This course provided classroom like structure, peers to talk to, platform to ask for help, get feedback from experts. I was only booked for the Python course but Frank persuaded me to do both Python and Excel and I am so grateful for that.

One negative thing after doing this course is I have lost trust in numbers presented because I am now aware how easy it is to tweak the numbers, charts to prove ones’ point. It has made me more aware how much misinformation could be there in our daily lives and I do make my family aware too! So, double bonus.

 

Neha Jain

I have a degree in Biology, but I never found a job in science. I was told that they were not for me. Code Division showed me that women can be data analysts and/or programmers.

During the PDA, I was the project manager of my team for the project “Women in Tech” and I was able to use my project management skills. All together this gave me the necessary confidence and experience to pursue a career in data analysis.

I have landed a new job as Senior PMO in an international IT/Data company in Glasgow. This PDA has played a significant role in obtaining this position and I thank Code Division for that.

 

Ester Gimenez

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Image

I completed a BSc Biological Sciences degree in 2018 and wasn’t sure what I wanted to do after graduating. I applied to jobs and was hired as a data assistant for the Edinburgh Transplant Centre, Royal Infirmary of Edinburgh. My main responsibilities were data entry and extraction, but the role has evolved into a more analytical one which I really enjoy.

I enjoyed making visualisations and reports to tell the story of what subjects’ women are studying at Scottish universities. I also enjoyed discussing ideas and collaborating with my teammates and mentors.

I’ve recently been made Data Manager in my team with the aim of making use of more automated reporting and creating cool and informative visualisations with Python. I hope to continue my data learning journey by taking data management and AI courses.

 

Kelsey Pearson

I have previously studied Information Technology and Computing, with specialisation in networking, through the Open University several years ago but I have never worked in the field. I currently work within a company producing stone worktops, where I create production drawing and program CNC’s.

I saw this data science course as an opportunity to build on my existing qualifications and possibly progress to a change of career. I particularly enjoyed the python aspects of the course. The bite sized steady progression gave a feeling of real progress and satisfaction when manipulating the data.

I am awaiting now waiting for a training start date for the fire service. Although I am unlikely to work in a data science environment, I still consider this course to have been worthwhile and given me a greater appreciation of data.

 

Ryan Murphy

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How we did this

Key Findings

Schools

  • At all levels Computer Science is near the top for gender imbalance.
  • Higher Computer Science has the second largest gender imbalance.
  • Higher Physics has the third largest gender imbalance.
  • Data indicates girls perform better; pass (A to C) and pass (A) at National 5.
  • Proportion of girls taking Higher decreases.
  • Indicating a significant voluntary drop off among girls compared to boys from Nat 5 to Higher.

https://github.com/EsterGM/Women-In-Tech/blob/main/final_computing_in_schools/CS_in_schools.ipynb

https://github.com/EsterGM/Women-In-Tech/tree/main/SQA_datasets

Colleges

  • 10 Colleges in Scotland offer HND Software Development
  • 6 Colleges offer HND Web Development.
  • There are currently no Colleges offering Data Science either at HND or PDA level.
  • In 2019/2020 for the whole of Scotland there was 63 female students on HND Software Development
  • In 2019/2020 for the whole of Scotland there was 33 female students on HND Web Development
  • The overall offering of HND courses in Scottish colleges does not reflect the current demand for tech skills.
  • https://www.scotlandis.com/scottishtechsurvey/
  • There are large areas of Scotland where students cannot access HND courses in Computing.

https://github.com/EsterGM/Women-In-Tech

Universities

  • Female students make up 18.82% of total enrolments in Computing.
  • 57% of students enrolling in Engineering and technology were female.
  • Percentage of females enrolling in part-time Undergraduate Computing subjects is higher than in full-time Computing courses (31.54% vs. 18.82%).
  • Percentage of females enrolling in full-time postgraduate Computing courses is higher than the percentage of females enrolling in full-time undergraduate Computing courses (18.54% vs 31.80%).
  • Most courses ask for Higher Maths, with some specifying the grade.
  • 11 out of 25 courses ask or recommend Computing be included.
  • A number of universities ask for either Maths or Physics.
  • Computing and Physics have the second and third largest gender gap at Higher level.

https://github.com/kjp07/Women-In-STEM/blob/main/code/computing_entry_requirements.ipynb

What we would like to do

Ester put in a request for entries for Computer Science broken down by region. Along with the numbers in Colleges we are going to create a folium regional map similar to the one showing Scottish Universities. This Data visualisation will help to show gaps in specific areas not covered by School, College or University courses.

We would also like to investigate in Schools why girls are not choosing Computer Science. A quantitive survey of S1/S2 or even P7 on what they think Computing Science is would help us understand better its unpopularity with most girls.

We would also like to look at the data when it comes to apprenticeships (Foundation and Modern) to see if there is similar patterns in gender imbalance.

In 2016 Scottish funding council had a gender action plan for Scottish colleges and universities, we would like to examine the data more fully to see if this had any impact on gender imbalance.

Action Plan Link