Data Scientist Internship Programme
Data Scientist Internship Programme
Data Science for Public Good
LIDA launched its Data Scientist Internship Programme in September 2016, as part of its commitment to developing data science capability and driving multidisciplinary data science for public good. Now in its fifth year, the Programme currently has fourteen talented interns, drawn from a wide range of academic disciplines and backgrounds, who are passionate about finding data science solutions to complex, real-world challenges. They are currently working alongside researchers from across the University on a variety of projects.
The Internship Programme provides the opportunity for interns to:
- Own delivery of a data science project and get hands-on experience using real data
- Establish links with project partners and work to provide solutions to real world challenges
- Build skills and knowledge in advanced analytics
- Participate in on-site and external training opportunities in statistical analysis, visualisation, research methods and computer programming
- Work alongside leading scholars as part of the LIDA research community and gain valuable work experience
Meet this year's cohort of interns (2020/21)
Rosalind Martin
Academic and personal background
I completed a BSc in Geography (with a year abroad) at the University of Sheffield. Whilst abroad at the University of Turku, Finland, I was introduced to spatial data and data visualisation in more depth than in my first two years of undergraduate study. This experience heavily influenced my choice of undergraduate dissertation topic. I have recently completed a Masters in Geographical Information Systems at the University of Leeds. It was here where I was properly introduced to programming and solidified my love for data.
What do you hope to get out of your internship?
At LIDA I hope to further my programming skills; learning good practice for writing programs and compiling scripts that enable me to work with larger, messier, and more challenging datasets. During my time at LIDA I’m looking forward to having the freedom within my projects to ask challenging questions of the research and data I am working on. At the end of this internship I am confident that I will have improved in my programming, data visualisation, presentation skills and much more! Through LIDA’s partnerships I also hope to gain more insight into data science as a career.
Where do you see yourself in 12 months’ time?
Upon completion of this internship I hope to be launching into a (perhaps spatial) data science career where I can continue to grow and learn whilst working on projects that have a positive impact.
Sam Relins
Academic and personal background
I am a graduate of the university of Sheffield with a BA in Accounting & Financial Management & Mathematics. After 5 years working for a leading UK stockbroker, I found myself growing restless and looking for a new challenge. Inspired by the vast library of information and learning available online, I began to teach myself programming and basic computer science. This quickly led, via my innate interest in mathematics, to Data Science and to the fascinating world of Machine Learning. I became somewhat of an obsessive, and began taking part in Data Science challenges hosted on the Kaggle online community. This culminated in my being awarded a “community contribution award” for my solution to the Covid-19 Open Research Dataset Challenge and from there to the LIDA internship scheme!
What do you hope to get out of your internship?
Data Science offers us a toolkit to make amazing discoveries and solve some of society’s most challenging problems. I’m excited to develop my skills and experience in the unique environment that LIDA offers, working with experienced academics and industry partners, and generally to be around folk as enthusiastic about data as I am! LIDA’s commitment to making a difference in society is fantastic and I hope to see an impact from my projects in the wider community.
Where do you see yourself in 12 months’ time?
I’ve yet to single out one specific area of interest from the vast domain of Data Science. I plan to use this year to explore new disciplines and find the best fit for me, given my skills and interests. Primarily, I hope to be working in an area that has a positive social impact, whether that be a position in industry or continuing to work with LIDA in one of their PHD programmes.
Alex Dalton
Academic and personal background
I am a University of Leeds graduate with an Integrated Masters in Mathematics. I spent my third year studying at the University of Adelaide where I started picking up programming languages in my spare time. In my final year I focussed on mathematical modelling, including a Masters project on ‘modelling animal movement’.
Last year I took part in ‘Fraudwars’- a hackathon by Simudyne and Barclays at my University. Fraudwars introduced me to agent-based modelling, through which I discovered the field of data science. Data Science is applicable to everything, making it a fascinating discipline to me.
What do you hope to get out of your internship?
LIDA’s Internship Programme struck me as unique. LIDA’s role as a research hub means they have access to a diverse set of data sources. Using this plethora of data to address real-world issues makes the opportunity special. It is an eye-opening experience to work on such projects first-hand. The Internship Programme unites disciplines in the work LIDA does, and within each cohort of interns. This diversity offers a knowledge rich and collaborative environment. I am keen to take advantage of all the learning opportunities available at LIDA. I hope to explore areas of data science that are novel to me, such as Urban Analytics and GIS.
Data Science is one of the most powerful tools for innovation. I am interested in its use to drive forward-thinking solutions for humanitarian and environmental aims. LIDA’s research achieves this. Seeing the projects completed by previous interns and LIDA’s use of data for public good is what excites me about the internship.
Where do you see yourself in 12 months’ time?
I see myself working, ideally abroad, as a data scientist for a non-profit/public sector organisation. I imagine the Internship Programme will further inspire a specific area of data science to explore.
Harry Tata
Academic and personal background
I’m joining LIDA after finishing an Integrated Masters degree in Natural Sciences at the University of Leeds. During my degree I relished tackling projects with my “data scientist hat” on. For example I modelled polymer conformations with Monte Carlo simulations in my Masters research project, and investigated living conditions in a demolished Montréal neighbourhood using GIS software while studying in the city.
What do you hope to get out of your internship?
In a year where the world has been gripped by a pandemic, the power of data science to inform policy and investment is more evident and essential than ever. I am extremely proud to have the opportunity to conduct impactful, beneficial research at LIDA, and to contribute in some way to the response to COVID-19.
I am also excited to make the most of the supportive environment and wealth of expertise at the Institute, in order to strengthen existing skills and develop new ones. Through the training courses on offer, by challenging myself to push my research further, and by working alongside academics with such a breadth of experience and insight, I am sure I will come out of this internship a more complete and effective researcher.
Where do you see yourself in 12 months’ time?
Before starting my role here I looked into PhDs and careers in the public and private sectors. My time at LIDA will build skills and experience helpful along any of these paths, so in a year’s time I expect to have a clearer idea of which options are right for me.
George Breckenridge
Academic and personal background
I joined LIDA having recently graduated with a First-Class degree in BA Geography from the University of Cambridge (Fitzwilliam College). Whilst this course regularly used data to enlighten social and geoscientific research, it was actually time spent at UCL in the Bartlett Centre for Advanced Spatial Analysis (‘CASA’) that really brought my attention to the ways in which big data and data science are revolutionising the study of quantitative urban geography. A two-month placement with Dr Sarah Wise in July-August 2019 at CASA was pivotal in turning my interests to research in the computational social sciences, where I believe we can offer a lot to drive geographical insight in the interests of public good.
What do you hope to get out of your internship?
I’d really like to strengthen my abilities in Python/R programming, as well as improve my understanding of the mathematical basis for advanced statistics. There is no substitute for learning this set of scientific skills outside of expert supervision on real, data-driven research projects. The opportunity to do so alongside a talented cohort of fellow peers, in response to LIDA’s high-profile project partners, alongside the guidance of leading LIDA academics, is a degree of exposure to high-calibre research that I believe I’ll find incredibly valuable as a young scholar.
Where do you see yourself in 12 months’ time?
Probably just about to embark on an MSc programme in urban analytics. I’m open to where my interests might lead in the future but I think the pursuit of a PhD programme in a similar field is where my mind is currently heading. For the foreseeable future though, I hope that in 12 months’ time, like many others, I’ll just be happy to see the back of the Covid-19 pandemic, and feel incredibly grateful that I had the LIDA Internship Programme to advance my career experiences during what will have been a difficult time for us all.
Ben Cooper
Academic and personal background
I applied to the LIDA internship following my PhD in which I studied statistical approaches to quantifying individual behavioural variability in desert locusts. I enjoyed exploring how we can turn the trajectory of a locust moving around a space into a meaningful description of the animal’s behaviour. How can we use these variables to measure how individuals differ from one another? Or how flexible are individuals in different contexts, or over time? What does this mean for how animals adapt their behaviour to changing environments? And how do we best generate interest in these findings to people who don’t know about locust behaviour? Answering these questions led me to develop a real interest in the process of data science: analysing data, visualising the results, and communicating this effectively to non-specialists.
What do you hope to get out of your internship?
I aim to develop a greater knowledge of data science processes, especially outside my background in behavioural ecology. I want to be able to apply the quantitative and programming skills learned during my PhD to more real-world cases, where my work can have a much more tangible impact and ideally show the more interesting or accessible sides of data analytics. Finally, I want to build my experience of working alongside researchers in a range of different disciplines.
Where do you see yourself in 12 months’ time?
I hope to have developed my own skills further, started to develop a network of contacts, and figured out which areas of data science interest me the most. I would also love to get involved with outreach or teaching, as I am passionate about open and accessible data science practices.
Millie Wagstaff
Academic and personal background
I have always been interested in the relationship between brain and behaviour. I pursued this interest by studying an MSci in Natural Sciences specialising in Neuroscience at the University of York. Here I was able to explore neuroscience at the level of synapses, neurons, brain networks and behaviour. I became particularly interested in using neuroimaging techniques to study both the structure and function of the brain. For my dissertation I combined functional magnetic resonance imaging (fMRI) with transcranial magnetic stimulation (TMS) to investigate the neural basis of face perception.
Alongside my studies I worked as a research assistant where I established a keen interested in Data Science. I developed programming skills in R and Python and knowledge of statistics and machine learning techniques. My work included visualising human movement within virtual reality environments and using electrocardiogram datasets to investigate the physiological correlates of fear. I am now fascinated by how we apply data science to answer important scientific questions about the brain. I am particularly inspired by research that combines machine learning with brain imaging tools to create better diagnostics for mental illness.
What do you hope to get out of your internship?
During this internship I hope to obtain insights from data that will have a positive impact on people’s lives. I am particularly keen to explore how Big Data can improve healthcare. I would also like to advance my skills in R and Python and work with software and programming languages I am less familiar with like SQL and Tableau. Finally, I am really looking forward to working within a multidisciplinary team. I hope to gain insight into new disciplines and help produce exciting research at their interface.
Where do you see yourself in 12 months’ time?
In 12 months’ time I hope to be continuing using data for public good. I would love to unite the skills and experience learnt over this internship with my interest in neuroscience. I think data science has huge potential to accelerate neuroscientific research, particularly in our understanding and treatment of psychiatric disorders.
Sebastian Heslin-Rees
Academic and personal background
I join this internship from a social science background after completing a BA (Hons) in Human Geography at University of Leeds. However, I have always had a strong interest in the quantitative side of the social sciences, focusing on how the power of data can be used to understand and address complex issues. For example, my thesis examined the distribution of youth service funding reductions in London since austerity, and its impacts on communities, largely focusing on crime. Additionally, I have also worked with the Department of Education at University of Leeds, to help understand and map inequalities in Core Maths uptake across the UK. Furthermore, I completed an internship with the data analytics and location planner organisation, GMAP Analytics, assessing inequalities within the UK’s electric vehicle utilisation. I was immediately drawn to LIDA as an organisation, given their fusion of high-level data analytics with their ethos of delivering solutions to pressing social problems.
What do you hope to get out of your internship?
My experiences thus far have highlighted the importance of data-driven social research, and have motivated me to further my development of data analytical techniques, so I can more readily tackle research within the scope of data science for the public good. LIDA offers a unique opportunity to totally immerse oneself in the field of data science and world class research, whilst simultaneously offering a supportive environment for enhancing skills in coding, statistics, public communications, and much more.
Where do you see yourself in 12 months’ time?
In 12 months’ time, I would hopefully be able to use my newly-sharpened skills in coding and statistical analysis to contribute to social science and public policy research, that addresses key issues with an emphasis on inequalities. Whether this materialises in the form of a Masters degree, a PhD, or industry/council research, the skills and knowledge acquired at LIDA will ensure I can move more confidently onto my next step into data analytics.
Maike Gatzlaff
Academic and personal background
I am from a geography background, taking a geospatial viewpoint of societal matters. During my study abroad, I focussed on geospatial techniques and developed a critical thinking about the ethical consequences of technological developments and data analytics. I recently completed an MSc in Geospatial Sciences, specialising in Geoinformation Systems and Computing and am interested in using data to address real-life challenges. My research experience covers diverse topics, from analysing cycling patterns to improve sustainable transportation to evaluating the fitness of purpose of OpenStreetMap data in humanitarian disaster response.
What do you hope to get out of your internship?
I see this internship as an excellent opportunity to develop strong data science skills while working on projects for the public good. I enjoy working on projects that are related to real-life challenges and are aiming to have a positive impact on people’s lives. At the same time, I am looking forward to broadening my skillset, from expanding my R and Python skills to applying machine learning algorithms. Working with external partners and presenting the project findings to the public, I am excited to improve my data visualisation skills to communicate more effectively. By being part of a multidisciplinary team and being exposed to the various backgrounds of the cohort, I am hoping to expand my geographical viewpoint.
Where do you see yourself in 12 months’ time?
I am open-minded about my future career. However, I know that I want to apply the skills learned during the LIDA data science internship to help make a positive impact on communities.
Paul Truman
Academic and personal background
My first real encounter with Data Science was during my BSc Physics dissertation, where I modelled the optical phenomena of the “Walkie Talkie Building” in London. The skyscraper’s south-facing, concave face resulted in the Sun’s rays reflecting into a focused beam which was hot enough to warp car panels in the street below.
Underneath this Physics problem was the process of gathering disparate astronomical and architectural data into a single model – which was very much a Data Science task in essence. After this, I decided to undertake an MSc in Data Science, with my final choice being at the University of Leeds – the deciding factor being the links to LIDA and the fantastic work being done here.
During my MSc thesis I modelled air pollution in Leeds, with the work aiding a previous intern’s project in linking air pollution to health problems in the city – which led me to finding out about the internship.
What do you hope to get out of your internship?
One thing I’ve learned from my first week here is the amount of training & courses we have access to as an intern; so making the most of these resources to expand my skillset is a top priority for me. Coming from a more academic background, I want to use the opportunity to apply my knowledge in practical real-world settings.
Where do you see yourself in 12 months’ time?
Whether I’m in industry or academia, I feel confident that my time here will prepare me for whatever I decide to do. I’ve noticed that no matter what the previous interns are up to at the moment, they’ve still made time to help the latest cohort bed in – a fitting testament to the communal spirit here, and something I would also love to do.
Simon Leech
Academic and personal background
I completed a BSc in Geography at Lancaster University, which included a dissertation assessing societal views towards the use of white roofs as a method to mitigate urban heat islands. I then undertook an MSc in Geographic Information Systems at the University of Leeds. It was during my time at the University of Leeds that I started to focus upon data skills and coding. I produced agent-based models in Python and spatial microsimulation models using R, alongside GIS work that showed the value of data visualisation to unlock new insights.
What do you hope to get out of your internship?
I firmly believe that data science should be accessible, so I am very excited to work on projects that foster engagement across different stakeholders. After all, we all create and use data every day, so everyone should feel empowered and inspired by how it can be utilised. Working in a multidisciplinary environment that fosters collaboration for public good is something I know I will enjoy, particularly being able to learn from people from so many different backgrounds and skillsets. From this, I hope to improve my coding ability and learn new technical skills. I am also keen to push myself out of my comfort zone using the numerous opportunities LIDA provides, such as the training into public speaking.
Where do you see yourself in 12 months’ time?
Honestly, I don’t know the answer just yet! I am keen to explore different avenues, both academically and commercially, and will remain open-minded. However, I know the LIDA internship will provide a fantastic opportunity to do this, and I know the skills I learn here will better prepare me for whatever the future holds!
James Lazarus
Academic and personal background
My life in academia started with a BEng in Mechatronics and Robotics Engineering, where I developed my interest in computer science, problem-solving and AI. After completing my undergrad, I decided to pursue my interests by taking an MSc in Data Science and Analytics, where I learnt the foundation of my data science skills. I completed two main full data science projects during this year. The first being an investigation into the causes of car crashes in the USA; the second was my thesis on the continuous prediction of demand and supply of radiology services. Thus my love for the subject grew, and I stumbled on the LIDA internship, which struck me as the perfect opportunity to carry on pursuing data science.
What do you hope to get out of your internship?
During this internship, I would like to build upon my current knowledge in data science and learn new skills in data science that are relevant in industry. I hope to also gain hands-on experience with real data, as well as employ the data science skills that I have and the ones that I will learn.
Where do you see yourself in 12 months’ time?
I hope to be in industry further developing my skills or carrying out a PhD. However, I want to be using the skills I have learnt during my time at LIDA, and working on data science projects that interest me, but I am unsure as to what the future will hold, I can only hope it’s something good.
Stuart Ross
Academic and personal background
My name is Stuart, I am 24 years old and am from a small town in Connecticut, USA. After completing my BSc degree in Ecology from the University of Queensland in Australia, I spent the next year working as a Marine Ecologist. I quickly took advantage of the hole in the field when it came to skilled data analysts and software engineers, but I wanted to push my skills beyond that of just marine ecology. I took my passions further by completing an MSc in GIS at the University of Leeds where I got great hands-on experience working with Machine Learning and Big Data. Since then I have completely fallen in love with Data Science and intend to pursue a career in it.
What do you hope to get out of your internship?
Besides getting a chance to improve my coding and machine learning skills, I am excited to really dive into my own long-term Data Science projects. These projects will give me the chance to work through the whole Data Science process, from data acquisition, to cleaning, to determining the best model, and finally presenting my work.
I am also excited by the fact that LIDA is the perfect stepping stone between the academic and industry worlds. I know that my time as an intern will allow me to work with industry project partners, while still having the academic feel of a university setting.
Where do you see yourself in 12 months’ time?
I hope to take my Data Science skills into industry. I want to work on larger more complex problems where I can further improve my abilities and skill set. I will be happy wherever I end up as long as I can continue to grow and learn!
Tom Albone
Academic and Personal background
I recently joined LIDA to pursue my interests in data science and healthcare research. Since completing my undergraduate degree, I have worked in child protection services, completed a PGCE in Primary Education and studied part-time for an MSc in Geographical Information Systems from the University of Leeds. My industry experience includes developing flood maps for high resolution digital terrain models and working as an analyst in healthcare research, where I used a range of technologies to measure and interpret patient, staff and service user experience across different sectors. I have discovered a real passion for projects that use data analytics to empower individuals or communities and support key policymakers with decision making.
In my spare time, I love distance running, completing several marathons and ultramarathons. Making healthy choices has always been important if I want to continue taking part in these events. I relish opportunities to synthesise my passion for this with data science to help answer questions about the links between public health and lifestyle choices.
What do you hope to get out of your internship?
Ultimately, I hope to enhance my existing industry experience of data analytics and research with the extensive knowledge base at LIDA. I am particularly keen to combine my experience with GIS and data analysis with more advanced techniques, such as machine learning, to expand my horizons of what data science can achieve and how it can be used for the benefit of society.
Working with public and private healthcare sector clients has been extremely rewarding and I am eager to develop a broad toolbox of analytical techniques and programming languages, such as Python and R, to meet the requirements of bespoke research projects. Furthermore, I would also like to develop a wider network of professional contacts through the learning and development opportunities available at LIDA.
Where do you see yourself in 12 months’ time?
I am a big believer that success comes from achieving lots of small goals, one step at a time. It is an approach that has helped me develop positive relationships and gain valuable experience in prior employment. I will spend the next 12 months adding to my data science skills and cultivating a broad network of professional relationships so that I can take full advantage of any opportunities, whether I continue in academia or return to industry. I would love to work for an organisation that uses data science to help shape public health policy through innovative research methods.
Examples of Intern Projects 2019/20
Claire Shadbolt
Title: Simulating capability to reduce transport energy emissions and associated social vulnerabilities
Abstract: There is an urgent need to decarbonise the transport sector to meet the UK’s climate change agreement target. Although current research has indicated that this is achievable through increasing the uptake of public and active transport and the electrification of the sector, there is still a necessity to reduce the total energy demand in this sector. It is important to understand where people can reduce their transport emissions and demand for transport.
However, individuals have different capabilities in terms of adapting to a new transport mode. Factors like health, car ownership, economic status, and location can impact an individual’s transport choice. Therefore the inequalities and vulnerabilities that might arise through transport emission reduction policies must be evaluated and understood to identify an equitable solution.
For this project, travel behaviour and health datasets will be used to construct an individual and a household level spatial microsimulation that will calculate the capability of individuals and households taking-up low-emission transport modes. It will also assess the effect a policy might have on a population in a small area, identifying groups of people that might be vulnerable. The results of the project will be useful to policymakers who are interested in decarbonising transport in an equitable way.
Caitlin Chalk
Title: Using Rasch measurement theory to guide consumer product design
Abstract: The personal opinions and attitudes of consumers towards the products they use are of fundamental importance to manufacturers. Data obtained from consumer surveys are typically employed to aid the development and design of products. However, such data are difficult to interpret. How can people’s emotions regarding products be effectively measured? Using Rasch measurement theory, ordinal level data (i.e. survey responses) are converted into interval measurements. This allows descriptive statistical analyses of consumer data, providing valuable insight into the consumer’s sentiments and attitudes. In particular, Rasch theory enables the relationship between consumer responses and the product features or formulation to be modelled, which is used to inform the design of new products. These models provide rich information about each participant and question in a survey, leading to improvements in the design and administration of consumer research.
In this project we are collaborating with Procter and Gamble – one of the largest multinational consumer goods corporations – to analyse their consumer data using Rasch theory. The methods used in this project have the potential to assist manufacturers worldwide in the optimal design of new products.
Maab Ibrahim
Title: Natural Language Processing (NLP) for NICE symptoms extraction from Health Records
Abstract: Electronic Health Records (EHR) are the medical notes that doctors use to describe their patient’s information in an unstructured textual format. Similarly, for cancer diagnosis, doctors use EHR to present the signs and symptoms that are identified to be critically related to the management of patients suspected of being diagnosed with cancer through their referral pathway. In this project I am using these EHRs to apply text analytics and construct Natural Language Processing (NLP) model. This model will automatically extract and map the NICE cancer symptoms from Electronic Health Records (EHR). The medical notes sublanguage traits are analysed and Computational methods are applied to the medical data in order to identify the probability of a patient being diagnosed with cancer. The project is an augmentation for the PinPoint Early Cancer Detection data science project and applied on the NHS medical data.
With the vast growth in the availability of medical data and the rapid development of data analytics and AI tools and research in the healthcare domain, this project is an effort to aspire to help facilitate in the process of early diagnosis of cancer and cancer reoccurrence identification, by helping the doctors improve medical decision making to improve healthcare quality, as well as supporting cost reduction in the cancer diagnostic process.