Data Scientist Internship Programme
Data Scientist Internship Programme
LIDA launched its Data Scientist Internship Programme in September 2016, as part of its commitment to developing data science capability and driving multidisciplinary data analytics research. Soon to be in its fifth year, the Programme currently has ten 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.
Applications for the fifth cohort of LIDA Data Scientist Internships closed on 17th July 2020. If you missed the opportunity to apply for this cohort, please bear in mind this is an annual Programme and we will be looking for interns again next year.
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 team and gain valuable work experience
“My experience as a LIDA data scientist intern has been great. The internship has given me the opportunity to work in the field that I’m most passionate about, at the cutting edge of cancer research and artificial intelligence. Through my project on biomarker discovery in glioblastoma, I acquired skills and experience that will be of paramount importance in the next steps of my career.”
Stelios Theophanous, former Data Scientist Intern (2018-19) now undertaking a PhD in predictive modelling for cancer outcomes
“I have thoroughly enjoyed my time as a Data Science Intern. This program is exactly what I needed to combine my PhD research skills with industry projects. I am confident I will finish this year perfectly positioned for my next career move.”
Kevin Minors, former Data Scientist Intern (2018-19) now working as a Data Scientist at The Oakland Group
“I completed a LIDA Internship working with Sky Betting & Gaming to develop models to identify problem gamblers. My time at LIDA was essential to my transition from academia to industry. Working in collaboration with academic and industry partners allowed me to develop my understanding of business challenges, while maintaining a solid academic grounding. Having the support of academic supervisors helped ensure my work was rigorous; an ethos I have carried over into my current role as a Data Scientist at Sky Betting & Gaming”.
Ed Berry, former Data Scientist Intern (2017-18), now working in the public sector
Wondering what our interns get up to here at LIDA? You can read a number of blogs from our current and previous cohort of interns below:
- Vijay’s Thoughts on the LIDA Internship Programme
- Holly’s Improving lives through Big Data
- Claire’s Attending the Women in Data conference
- Francisco’s An introduction to the LIDA internship
- Luke’s Secondment to SoCal
- Kevin’s Day in the Life
- Alex’s Life as a Data Scientist Intern
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.
Thomas Richards
Title: Developing simulations of smart cities
Abstract: My project part of the Data Assimilation for Agent-based Models (DUST) project. I am working in collaboration with Connected Places Catapult using data from their ‘smart building’. The building is equipped with sensors that measure things such as CO2 levels, light levels, and noise levels which reflect the internal environment of the building. The aim of my project is to feed these data in real-time into a simulation of peoples’ movements around the building. The simulations we are using are known as agent based models, which are typically run using historical data. These models could be improved with the incorporation of real-time data, a process known as data assimilation. Decisions could be made based on simulations which reflect moment-to-moment changes of the actual internal environment of the building. This has real-world implications for improving the efficiency of management of events within the building.
Francisco Videira
Title: Assessing the presence of e-food deserts in the UK
Abstract: Food deserts are vulnerable areas, traditionally thought of as deprived urban neighbourhoods, in which people experience physical and economic limitations in accessing healthy and affordable food. Vulnerable neighbourhoods usually have low-income households with limited private means of transport and an elderly population. Growth in e-commerce has improved access to grocery retail services for many consumers, though there is an uneven geography to provision. Retailers decide where they deliver to, at what cost (to the consumer) and ultimately influence the quality of service via the availability of delivery slots. Thus, a new form of food desert might be emerging: the ‘e-food desert’.
This project aims to assess the presence of these catchments and what their typical socioeconomic characteristics are. In addition, it also aims to reveal ‘hidden food deserts’ by simulating changes in availability and cost of delivery services. An understanding of the presence of e-food deserts and neighbourhood level vulnerability to changes in delivery availability could support retailers in planning their home delivery services and support policy makers, regulators or local planning authorities in evaluating local retail provision.
Holly Clarke
Title: Modelling the impact of school admission criteria on pupil distribution across Bradford Local Authority
Abstract: Education is a powerful tool in improving social mobility and in Bradford approximately 38,500 secondary school age pupils attend 35 state-funded schools. Each year a new cohort of year 7s enter these schools and their places are determined by schools’ admission criteria. These vary depending on school type and create a complex system which leads to some children not being placed in any of their preferred schools or travelling long distances to attend low-performing schools. In partnership with the Department for Education and Bradford Opportunity Area, this project employs modelling and visualisation techniques to delve into the admissions process and understand the impact it has on pupil distribution across Bradford. Particularly for disadvantaged children, it looks at how admissions policies impact their access to high performing schools.
The aim of this project is to present findings and create tools which stakeholders and policy makers can use to improve equal access to education across the city and improve children’s life chances.
Natalie Nelissen
Title: Low Carbon Cities
Abstract: In response to the UN’s report on climate change, Leeds City Council has committed to making Leeds carbon neutral by 2030. Since domestic energy use accounts for 14% of greenhouse gas emissions, one important action point is to retrofit existing houses to become more energy efficient and/or use renewable energy. In collaboration with the Housing Team at Leeds City Council, we are developing a model to help the Council create a road map for retrofitting their social housing stock. Input data include housing stock characteristics and energy usage as well as planned retrofit activities. The model considers various scenarios, ranging from business as usual to deep retrofit strategies, and reports associated costs and benefits.
Robert Long
Title: Advancing analytics for Police resource deployment
Abstract: Understanding where and when resources should be deployed is essential to the design of effective crime reduction strategies. The last 15 years has seen a revolution in the use of analytics capable of informing such decisions. A major component of this has been the development of predictive policing analytics – techniques that seek to identify areas at increased risk of volume crime (e.g. burglary, vehicle crime) in the near future. While there have been considerable research efforts to develop these techniques, evaluations of their potential effectiveness in UK policing environments remains limited. To date, predictive policing analytics have concentrated on identifying locations at an increased risk of future crime events. The implicit assumption of these techniques is that all crimes are created equal in terms of prospective resource allocation. However, the practical allocation of crime reduction resources is a considerably more nuanced enterprise which requires practitioners to balance multiple constraints including crime risk, seriousness/harm, and resourcing impact of response.
Building on the ongoing N8 PRP Project – Analytics for Resource Deployment, this project aims to develop new predictive policing analytics capable of incorporating event-weighting schema into spatio-temporal forecasts of crime. These techniques will allow forecasted crime risks to be weighted based on a range of crime and harm reduction priorities. For example, serious offences against the person may be weighted more strongly than property crimes. This approach will also produce multiple forecasts; the second half of the internship will explore how forecasts might be combined using ensemble approaches to solve multiple constraints problems. These efforts will support the design and evaluation of new decision support systems that better reflect the diverse demands of police and their partners.
Vijay Kumar
Title: Modelling the Joint Effects of Dense Temporal Datasets on Outcomes
Abstract: Pollution has been linked to many health ailments in the previous years including accelerated aging of the lungs and loss of lung capacity. However, assessing the real life impact of pollution on a large population whilst also taking into account lifestyle factors is a difficult task, this is mainly due to the spatial sparsity of the data that is available. This project aims to tackle this problem by proposing new statistical models to better analyse the effect of air pollution and lifestyle on Chronic Obstructive Pulmonary Disease (COPD) using real data from the Leeds area. COPD is a currently incurable progressive lung disease that has been linked to air quality. By creating these new statistical models we strive to uncover more information about the public health risks pollution can bring, and open the door for further work to be carried out in this area.
Patrycja Delong
Title: Changes in meat consumption patterns in the UK – investigation using loyalty card data
Abstract: Vegetarian and vegan diets are increasing in popularity in the UK. Main motivations behind these dietary choices include health benefits along with concerns for animal welfare and environment. Little is known however, on how people’s overall dietary patterns change when they reduce their meat consumption. Traditional food surveys can provide some information about global trends, but lack insight of changes in behaviour at individual level. Moreover, they suffer from non-response bias and inaccuracy in self-reported food consumption. Using customer transaction data allows us to observe changes in purchasing behaviour of a cohort of households over time.
Within this project we are going to apply machine learning techniques to identify households that reduce their meat consumption and examine the dietary patterns associated with that transition. This research is conducted in collaboration with Oxford University as a part of the Wellcome Trust funded LEAP project (www.leap.ox.ac.uk).
Intern Profiles 2019/20
Claire Shadbolt
Academic and personal background
BSc in Natural Sciences (International) focusing on Chemistry and Maths
MSc in Climate Change and Environmental Policy from the University of Leeds
What do you hope to get out of your internship?
In this era, where every aspect of our daily life is digitised, a large volume of data that has emerged from various data sources. It is fascinating that statistical and analytical techniques can be used to help industries make more accurate and inform decisions. Hence, these techniques can strengthen research and address problems that regularly affect populations. During this internship, I hope to gain a better understanding of the tools and techniques that use real world data in this way. Improving my code writing skills in R and Python, while also learning new techniques like GIS and Tableau.
Furthermore, the internship presents a great opportunity to work in an interdisciplinary arena; sharing and discussing ideas with like-minded people and with experienced researchers. This will enable me to develop my own and other’s understanding of a certain subject area and to synthesis connections that are not always clear.
To engage the public with the real-world application of data science, I strongly believe it is imperative that project results are accessible to everyone regardless of what level of knowledge they have about the topic. Therefore, I hope to develop visualisation techniques and communication skills during this internship too, so I can tell a story about the impact of the data analysis and to keep the audience engaged.
Where do you see yourself in 12 months’ time?
One of the key things that drew me to this internship programme was the plethora of opportunities available at LIDA. With this in mind, I hope to achieve the goals I have set out by expanding my skill set and knowledge of data science. It is encouraging that these skills will be fruitful for me to progress in a career within the public sector.
Caitlin Chalk
Academic and personal background
I have recently completed a PhD at the CDT in Fluid Dynamics at the University of Leeds. The major part of my project was devoted to the development of a numerical model to simulate different stages of landslide behaviour. In addition, I conducted experiments of a small-scale debris flow. In the final year of my PhD I also worked as a Research Officer for Procter and Gamble, where I carried out numerical simulations regarding the products manufactured by the fabric department of the company. Prior to my postgraduate studies, I completed a BSc in Mathematics at the University of Leeds. Over the course of my academic career, I have acquired a keen interest in the development of numerical models and applied techniques to solve real world problems. In my spare time, I have been (slowly) learning Spanish for the past four years.
What do you hope to get out of your internship
I am excited to further my research career within both academia and industry, and to learn more about the link between these two areas. I would also like to learn new ways of processing and interpreting big data, with an informed knowledge of the best practice data management and software development techniques. I am really interested in the vast array of applications of data science, ranging from business analytics to population behaviour. I would like to be immersed in the LIDA community, and learn as much as possible about the range of ongoing research.
Where do you see yourself in 12 months’ time?
I hope to be in the initial stages of a career using data analytic techniques to work on projects that have a positive societal impact. A challenging and stimulating environment is essential.
Maab Ibrahim
Academic and personal background
I applied for LIDA internship position before completing my MSc degree in Advanced Computer Science at University of Leeds. Previously and after finishing my BSc degree in Computer Science, I chose to gain experience in the professional sector by working as a software developer since I believe that the theory and limited experience are not enough to pursue a career in the continuously updated computer science field. Later, I co-established my software development business. I manged at that time to lead a small group of software developers into planning and carrying out successful software systems in various sectors. One of the dimensions of my professional life was empowering others which I enjoyed especially when I remember having the chance to co-found one of ACM professional chapters.
During my MSc degree, I focussed on investigating the emerging approaches for human and computer interactions alongside the computational systems and the infrastructure and system performance issues in the large-scale, heterogeneous hardware distributed systems. In addition, I worked in many projects that allowed me to analyse, manipulate and process real-world data in different ways. I worked with different types of data both structured and unstructured. I also managed to build an understanding of how to handle and process big data systems using the state-of-the-art technologies as well as exploring the different machine learning algorithms and identifying their efficiency while trained on different datasets. I also worked with different types of data to achieve different goals like sensors generated data to analyse air quality, social media data for language, dialect and sentiment analysis of the text, railway data to analyse the quality of the data as well as visualizing it, and other types of data.
What do you hope to get out of your internship?
Working as an intern in LIDA will have a great impact on my personality and my career. As most of my study and work experience involved working mostly with people from computer science background. Working in a multidisciplinary environment is something I would definitely enjoy and benefit greatly from by getting the chance to team up with people from different backgrounds as well as managing to see how people can look at problems from different perspectives which will definitely affect my perspective of problems accordingly. Furthermore, as I am aiming to prepare myself for my PhD degree. Another thing I am looking to explore in LIDA is getting the opportunity to combine between my programming skills and my passion for research with the supervision and guidance from experts in their fields to develop theoretical and practical skills I need. In additional I am aiming to also generate a publication from the research projects I will work on.
Where do you see yourself in 12 months’ time?
After working in a research-oriented program, I am expecting to be ready to output a quality research for my PhD research. In this 12-month period by designing and implementing systems for different domains I am expecting to end up with a different perspective for my PhD thesis as well as to benefit from the experts and professional networking for future research-oriented job opportunities.
Thomas Richards
Academic and personal background
The title of my PhD thesis was “Identifying shared pathways across multiple muscle groups of the human upper limb”, and addressed how pathways in the brain, hindbrain, and spinal cord generate different patterns of muscle activity. I am from a biology background, but during my BSc Hons in Medical Sciences, I learned fundamentals of how computational modelling and programming could be used to simulate biological problems and answer questions not approachable without automation. This is partly what led to me taking the PhD project I did, since large parts of the project were dependent on learning the principles of computer and data science to analyse neurophysiological data I had collected. To do this, I wrote a number of programs in Matlab, CED, VBA, R, and other languages. This experience taught me the value of being computer-literate and the considerable possibilities when data science is applied to real-world data. The natural next step following my PhD was to broaden my experience of using computers for science, which led me to the LIDA internship programme.
What do you hope to get out of your internship?
My primary motivation for applying to the internship was to get broader experience of computational data science while at the same time learning good practice for writing programs and compiling scripts. During my PhD, I learnt valuable lessons about how methods such as parallel or batch processing can allow you to address questions which would not otherwise be possible. I hope to finish the internship with a broader toolbox and more refined set of skills with which to approach my future research. On top of this, I hope to meet people from a broad range of backgrounds so that I can get a feel for different ways data science is being utilised throughout the modern world. I want to gain awareness and understanding of the broader applications of computational models and data sciences, which will benefit me in future projects.
Where do you see yourself in 12 months’ time?
In 12 months’ time, I will have just finished my second project at LIDA, and at this point hope to have secured funding for post-doc project into clinical neurophysiology where I will incorporate skills and knowledge gained at LIDA. I also hope at this point to have begun work on launching a start-up in the medtech space.
Patrycja Delong
Academic and personal background
My academic career started with a BSc in Biophysics, where I learned quite a bit of maths and some programming. During a seminar I first heard about Brain Computer Interfaces and machine learning, which inspired me to pursue Masters and PhD in Neuroscience. I have recently submitted my PhD thesis about the role of awareness and metacognition in audio-visual integration. In my research I used supervised machine learning to discriminate between neural representations of different mental states.
In my free time I enjoy playing board games, RPG and sailing. I am an enthusiastic follower of global and national politics and I believe many issues could be solved by making data-driven decisions instead of based on personal sympathies.
What do you hope to get out of your internship?
I hope to gain more insight in data science as a career and develop my understanding of advanced statistical methods. I would like to gain more experience working with software/programming languages commonly used in industry, which I haven’t had a chance to use during my PhD (such as Python, SQL, Tableau).
Where you see yourself in 12 months’ time?
Towards the end of this internship I hope to have more clarity about my next career steps. I have confidence that in 12 months I will be well prepared to get my dream job.
Francisco Videira
Academic and personal background
My name is Francisco, I’m 24 and I’m from Portugal, Lisbon. My academic path began when I got into Medical School. After two years I realised I could make a broader impact on public health by using the power of data. That was when I made the decision to take a degree in Computer Science and Engineering. When I took an AI and a Databases module I understood the potential of machine learning in finding hidden patterns in data. These could allow stakeholders or policy-makers to change their perspective in approaching a particular issue.
What do you hope to get out of your internship?
Aside from working on my communication and team-work skills, I hope to develop a successful mentor-mentee relationship, which I think is important in an academic environment.
I also hope to take my technical skills one step further. I look forward to building a more in-depth knowledge of R and Python, exploring data science techniques using machine learning. Working in an interdisciplinary environment will allow me to get in touch with different areas of knowledge and help me understand what I want to be doing in the future.
Where do you see yourself in 12 months’ time?
I’m thinking about taking a Master’s degree in either Artificial Intelligence or Data Science. In the future I would like to work in the healthcare industry as a data scientist.
Read Francisco’s blog: An introduction to the LIDA internship
Holly Clarke
Academic and personal background
I have a background in the social sciences, first completing a degree in Politics and International Relations at the University of Manchester, then a Master in Social Research, specialising in quantitative methods, at the University of Sheffield. Through this I have gained a deep interest in how data can be used to answer questions about the social world, always gravitating towards the numerical side of the subject. As part of my studies I’ve used data to investigate diverse topics from the representation of refugees on twitter to the prevalence of national identity in Britain.
What do you hope to get out of your internship?
During this internship I’d like to build on the knowledge I have gained from my academic education to develop new capabilities using the programming language R and extend my skills to use new languages and techniques such as Python and Machine Learning. The LIDA Internship Programme is partnered with a wide variety of external organisations and I’m excited about the possibility of working with these, applying myself to practical challenges and learning from the diverse skill-set and experiences of the other interns in my cohort. I also love the way data can be used to tell a story and hope to develop my ability to produce engaging and interactive data visualisations which can communicate findings to broad audiences.
Where do you see yourself in 12 months’ time?
Alongside my studies I’ve worked for charities and would love to unite this with data science to apply its techniques to the third or public sector. There is huge potential for utilising data to improve issues from inequality to the environment and I believe occupying a role that does this would be incredibly rewarding. Ideally in 12 months’ time I will have gained many of the skills and experience needed to equip me for such a role and will have the opportunity to continue my personal and professional development along this path.
Natalie Nelissen
Academic and personal background
I’m a biomedical scientist by training and have worked in the fields of neuro-imaging and digital health. In my spare time, I enjoy long dog walks, reading, writing, crochet, and trying out other arts and crafts. I’m originally from Belgium and moved to the UK in 2011.
I started my career in academia, acquiring and analysing brain scans of dementia patients. My next project was focussed on multivoxel pattern analysis (machine learning) to decode brain signals around selective attention. As part of one project, we developed and tested a mobile digital platform to monitor mood and cognition, aimed at learning more about bipolar disorder. Keen to delve deeper into the fascinating world of digital health, I left academia and joined mHabitat, an NHS hosted organisation which supports people-centred digital innovation in health and care. It was a real eye opener to see first-hand the gap between academic knowledge and best practice versus what is feasible and desirable in ‘real life’.
What do you hope to get out of your internship?
Since the next big wave in digital health is likely to be Big Data and AI, I want to be better prepared and learn more about these topics. The LIDA data internship is an amazing opportunity, offering a balance between formal support and freedom to explore. My aim for this year is to build a strong methodological foundation in data science, as well as a support network of colleagues working across multidisciplinary teams.
Where do you see yourself in 12 months’ time?
I’m open-minded as to where this fellowship may take me in 12 months’ time. My career path up till now has not been straightforward, as I enjoy tackling new challenges and learning new skills. I have always been drawn to health and social issues and would love to use my new data science skills to help make an impact.
Robert Long
Academic and personal background
As a teenager in the early 1980s I became interested in programming personal computers, which had just become affordable to the average family. After graduating from university in Accounting and Finance I was well placed to work in the newly deregulated financial markets as an analyst/ programmer. I moved on to trading in financial derivatives in London, Hong Kong and Tokyo before leaving to work in IT again as a programmer and system administrator for a start-up distributing music in physical and digital formats. When my children were nearing school age, we moved back to the UK, and I undertook a Master’s degree in Statistical Epidemiology at Leeds University and then went on to teach on the same programme, which has now become the MSc in Health Data Analytics.
What do you hope to get out of your internship?
After working in the private sector as a data scientist, I came back to academia to undertake this internship, where I hope to integrate my knowledge of programming, business and statistics into a paradigm of frequent delivery of insightful data-driven products (Data DevOps). My initial project concerns the advancement of analysis of crime data for police resource deployment.
Vijay Kumar
Academic and personal background
I joined LIDA as a Data Scientist Intern straight after finishing my BSc Hons in Physics. During this time, I undertook some computational physics projects and used data science in my studies. This involved using advanced statistics and methods such as Monte Carlo simulation. I always enjoyed my computational science modules and knew I wanted to grow in my technical computing abilities. I also enjoyed my experiences in research and contemplated undertaking a PhD.
But I joined LIDA as I wanted to gain hands-on experience working with real data on projects that will affect people’s lives. I am unsure whether I want to pursue a career in academia or industry, and I hope that working as a Data Scientist Intern will give me the experience to be able to make an informed decision about working in research whilst also giving me networking opportunities with people in industry. I believe that working at LIDA will give me the vital computational and soft skills needed to thrive in whatever I do.
What do you hope to get out of your internship?
I want to make full use of the training available to broaden my skillset. I would like to learn to use other coding languages such as R and Java as I believe it will help me be a better all-round professional. I am also very interested in data science techniques such as machine learning. I have been playing around with this in my own time, but I would love to see how machine learning can be implemented in a research environment on real data. This is something I would love to get stuck into as it has many implications in science and medicine.
Where do you see yourself in 12 months’ time?
In 12 months’ time I would hope to be starting a new job in industry or starting a PhD. I am not too sure what the future holds but I am reassured that working as a Data Scientist Intern at LIDA will give me the skills to be able to have options, and the experience to know where I am best suited.