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Welcome to Careers in Data Science & Data Analysis

Isobel EPowell2 December 2019

Data Science & Data Analysis Month

Interested in data? Have a passion for exploring information or creating solutions? Ever thought of using your skills in data management, coding or analysis as a career? Join us for Data Science & Data Analysis month to find out more about this fast-growing and lucrative industry. Come along to our employer taster session and test ut your data mining skills or attend our employer and alumni forum to hear more about the industry, its scope and the specialisms you could go into.

Thinking about attending but not sure if it’s for you?

Come along if you want to learn more about how to use data in a career or just hear from speakers with research and PhD backgrounds who have transitioned out of academia. Improving business practice, creating important analysis or implementing processes your thing? A career in data could allow you to expand the reach of your research and support an organisation to grow in so many different industries.

If you want to transition out of academia but still support research, come along to our sessions and hear how this is possible with a career in data. 

Heres what’s coming up…
Check out the events coming up this month and learn more about this expanding and research-focused industry. Understanding the meaning behind data is becoming central to all business practice. This is why careers in data span across retail, finance, government, education and more.  Check out what’s coming up and explore a career in something new! 


Careers in Data Science & Data Analysis Forum
Thurs 5 Dec, 5.30-7.30pm

Skills in research, analysis and data presentation are vital to the data science industry and are why increasingly organisations are looking to hire researchers.

This forum will give you the opportunity to get an insight into the data science and data analysis sector from PhD level speakers who have paved a career for themselves in this industry. Find out more about what a career in data encompasses, the wide range of industries and specialisms this covers and gain tips on how to find a researcher role. This is a key opportunity to gain an insight into a career you may not have previously considered.

Speakers include:

Dr Lucie Béraud-Sudreau
“Dr Lucie Béraud-Sudreau is Research Fellow for Defence Economics and Procurement. She studied international affairs at Sciences Po Bordeaux (France) and holds a PhD in political science from the University Paris-2 Panthéon-Assas (France). Her PhD thesis compared French and Swedish arms export policies since the end of the Cold War. Lucie’s current role involves, inter alia, data collection and analysis, catering and updating datasets on military expenditure.”

Dr Liam Duguid
“Liam has been worked in data science for the last 4 years and just moved organisations from Capita to HCL. His role has focused on data preparation, algorithm design and machine learning development along with implementation. The new role will be using these skills and others such as natural language processing to solve problems in Data Science Consultancy. He previously completed a master in Theoretical Physics at UCL followed by a PhD at Royal Holloway in High Energy Particle Physics working on dielectron decays in the ATLAS experiment at the LHC.”

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Employer Taster Session in Data Analysis – Led by Celonis
Mon 9 Dec, 12.30-2pm

Please bring your laptops!

Process Mining – Understanding The Story Behind the Data
Validating meaning behind data patterns can sometimes be tricky. Very often we can identify trends but struggle to understand the root causes and the story behind them. Process Mining is a useful method to reconstruct the as-is process behind the data and use sequential analysis of activities over time to visualise bottlenecks and deviations based on data stored in IT systems.

With its academic DNA Process Mining has not only turned into a budding research field but also transformed the way companies operate. The talk will cover an introduction to Process Mining both from an academic and applied perspective, its application in data-driven management today as well as a hands-on case study in the software. The session will also include insights into the unicorn story of Celonis.

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What else can you do to get career ready?

Alongside this, we have a team of careers consultants with research backgrounds who work closely with UCL’s researcher community and can provide support regardless of whether you’re looking to continue in academia or explore other options. Our “Researcher appointments” can be booked at any time through your myUCLCareers account and can be used to cover a range of queries from exploring options to getting support with applications/interview preparation. The careers consultants also run separate workshops covering a range of topics on academic and non-academic career routes for researchers.

Details of the full events programme can be found here

What is Data Science and how can you get into it? Tips from a Data Scientist

Vivienne CWatson1 April 2016

Shaun Gupta has a MSci in Physics from UCL and a PhD in Particle Physics from Oxford. He tells us how he started his career in Data Science and what being a Data Scientist is like.

GuptaTell us about your job.

I am currently employed as a Data Scientist at a startup called Row Analytics. Data Science is an emerging field, and it involves using a mixture of coding and statistical analysis to answer questions using big datasets. The company is very small (less than 10 people), which means my role actually covers a wide range of different activities in addition to just Data Science. It is an exciting place to work as I am helping to build the company from the ground up, in a sector that is still relatively new and constantly evolving.

How did you move from a PhD to your current role?

After undertaking an MSci in Physics at UCL, I pursued a PhD in Particle Physics at the University of Oxford. During my final year, I spent a month taking part in the 2015 Science to Data Science (S2DS) bootcamp, based in London.  The school was a pivotal opportunity to learn more about the emerging field of Data Science, and showed me how relevant my skill set was in industry. As part of the school, I spent time working on an exciting project with my current employer Row Analytics, who offered me a full-time position once the school was over.

What does an average working day look like?

As the company is currently very small, I tend to perform a variety of tasks as part of my job. My time at the moment is split between helping to set up an infrastructure in the cloud on AWS, setting up and configuration databases (noSQL and graph based), building a web application (both front and server backend), writing programs to scrape unstructured data, and performing Natural Language Processing (NLP) on the data.

How does your PhD help you in your job?

In essence, my role is very similar to what I did during my PhD, only using data from a different source. As a result many of the techniques and practices I learnt during my PhD are useful in doing my job. These include programming, problem solving skills, strong mathematical skills, statistical analysis techniques including knowledge of learning algorithms, and the ability to work independently in a research driven way to develop new ideas/products.

What are the best things about your job?

I enjoy working in a constantly evolving field with many opportunities to get involved in new projects and learn about new cutting edge techniques. I also find it exciting working for a company at such an early stage in its development, and being involved in shaping its future. I am also lucky in how flexible my work is, with the ability to work from home a couple of days a week.

What are the downsides?

As the role involves a lot of coding, a lot of time can be spent fixing bugs. Also a lot more time is spent working with the data and structuring it in the correct way for analysis than one may expect initially.

What tips would you give researchers wanting to move into the same, or similar, role?

Data Science is a new and exciting sector that is rapidly growing, and so now is the perfect time to get involved. I would say solid programming skills coupled with good analytical ability is key. Therefore, I would advise to brush up on your coding skills in languages such as Python and R, and your knowledge of statistics. Attempting challenges on sites such as Kaggle is a useful way to do this. Attending a school such as S2DS will help you to learn more about the industry, and get involved in real world applications of Data Science with companies. There are also many meet-ups around London and boot-camps that are worth attending.