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University of Essex Big Data and Analytics Summer School
About the placement/internship

Why did you decide to do a placement/internship?

In my PhD study, I investigated to identify methods and build tools to aid researchers to select the most suitable statistical model checking for analysis and verification of the biological models. Most of the experience I had was involved in data cleansing, translation, visualisation and modelling data with machine learning algorithms, which are similar processes that big data and data science involve. However, I have learned these skills on my own, and I could focus on learning some very specific methods that I needed to apply to my experiments. Despite, this it were enough to complete my experiments and analyse the results, I was not proficient enough (as a self-taught) to apply these skills to different fields. However, in order to exercise my data science skills in interdisciplinary fields, such as the natural sciences, social science, and the humanities, I needed to hone my existing knowledge and learn more about working with data in practice and learn how to store/manage/analyse big data.

How did you find your placement/internship?

Department email

Application process

The summer school is organized annually, and it is advertised on University of Essex web site (, where users can apply online.

Main responsibilities

As a participant, I have attended the courses and we learn some data analytic tools by completing lab works.

Typical day

The training was a two weeks programme. There were 35 courses, but I had to select 7 courses over two weeks. The courses were taught by researchers and industry experts in big data and data science field. Each week there was a key note talk by a field leading expert. The key note sessions were especially very useful for hearing what are the data science challenges that big companies encounter and how they develop solutions. Also in the second week, a peer network event was organised. The participants' backgrounds were quite diverse, both from industry and academia (ranges from master students to professors). It was a good chance to exchange our experience with other participants.

What did I enjoy about my placement/internship?

The programme was organised very well. The courses covered a wide range of topics from multiple perspectives of data science. Mostly, cutting edge technology tools are used for education.

Challenging aspects

In general, most of the courses ran very smoothly, and no significant problem occurred. If I want to be a bit picky, some of the courses needed to be elaborated in more details. For example, some lecturers used pre-configured tools, but the ability to configure these tools are distinctive qualities for data scientists, hence it was important and very relevant to teach how we can configure the tools. I was hoping to learn how these tools are configured for running on large-scale systems, with multiple nodes, but since orchestrating the right configuration were quite hard tasks, the lecturer skipped it and didn’t provide such details. I believe it was a big miss in the course content.

Did you use any skills learned from your course?

I started to use Gate tool for text processing. I also use Spark for big data analysis and Casandra for data storing. Additionally, I believe I learned how to systematically analyse data which become a regular metrology I apply when working with data.

Has your time on placement influenced your future career choice?

Yes, it definitely did. By attending this training, I learned a wide range of algorithms and technologies. Learning more tools and methods increased my experience and confidence, now I am more inclined to develop a career in data science.

Advice to students

Other comments

The summer school registration fee was not cheap, without bursary from the Postgraduate Researcher Experience Programme (PREP) it would not be possible to attend the programme. Now I have more career options, and I have more control to choose the job/field I want to work on.

Form completed: 19 Aug 2017

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