DataCamp’ing during Lockdown

Kaori Narita, Economics, University of Liverpool (2017 Cohort)

A few months in during the first lockdown, I finally started finding myself comfortable with the “new normal,” which is quite different from my old working environment, since I was never a working-from-home person and enjoyed the company of my colleagues in our PhD office.

Thanks to the financial support given by NWSSDTP, however, I have developed necessary skills as a quantitative researcher over the past three years of my PhD journey, and this has remained unchanged amidst the global pandemic.

One of the silver linings found in the lockdown is the opportunity to further develop my research skills. Almost a year ago , I decided to subscribe to DataCamp. The reasons why I opted for the subscription over attending online workshops are its flexibility and diversity in the modules that DataCamp offers. This means that you can learn and practice a specific topic on demand, which may arise in the process of conducting your research.

My PhD project involves analysing sport data, which provides by its nature vast amounts of observations and variables. Whilst the majority of my empirical analysis owes to parametric estimation of regression models, I saw the possibility of improving predictions by applying Decision Tree models to one of the classification problems. I was completely new to R and Machine Learning (ML), which meant I needed to learn both from scratch. The DataCamp courses “Introduction to R” and “Tree-Based Models in R” did a brilliant job in meeting my specific demand and provided the very information I was looking for, as well as an opportunity to practice coding.

Another perk of using DataCamp materials for research skill training is its structure of courses. In a typical module, a short video introducing a new concept is immediately followed by a simple task that involves coding, before moving on to the next topic. This dynamic works well with a person like myself, who prefers learning by doing over listening to lectures. One benefit of this structure is that learning and practicing can be done at your own pace, and the difficulty can be tailored according to your own experience. This means that you do not need to commit to a specific time and date to attend a workshop nor to an entire course, but you can, for instance, decide to spend an hour a day to learn what you need. The courses are offered for a wide range of programming languages, including Python and R. You can learn from as many courses available on DataCamp as you wish, whilst your subscription lasts.

I completed several other courses, and now have completely shifted to R for my data analysis. This has opened up more options in terms of modelling and what type of data I can work with.

In the earlier stages of my PhD, I was fortunate enough to use the RTSG to attend in-person conferences as well as methodological workshops. As much as I appreciate and miss these opportunities, I would encourage anyone to make the most of the generous grant and wide range of online training available out there.

Speaking of a new skill that I have developed during lockdown, I picked up a new hobby in outdoor climbing. Funnily enough, this wall is sometimes what my PhD feels like: challenging but rewarding when you make progress!

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