‘If you’re not learning data science skills’, says Martha*, ‘you’re essentially dying a slow death’. Strong words, some might say, but with these skills fast becoming integral to standing out academically and professionally, maybe it’s not so far from the truth.
Currently working towards her humanities PhD, Martha has taken a number of SAGE Campus online data science courses in order to be able to integrate computational text analysis as her key research methodology. We spoke to her to find out what motivated her to learn, and how she’s putting her new skills to use.
What motivated you to learn data science skills?
‘It’s the way the field of social science is headed’, says Martha, ‘and I realized I have to learn these skills to keep up’. Martha believes that the analysis of web data will be integral to research across all disciplines in the near future.
‘Research in the future will be based much more on how to solve problems, and funding opportunities will favor researchers who are utilizing computational research methods’. Like many others, Martha recognizes that data science skills afford researchers a closer and more meaningful connection to contemporary society, owing to the vastness and up-to-the-minute nature of the data now available for analysis. And quantitative analysis makes it easier for academics to justify how their research outputs can help solve problems and benefit society.
Positioning herself advantageously within the professional market is also a key motivation for Martha. She saw ‘a wealth of opportunities opening up for social researchers who are proficient in programming and data analysis’ and understands that over the next couple of decades, these skills are likely to become a necessity for thriving in the job market.
How was your experience of learning with SAGE Campus?
Martha found SAGE Campus through our introductory videos on computational social science and believes that ‘Campus is creating a general syllabus of computational research methods’. For Martha, ‘it’s great to have that panoramic view. I didn’t even know that some of these topics existed before coming to Campus!’
Martha had previously tried other online learning platforms, but found that the long courses - some up to 15 weeks - didn’t fit around her busy schedule. Martha tells us she prefers Campus as ‘courses are split into small, manageable portions, as they allow for more flexible learning’. Martha believes ‘online learning is perfect for beginners as it affords you more time and freedom to interact with the teaching materials. You can return to them as many times as you want within the course schedule’.
In particular, Martha likes the visual nature of Campus courses, and that the content is simplified and engaging to keep learners motivated. ‘Learning these techy skills can be challenging and it’s important that learners aren’t put off by frustration. The Campus course design is perfect as complex topics are presented in a simplified way that’s perfect for beginners, as it’s not intimidating’, says Martha.
Furthermore, Martha found that other data science courses she tried weren’t detailed enough. And they were either very basic or advanced, but didn’t take learners to the intermediate level, whereas ‘Campus fills that gap’.
How do you think data science will impact social science in the future and how will you put your new skills to use?
Martha believes that ‘the explosion in interest in data science will only get bigger’. Crucially, data analysis skills have applicability in both academia and industry, and across both the public and private sectors. ‘Students and faculty alike will have to educate themselves in data science and computational methods’, says Martha, who envisages a future where faculty members come from increasingly diverse backgrounds, and a growing necessity for researchers and practitioners to work in interdisciplinary ways.
In terms of her own future, Martha is excited to continue building her skills with SAGE Campus, and using these to continue broadening the scope of her research outputs.
*Name has been changed.