Fashion education needs a reset for the data science era
Fashion education needs to be reassessed to ensure students are equipped with the data science and analytics skills needed to thrive in an ever-changing industry, according to Dr Sheng Lu from the University of Delaware, who has co-authored a new study on undergraduate curriculums in the US.
As the fashion industry is becoming ever more data-driven, the type of talents required by the industry and related skill sets needed are also quickly and fundamentally changing in nature.
Notably, data science-related skills – the ability to understand, interpret and analyse patterns, trends, and associations from data by using quantitative analysis tools, have become ever more relevant than ever.
The research, ‘Are fashion majors ready for the era of data science,’ is co-authored with Lora Merryman from the University of Delaware. And it comes as more and more fashion companies are leveraging data science and related business analytics tools to support their daily business operations.
From supply chain management and inventory control, to sales forecasting, social media and analysing consumers' purchasing behaviours, fashion companies are leveraging data science to improve or fundamentally change how they create new products.
Fashion companies are leveraging data science to improve or fundamentally change how they create new products
Some firms have begun to integrate data analytics and machine learning in their apparel design process. Well-known fashion icons, such as Gap Inc, have attempted to remove the "creative director" position and instead use data scientists to design new products.
The combination of data science and fashion has attracted many new players, especially technology companies. These tech newcomers, such as EDITED, Trendalytics, and Style Sage, provide big-data based analytics tools that help conventional fashion brands and retailers more powerfully and effectively analyse their sales, identify market-popular styles, trends, textile materials, and design product assortment and pricing strategies for their target consumers.
Shifting fashion job market
Not only is the increasing use of data science changing how fashion brands and apparel retailers design, merchandise, market, and deliver their products, but it also impacts the skillsets companies now expect and demand from their talent.
On the one hand, the growing use of data science is creating new types of jobs that did not exist in the past. An analysis of job openings by US fashion brands and retailers posted on the Business of Fashion (BOF) from January 2019 to July 2020 shows that job titles such as "data editor," "data scientists," and "smart inventory manager" were among the most in-demand.
Meanwhile, with the widespread use of data science in almost all aspects of a fashion company's business operations, even the expectation for traditional "merchandising" and "design" positions are gradually adding new data-related requirements.
Fashion education in the United States
As one of the most popular college majors among Generation Z, over 50 undergraduate fashion programmes are currently offered by US-based higher education institutions. These college programmes typically provide Bachelor of Science (BS) or Bachelor of Art (BA) degrees that concentrate on the design, making, or merchandising of fashion, apparel and textile products.
Pursuing a career in the fashion industry after graduation is among the strongest motivations for students enrolled in an undergraduate fashion programme.
Updating skills in the era of data science
Even though the fashion industry and the fashion job market are turning more data-intensive, the study finds that fashion students still have minimal opportunities to play with numbers.
Fashion students in US colleges are typically required to take only 2-3 mathematics, statistics, and merchandising courses that include some quantitative data analysis components. However, there are almost no fashion courses directly addressing data science or data analysis.
The study also finds that design-oriented fashion programmes, in particular, need to shift the culture of avoiding "math and numbers" and instead create more opportunities for students to play with data and improve their quantitative reasoning skills.
Fashion students' data processing and data analysis software skills could be improved through stronger industry-academic partnerships.
Another suggestion is launching new interdisciplinary fashion programmes that target fast-growing but non-traditional jobs in the fashion industry, such as fashion data scientists.
These new fashion programmes could also appeal to students interested in the science, technology, engineering, and mathematics (STEM) discipline, resulting in an expanded and more diverse student body of fashion majors.
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