2026-01-07
Recently, the School of Economics and Management of Beijing University of Technology successfully hosted a keynote sharing event themed "Unlocking New Momentum for Data Talent Growth". Aimed at systematically introducing the development trends, core skill systems, and wide-ranging industrial applications in the field of data science, the event strived to spark students' keen interest in data analysis and digital technologies. Against the backdrop of the accelerating evolution of the digital economy and the in-depth penetration of artificial intelligence into all industries, higher education is in urgent need of alignment with cutting-edge industrial practices. Events of this kind serve as crucial initiatives to promote industry-education integration and enhance students' digital literacy.

To address the prevalent postgraduate entrance anxiety and directional confusion among college students, the event featured a special session titled "Exploration of Postgraduate Directions in Big Data". Speakers systematically sorted out the popular research directions at the postgraduate stage from dual perspectives of academic research and industrial application. They emphasized that future research in data science is no longer confined to a single discipline but presents a highly interdisciplinary and integrated feature. Statistics provides a rigorous inferential framework, computer science empowers efficient algorithm implementation, while economics, sociology and even psychology inject real-world contexts into problem modeling. Therefore, undergraduates should focus on laying a solid foundation in mathematics (e.g., linear algebra, probability theory and mathematical statistics), master at least one programming language (e.g., Python or R), and actively participate in research training or data competitions to enhance their comprehensive competitiveness.
Under the national strategy of "Mass Entrepreneurship and Innovation", a growing number of young students regard entrepreneurship as an important path to realize self-value. The speakers systematically dissected the key stages of building a startup from scratch: first, it is essential to clarify a Validated Business Hypothesis to avoid falling into the trap of "technology for technology's sake"; second, building a founding team with complementary capabilities is crucial, especially a diverse talent pool with expertise in technical development, product design and market operations; third, one should be adept at leveraging open-source tools and cloud service platforms to reduce initial technical costs; finally, it is imperative to establish a mechanism for rapid iteration and user feedback to optimize products through dynamic trial and error. He also specially reminded students that entrepreneurship is not a solo adventure for the bold, but a long-distance race that requires patience, resilience and resource integration capabilities. It is recommended that students conduct "low-risk trials" during their academic years by participating in innovation and entrepreneurship competitions and joining incubator programs.
In the context of increasingly fierce competition in the job market, professional certifications have become an important credential for job seekers to stand out. The event provided a detailed introduction to the structure, assessment content and industry recognition of the CDA (Certified Data Analyst) certification system, which covers a complete competence spectrum ranging from basic data processing and visual analysis to advanced modeling and machine learning.
The event venue was packed to capacity, and the interactive session was particularly lively. As one insight goes, "As long as we lay a solid foundation, master relevant tools and engage in practical practices from now on, we will be fully poised to become contributors in this field in the future." This shift from "passive acceptance" to "active exploration" perfectly embodies the value of the deep integration of general education and career enlightenment in universities.
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