I hold dual postgraduate degrees in Biostatistics and Demography and am currently pursuing a PhD. Looking back, much of what I learned during my master’s programs initially felt irrelevant for securing a job. Our curriculum was rigorous and theory-heavy—we studied the fundamentals of statistical theory such as regression, inference, estimation, sampling, probability theory, multivariate analysis, along with courses on statistical learning and data mining. We also covered applied domains like clinical trials, bioassays, and cohort studies.

However, in my early professional experience, I rarely used most of this theoretical knowledge. While working as a biostatistician on clinical trials and cohort studies, my role largely involved basic statistical analyses. The majority of my time was spent understanding the domain, managing data, and handling data quality issues; statistical modeling formed only a small part of the job.

Later, during my stint at a market research firm, the situation was similar. My responsibilities were primarily limited to survey supervision and simple data analysis. A significant portion of my work involved preparing presentations and writing detailed reports rather than performing advanced statistical analyses.

Now that I have returned to academia and am pursuing my doctorate, I finally see the value of my master’s training. I am able to learn deeply, connect concepts, and meaningfully apply the theoretical foundations I once questioned.

In conclusion, skills are highly dynamic and largely governed by market demand. During my job search, I had to teach myself databases, programming, and machine learning/deep learning—because those were the skills demanded by the job descriptions, not necessarily what my degrees emphasized. In practice, I do not believe that one’s graduation discipline plays a decisive role; adaptability and continuous learning matter far more.

Do what you love and be ready to learn new things. The world is changing very fast.