Unlocking the Power of Machine Learning in Mathematics: Practical Use Cases and Skills | by Yu Dong | January, 2025

Your machine learning essentials for success as a data scientist in statistics

Over the past decade, we have seen explosive growth in the data science industry, with the rise of machine learning and AI use cases. During that time, the title “Data Scientist” has evolved into different roles in different companies. If you think about jobs, there are Product Data Scientists, Marketing Data Scientists, those who specialize in Finance, Risk, and people who support Operations, HR, etc.

Another common distinction is the DS Analytics (commonly referred to as DSA) and DS Machine Learning (DSML) tracks. As the name suggests, the former focuses on analyzing data for insights, while the latter trains and implements additional machine learning models. However, this does not mean that DSA positions do not include machine learning projects. You can often find machine learning among the required skills in job descriptions for DSA openings.

This overlap often leads to confusion among budding data scientists. During coffee conversations, I often hear questions like these: Do DSA positions still require machine learning skills? Or do DSAs also use machine learning models? Unfortunately, the answer is not a simple Yes or No. First, the boundaries between these two positions remain blurred (even ten years after the data science profession became mainstream). Sometimes, within the same company, DSAs…