Unlocking the Power of Machine Learning in Analytics: Practical Use Cases and Skills
Your essential machine learning checklist to excel as a data scientist in analytics
In the past decade, we have seen explosive growth in the data science industry, with a rise in machine learning and AI use cases. Meanwhile, the “Data Scientist” title has evolved into different roles at different companies. Thinking about functions, there are Product Data Scientists, Marketing Data Scientists, those specialized in Finance, Risk, and people supporting Operations, HR, etc.
Another common distinction is the DS Analytics (often referred to as DSA) and the DS Machine Learning (DSML) tracks. As the name suggests, the prior focuses on analyzing data to derive insights, while the latter trains and deploys more machine learning models. However, this does not mean that DSA positions do not involve machine learning projects. You can often find machine learning among the required skills in the job descriptions of DSA openings.
This overlap often leads to confusion among aspiring data scientists. During coffee chats, I frequently hear questions like: Do DSA positions still require machine learning skills? Or do DSAs also deploy machine learning models? Unfortunately, the answer is not a simple Yes or No. Firstly, the boundaries between the two positions are always blurry (even a decade after the data science job became a trend). Sometimes, within the same company, DSAs…