These skills:

Expert in data visualization using Tableau, PowerBI, Shiny, or other dashboarding tools.

Develops advanced analytic tools through the use of traditional regression, artificial intelligence and/or machine learning modeling.

Experience with machine learning APIs and computational packages (examples: TensorFlow, Theano, PyTorch, Keras, Scikit-Learn, NumPy, SciPy, Pandas, statsmodels).

Programming skills in Python, R, Spark, SAS

Could be anything from creating and interpreting basic Google Analytics reports to advising or implementing A/B tests to tracking largely business KPIs to classifying data for AI/ML pipelines to actually training AI/ML models and data science.

Other comments have included ML in their descriptions and I don’t agree. Both analytics and ML can fall under data science but there is a clear line between the two.

where do you think the line is?

from what I was reading, in some Analytics teams you have people working on ML, others on Data Engineering, other doing business intelligence

If you take all of these terms, I think of analytics as a synonym of business intelligence, not as an overarching term that covers ML, DE and BI.