if you really understand ML/DS, then you would know that ML is founded on all the same concepts. > "... they tackle data is radically different from statistici

if you really understand ML/DS, then you would know that ML is founded on all the same concepts. > "... they tackle data is radically different from statistician ..." the short answer is, before we had limited data, compute, and the problems businesses needs increased substantially. now we can do better things, and if you cant do more than what companies were doing in the 1950s, then your value proposition is significantly less than someone that's using industry practices established in 2017 / early 2018. > "I wish statistic is better at marketing. Oh well." as a data scientist, I have to know stats, ML, big data tools (spark/splunk/hdfs), programming, and domain specific knowledge. some data scientists are just rebranded statisticians, which is fine. the role of a data scientist varies to the needs of the org they are in. I mostly do anomaly detection and classification, while others may focus solely on AB testing. > > "> why can't data science simply be about applying the scientific method in the realm of data analysis?" the statement doesnt make sense to me. if you are doing data analysis then by default you are doing observations and measurement on empirical data. As far as experimentation goes, almost no one has the resources or the standing to do that. Some do, like those AB testers I mentioned before. > "Actually my last day of internship I've met a few statistician interns some of them are from Cal (UCBerkely) and they came to the same conclusion (we have a lot of complaints). The ML/DS group is really just doing black magic (nicest way of putting it)" Then they don't appreciate ML and the problems they are trying to tackle. It reminds me of a stats PHD co-worker that was upset about the number of parameters in an image classification problem we had. they didn't think a model could be ran that had more parameters than observations. I was like we are not running linear models ... As an experiment, go to kaggle and try the spooky author classification competition. See how far you can get with basic stats and then see how much further you can get with ML. hopefully it will give you an appreciation for some of the ML tools available. It's not black magic, like many other domains; its just a dense subject that takes a lot of time to understand