I am sure we sure we are more than capable of looking beyond the scope of what your statistics professor had time to teach you at university. I have some knowledge and education of statistics myself, not that it makes me particularly more entitled to comment about it.
“Thats not the skill that’s taught in a statistics degree.”
I commend you for apparently having a statistics degree of some form. To suggest that analysing and comprehending large amounts of data isnt taught in a statistics degree makes me question your statistics degree. I’m not saying your degree is any better worse, perhaps just unique. Of course, comprehending large amounts statistical data would lead to the use of algorithms to accurately explain the data. We rely on algorithms and mathematics for statistical analysis. Understanding the ‘complicated’ maths or Bayes theorem wouldnt seem like that great a stretch given the OP’s education which is my initial point.
I have studied bioinformatics and as such I have a particular idea of the domain of medical statistics and the domain of bioinformatics.
Big data often means that testing for 5% significance is a bad idea. As a result people working on big biological data weren’t very welcome by the frequentists in medical statistics and bioinformatics formed it’s own community.
That community split produces effects such as bioinformatics having it’s own server for R packages and not using the server in which the statistics folks put their R packages.
In another post in this thread bokov speaks of wanting to use Hidden Markov Models (HMM) for modeling. HMM is the classic thing that based on Bayes rule and that people in bioinformatics use a lot but that’s not really taught in statistics.
Understanding the ‘complicated’ maths or Bayes theorem
Understanding Bayes theorem is not hard. Bayes theorem itself is trivial to learn. Understanding some complex algorithm for determining Hidden Markov Models based on Bayes rule is the harder part.
Machine Learning is also a different community then standard statistics. It’s also not only about Bayes theorem. There are machine learning algorithms that don’t use Bayes. Those algorithms are still different than what people usually do in statistics.
“My own statistics prof said...”
I am sure we sure we are more than capable of looking beyond the scope of what your statistics professor had time to teach you at university. I have some knowledge and education of statistics myself, not that it makes me particularly more entitled to comment about it.
“Thats not the skill that’s taught in a statistics degree.”
I commend you for apparently having a statistics degree of some form. To suggest that analysing and comprehending large amounts of data isnt taught in a statistics degree makes me question your statistics degree. I’m not saying your degree is any better worse, perhaps just unique. Of course, comprehending large amounts statistical data would lead to the use of algorithms to accurately explain the data. We rely on algorithms and mathematics for statistical analysis. Understanding the ‘complicated’ maths or Bayes theorem wouldnt seem like that great a stretch given the OP’s education which is my initial point.
I have studied bioinformatics and as such I have a particular idea of the domain of medical statistics and the domain of bioinformatics.
Big data often means that testing for 5% significance is a bad idea. As a result people working on big biological data weren’t very welcome by the frequentists in medical statistics and bioinformatics formed it’s own community.
That community split produces effects such as bioinformatics having it’s own server for R packages and not using the server in which the statistics folks put their R packages.
In another post in this thread bokov speaks of wanting to use Hidden Markov Models (HMM) for modeling. HMM is the classic thing that based on Bayes rule and that people in bioinformatics use a lot but that’s not really taught in statistics.
Understanding Bayes theorem is not hard. Bayes theorem itself is trivial to learn. Understanding some complex algorithm for determining Hidden Markov Models based on Bayes rule is the harder part.
Machine Learning is also a different community then standard statistics. It’s also not only about Bayes theorem. There are machine learning algorithms that don’t use Bayes. Those algorithms are still different than what people usually do in statistics.