How Statistics and Machine Learning Differ in Quantitative Finance

# What are important statistical concepts used as a quant?


# What are important statistical concepts used as a quant?












I'm interviewing for some quantitative researcher positions at some hedge funds, and I've been told that there will be one interview session focused on stats, and one focused on ML, among others. This made me realize that I have a hard time distinguishing between stats and ML because there's such a great deal of overlap, although I think I have some kind of idea of what parts of stats might not be typically apart of ML.

I took stats courses in college and high school, and never were the words "machine learning" mentioned in those courses. In those courses, I recall more of a focus on things (most of which I have forgotten) like hypothesis testing, basic probability and common distributions, confidence intervals, bar/box plots/histograms, and univariate regression.

What part of stats would you consider to not typically be part of ML and are important for practical data inference/analysis/prediction in the financial industry, particular at hedge funds / prop trading firms?

## Answer by Attack68 (score 4)

https://quant.stackexchange.com/a/55342

To be honest this question isn't really within scope of QuantFinance because it is borderline opinion based but I will contribute an answer to the community regardless.

If you cannot determine the difference between stats and ML then you might consider that you do not understand the uses and methods used within each relevant discipline and may have to do more research, before being successful in an interview.

Statistics covers the mathematics of sampling and distributions of data. Hypothesis testing, simple probability and common distributions are so basic that you will rely on these constantly as a quant. I suggest you make every effort to remember your studies.

ML covers the variety of model techniques, and model fitting techniques, used to solve supervised or unsupervised problems, i.e. you have input data and you want to predict output data or perform some analysis on that data. That analysis may be statistical or it may be algorithmic. For example K-means clustering is not a "statistical technique", it is an algorithm.

## Answer by Thao Bui (score 0)

https://quant.stackexchange.com/a/55377

In broad, ML and statistics belong to data science. In a mean way, ML can be considered to be statistical models for prediction/classification problems. In machine learning, you will learn to deal with models like decision tree, svm, neural network, etc. When you apply these models to a particular problem, you will use basic statistical terms to evaluate the performance of models. What you learned prob&stats at college is essential to understand machine learning. You only need to learn a good course in machine learning, and I will suggest you to take the course of Prof. Andrew Ng. on Coursera. happy learning!