This is a theoretical course covering fundamental
topics of probability and statistics in the context of
data science with its inherent challenges. This course
will start with a review of fundamental probability,
covering topics like random variables, their
distribution functions, expected values, conditioning
on certain events and independence. The students
will be acquainted with certain families of probability
distributions and then will learn how to estimate
certain quantities of interest from observations.
A range of properties of estimators will be studied,
including sufficiency, unbiasedness and consistency,
which enable the evaluation of their quality with an
emphasis in the framework of big datasets.
The students will also learn how to introduce
different types of hypotheses, how to construct tests
for their hypotheses, as well as how to compare
between tests and how to construct confidence
intervals for their estimators.
Outcome: Not Provided
