DescriptionData science has become an incredibly important discipline in our modern society. This course merges statistics with programming so students can solve data science problems programmatically. We will explore applications in real world topics such as finance, sports, and epidemiology.
We will cover probability theory (independent events, Bayes theorem, etc) with applications to card problems, dice problems, etc.
We will cover linear regression, correlation, and differentiating between correlation vs causation.
We will introduce basic programming skills in python for data analysis. Students will learn techniques such as loops, conditional statements etc.
Students will learn to interpret graphs, mean, median, mode, standard deviation, boxplot, histograms etc. Students will also learn Monte Carlo Simulations and apply it to interesting applications such as nontransitive dice.
Students will be expected to bring their chrome books to class.