Data analytics has emerged as one of the most important new areas with high demand in industry.
R, an open source domain specific language (DSL) focused on data analytics,
has grown in importance and usage in corporations, because it is free to use,
and is being constantly improved to include the latest statistical techniques.
Organizations using R span a wide range of industries and include companies such as Google,
Facebook, Bing, The New York Times, Orbitz, etc. R code is always at the cutting edge,
because the source code is open source, and it receives frequent new contributions
and improvements from experts around the world. This course helps students develop proficiency
in data analytics using R for statistical inference, regression, predictive analytics,
and data mining. It combines lectures, hands-on exercises, business case discussions,
and student presentations in a professional environment. As a student of data analytics,
you will benefit from learning R because (a) it is a core skill in high demand,
and (b) because doing data analytics using R will enhance your understanding of analytics.
Specifically, we will cover the following topics: R basics — data frames, packages, etc.,
Formal Inference – standard errors; t-distribution; confidence intervals;
Multiple Regression – assumptions and diagnostics; model fitting; comparing models;
interpreting coefficients; multicollinearity; Generalized linear models – Logistic regression;
Poisson and quasi-poisson regression; ordinal regression models; survival analysis;
Time series analysis – graphical exploration; autoregressive (AR) and
autoregressive moving average (ARMA) models; Data mining: clustering,
association rules; Text mining: analyzing twitter and social network data.