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If we can instead simply crunch vast data sets, relying on the awesome power of modern computers, so much the better. After all, coming up with realistic models describing the way (natural) processes might work is hard mental effort. The notion that we can manage without models and that sufficient quantities of data-big data-can take the place of models is a seductive one. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot.” As an example, he cites Craig Venter’s sequencing of genotypes. We can analyze the data without hypotheses about what it might show. Indeed, they don't have to settle for models at all.” He went on to say, “We can stop looking for models. This course may be taken concurrently with the prerequisite with instructor permission.Chris Anderson, the former editor of Wired magazine, famously wrote (2008) that “oday companies like Google, which have grown up in an era of massively abundant data, don't have to settle for wrong models.
Stats modeling the world versus the practice of statistics how to#
Not only do we go through a large collection of interesting, challenging real-life data sets but we also learn how to use the free, powerful software "R" in connection with each of the methods exposed in the class. While learning all the techniques, we keep in mind that our goal is to tackle real problems. Text mining especially through PCA is another topic of the course. Contemporary methods such as KNN (K nearest neighbor), Random Forest, Support Vector Machines, Principal Component Analyses (PCA), the bootstrap and others are also covered. While classical multiple regression and logistic regression technique continue to be the major tools we go beyond to include methods built on top of linear models such as LASSO and Ridge regression. Modern Data Mining: Statistics or Data Science has been evolving rapidly to keep up with the modern world. STAT471 - MODERN DATA MINING (Course Syllabus) Familiarity with basic probability models is helpful but not presumed. The predominant software used in the course is R, with bits of JMP when helpful for interactive illustration. Those with more knowledge of Statistics, such as from STAT 422, or computing skills will benefit. Familiarity with the R-Studio development environment is presumed, as well as common R packages such as stringr, dplyr and ggplot.
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Students should be familiar with regression models at the level of STAT 102 and the R statistics language at the level of STAT 405. Hierarchical generating models often associated with nonparametric Bayesian analysis supply theoretical foundations. Examples that span the course illustrate the success of text analytics. Unsupervised techniques suited to feature creation provide variables suited to traditional statistical models (regression) and more recent approaches (regression trees). Course includes topics from natural language processing (NLP), such as identifying parts of speech, parsing sentences (e.g., subject and predicate), and named entity recognition (people and places). Techniques include those for sentiment analysis, topic models, and predictive analytics. This course introduces methods for the analysis of unstructured data, focusing on statistical models for text. Prerequisites: STAT 102 OR STAT 112 OR STAT 431 This course may be taken concurrently with the prerequisite with instructor permission. The methodologies can all be implemented in either the JMP or R software packages. By the end of the course the student will be familiar with and have applied all these tools and will be ready to use them in a work setting. It also introduces classification and regression trees (CART) and the popular predictive methodology known as the random forest. It delves into classification methodologies such as logistic regression. In particular it introduces automated model selection tools, such as stepwise regression and various current model selection criteria such as AIC and BIC. It extends the ideas from regression modeling, focusing on the core business task of predictive analytics as applied to realistic business related data sets. This course follows from the introductory regression classes, STAT 102, STAT 112, and STAT 431 for undergraduates and STAT 613 for MBAs. STAT422 - PREDICTIVE ANALYTICS (Course Syllabus)