What is Statistics I like to think of statistics as the science of learning from data It presents exciting opportunities for those who work as professional statisticians Statistics is essential for the proper running of government central to decision making in industry and a core component of modern educational curricula at all levels Jon Kettenring ASA President 1997 http www amstat org careers whatisstatistics html The mathematics of the collection organization and interpretation of numerical data especially the analysis of population characteristics by inference from sampling American Heritage Dictionary 1 Learning from data turning data into information Glymour Madigan Pregibon Smyth 1997 Statisticians are like painters they tend to fall in love with their models Deveaux Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write H G Wells http www statoo com en statistics 2 What is data mining Data Mining is the process of extracting knowledge hidden from large volumes of raw data http www megaputer com We are drowning in information but starved for knowledge John Naisbitt http www statoo com en datamining Data mining is the process of identifying valid novel potentially useful and ultimately comprehensible knowledge from data bases that is used to make crucial business decisions Data mining is not a product that can be bought Data mining is a discipline and process that must be mastered a whole problem solving cycle http www statoo com en datamining 3 What is exploratory data analysis EDA Exploratory Data Analysis EDA is an approach philosophy for data analysis that employs a variety of techniques mostly graphical to maximize insight into a data set uncover underlying structure extract important variables detect outliers and anomalies test underlying assumptions develop parsimonious models and determine optimal factor settings http www itl nist gov div898 handbook eda section1 eda11 htm 4 What is algorithmic statistics Nothing firmly defined but computationally intensive methods such as classification and regression trees CART multiplicative adaptive regression splines MARS projections pursuit PP alternating conditional expectations ACE have come from a small group of statisticians working in what has been described as algorithmic statistics The methods originate by tweaking computations and iterations rather than from assumptions about probabilistic distributions 5 How do these definitions relate to this course This course bridges the areas of exploratory data analysis statistical algorithms and data mining A description that emerged from a previous class is Like an explorer crossing unknown lands we want first to simply describe what we see Begin by examining each variable by itself then move to study relationships between variables Begin with a graph or graphs then add numerical summaries Look for anomalies deviations from the norm don t just summarize 6 How does the textbook relate to the course Many of these tools have common underpinnings but are often expressed with different terminology This book describes the important ideas in these areas in a common conceptual framework While the approach is statistical the emphasis is on concepts rather than mathematics From the book description http www stat stanford edu tibs ElemStatLearn The book s coverage is broad from supervised learning prediction to unsupervised learning 7