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0 Statistics Intro.pdf
- 1. Statistics and Data Mining Methods. Leonardo Auslender Leonardo ‘dot’ Auslender ‘at’ gmail ‘dot’ com 732 494 1555. September 2019
- 2. 4 Lectures: 1: EDA Principal Components Clustering 2: Linear Regression and Variable Selection 3: Logistic Regression and Variable Selection 4: Classification Trees, Gradient Boosting and ensembles. 5: Time permitting: Issues. Grading policy: Class participation, discussion and computer work, possibly homework. Originality and creativity are heavily rewarded, as long as they are disciplined. Since it is not possible to review all analytical aspects, emphasis is in thinking on the problem, finding out satisfactory solution and in being able to contrast it to alternatives. Also not possible to focus in depth on material. Read at your leisure, keep on reading, and focus on INTERVIEW questions. Some of the questions require lots of research.
- 3. “If there are no alternatives, then we are failing to think about the problem. Or else, there is no problem to begin with.”. Seminars offer extremely short and terse view of many analytical aspects of data analysis and problem conceptualization. Just a guide of areas that need extensive further review. Review, re-read, make mistakes, re-learn, etc. TURN OFF YOUR CELL PHONES FOR THE DURATION OF THE CLASS.