MFE需要很高的统计知识和data mining的知识吗？

问题如标题，求解答~

啥叫很高的統計？。。。
看學校的要求吧。。。
這裡有Haas的要求 LZ可以參考一下：

. 1point3acres.com “Requirement:
A strong quantitative background including multivariate calculus, linear algebra, differential equations, numerical analysis and advanced statistics and probability.”

統計的Suggestion：
2 courses (one introductory, one advanced)
Examples:
LOWER DIVISION:
5. Probability models for random experiments. Random variables. Expectation and variance. The normal approximation. Testing hypotheses. Non parametric tests. Point estimation. Bias and variance of estimates. Ideas of experimental design. Illustrations from many fields.
20. Relative frequencies, discrete probability, random variables, expectation. Testing hypotheses. Estimation. Illustrations from various fields.
21. Descriptive statistics, probability models and related concepts, sample surveys, estimates, confidence intervals, tests of significance, controlled experiments vs. observational studies, correlation and regression.
25. Emphasis on concepts and applications. Conditional probability. Independence. Expectation. Standard discrete and continuous distributions. Regression and correlation. Point and interval estimation. Illustrations from engineering.
UPPER DIVISION:
101. Introduction to the Theory of Probability. Random variables and their distributions, expectation, univariate models, central limit theorem, statistical applications, dependence, multivariate normal distribution, conditioning, simulation, and other computer applications.
102. Introduction to the Theory of Statistics. Least squares estimates, t tests, F tests, and the application of these procedures to the design and analysis of experiments. Maximum likelihood estimates, Wald test and likelihood ratio tests in the context of logistic regression and Poisson regression. Computer-based applications.
134. Concepts of Probability. An introduction to probability, emphasizing concepts and applications. Conditional expectation, independence, laws of large numbers. Discrete and continuous random variables. Central limit theorem. Selected topics such as the Poisson process, Markov chains, characteristic functions.（這個應該是每個MFE的孩紙都要懂的。。。）
135. Concepts of Statistics. A comprehensive survey course in statistical theory and methodology. Topics include descriptive statistics, maximum likelihood estimation, goodness-of-fit tests, analysis of variance, and least squares estimation. The laboratory includes computer-based data-analytic applications to science and engineering

數學就很明顯了：所有的calculus，differential equations，linear algebra，還有numerical analysis（就是用matlab的）

其他Requirement請看source：
http://mfe.berkeley.edu/academics/prerequisites.html

data mining不是stat graduate school 才学的吗。。？很少人本科就学了吧。（我根本不知道那是干嘛的。。）

不要什么统计，最主要的数学基础是Stochastic calculus, Differential equations, Numerical method...

很高的统计肯定是逃不了的
data mining就不知道了

PS：數學好的童鞋就跳過lower division 吧

data mining不是stat graduate school 才学的吗。。？很少人本科就学了吧。（我根本不知道那是干嘛的。。）
小TT 发表于 2011-12-11 04:38

. ----本科有啊。。。Engineering裡面很多data mining的課。。。

data mining不是stat graduate school 才学的吗。。？很少人本科就学了吧。（我根本不知道那是干嘛的。。）
小TT 发表于 2011-12-10 14:38

    貌似确实一般都是graduate school才有，我们学校statistic没有，只有CS开了这个课

几乎每一个MS的金工都要学随机过程
几乎每一个MS的金工的prerequisite course里面都有Possibility & Statistics
另外4L的回答里面："advanced statistics and probability”赫然在目
3L竟然说不要什么统计
学数学的只能说是笑而不语了

呃
怎么越看LZ的照片越像我一个朋友
不淡定了