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Hongjing student: Li

School: Rice University

Major: Applied Mathematics

Pass account: Exam P, FM, SRM

hello everyone! I am a sophomore majoring in Applied Mathematics at Rice University. In June this year, I passed the SRM exam. I have some experience about the exam and want to share with you.

First of all, I think the most important point is that the biggest difference between examination and review is that there are too many conceptual contents in the examination. SRM itself has many formulas to memorize, but in fact, it does not assess so many formulas in the exam, but more conceptual knowledge! For example, what data do you want to use, what methods do you want to use, or clustering, or k-means method. But generally speaking, the difficulty is not particularly high.

In the aspect of review and preparation for the exam, I think as long as you follow the teachers of Hongjing international education, carefully listen to the courses, and brush all the practice examms according to the teaching materials given by the academic administration teacher, you will win! Thank you~


Hongjing student: Wu

School: Beijing University of Technology

Major: Economics and management

Accounts passed: Exam P, FM, IFM, SRM

Hello, I’m a student of Hongjing. I just passed the SRM exam. Today I share my experience in preparing for the exam. I hope it will be helpful to the students who are preparing for the exam.

Exam SRM is called risk modeling statistics, but the content of the exam is not software analysis, but more theoretical and computational questions.

The content of this course is very structured, and the content to be mastered is also relatively clear. At the beginning of the first chapter, the textbook has a lot of introductions about nouns. If you don’t understand, don’t worry. I have a clear understanding of the whole course only after I finish learning it again and look back at the content of the first chapter.

Now I can introduce it to you, which is also convenient for you to have an understanding at the beginning. Firstly, according to whether there is y, it is divided into two major methods: supervised and not supervised. Supervised is divided into two methods of calculating parameters in the form of regression and non regression. The methods of calculating parameters by regression include ordinary linear regression and generalized linear regression, which are familiar to us. The focus of the investigation is the data selection, index calculation and test of X / y; Then there are KNN and decision tree methods that can regress and find parameters. These two methods are very interesting and you will learn later. Not supervised has only the parameter x, which corresponds to PCA and clustering methods. In this way, if you list the framework, you will have a grasp of the whole. Each test site starts from this framework and compares the use and comparison of methods in the framework.

Now let me talk about the exam. In the exam at the back of the textbook, there are less theoretical questions and more calculations. But in the exam, the theoretical questions are far more than the calculation questions. Therefore, we must chew through every knowledge point at ordinary times, read and understand the content of the textbook! Do more theoretical questions in the question bank. Calculation questions should not be ignored. Calculation questions can help us understand the calculation of indicators and theoretical questions.