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Statistical Inference for Financial Engineering

D co-advised with T. Bielecki : Expected Graduation: Spring Bielecki, Z. Cheng, and I.


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Cialenco , Submitted, Submitted, Cheng and I. Bielecki, I. Cialenco, and Y. Mou and A. Liu, X. Chen, L. Cai, Q. Chen, and D. Chen, Z. Cai, and Y. Cialenco and Y. Statistical Inference for Stochastic Processes, 21 1 , pp. Journal of Theoretical Probability, 31 2 , pp. I dont want to take away the credits from duke uni or Dr Mine Rundel. She has done great work. I went through her book os and read pdfs. I am from engineering background. Time consuming, but this course is well worth the money on signature track!

This will give you a very solid understanding of statistic, which is the basic of so many other fields: experimental research, lean, and machine learning just to name a field. Unmissable if you want to broaden your knowledge on how to do things with scientific rigor. Very time consuming course still doesn't contain any proofs. All the time spent just to make you memorise quite simple concepts but not to understand why do they work. Don't wast your time, go through book and pdf slides and you will end up with the same knowledge many times faster.

Though this can be frustrating, it ultimately drives you to buckle down and learn the material. Those who cannot devote a decent amount of time to the course will feel lost if they have no former introduction to the topic. If you can devote the time, it is rewarding and very well taught. Highly recommended. Very high quality materials, presented and explained extremely well and with a lot of real world practical examples which most stats courses do not have. It is a high workload and a high pass mark but you will get real, useful knowledge.

Thisi is one of the best course I ever took online or at university. The content is challenging but the professor explains it so well that it is a pleasure to come back for more every week. Its not easy, you have to work a lot to succeed but if you do, you will be very happy with this class. One of the very best courses - instructor was engaging, course material was challenging but extremely interesting.

I find myself referring to this material repeatedly in my data science work. I took this course a while back before it changed format. From the other reviews, I see that that it is still a great course.

Real vs Fake Financial Engineering Degrees

I had no background in statistical inference but lots in programming before taking the course so the R programming was fairly trivial and I could concentrate on learning data analysis and statistical inference. The material was presented methodically and with many worked examples. This is not a 'fast-paced' course. If you need to cram material, this isn't the right course for that. This course teaches you the language of the scientific method - how to articulate a hypotheses that can be disproven. Like any language, the art of speaking it takes time and practice. A wonderful course on data analysis and statistical inference as well as the use of R and R Studio, although there is the option for students to complete the assignments online using a DataCamp platform.

I found the exercises and especially the project an extremely interesting endeavour, and the provision of an open-courseware textbook is a very nice gesture by the instructor. For other learners, particularly those who are not particularly after a certificate and don't want to wait till the next offering of the course, it is also possible to cover the same course material through the DataCamp platform at your own leisure!

Hiring Initiative in Data Science

One of the best MOOCs out there, not really much to say besides that. Very time consuming if you choose to join the track with programming assignments. However, they are totally worth your time. The recommended exercises are also useful, and the final project was interesting as well.

1. Introduction

It's just the perfect MOOC for me, at least. Very good practical oriented course. All concepts clearly explained. Many useful examples from real world are demonstrated in detail. Flow is very smooth from basic to advanced topics. Mine is one of the best professors I've known. She can communicate arcane concepts very effectively and her passion is seen in every lecture. Forty units are required to complete the degree.

Statistical inference - Wikipedia

Twelve of those units are elective courses, which you can choose from the options below. Surfactant-driven thin film flows in biomedical applications; Nonlinear parabolic equations; Stability problems in fluid dynamics; Scientific computations; Applied operator theory; Sturm-Liouville problems. Applied mathematics, Industrial modeling, Differential equations, Fluid mechanics, Wave propagation, Semi-conductors. Statistical inferences, Stochastic differential equations, Stochastic modeling, Simulation, Machine learning, Approximation theory, Graph theory.

Discrete optimization; Network models; Statistical physics; Random combinatorial structures.

Optimal Statistical Inference in Financial Engineering

Stochastic processes, Statistics, Risk management, Financial derivatives, Actuarial sciences, Statistical software. Corporate finance, Investments, Economics of strategy, Macroeconomics, Nonprofits, Corporate culture. The Financial Engineering program offers its students the exclusive opportunity to participate in an exchange program at the University of Lausanne, located in the beautiful country of Switzerland. Known for its banking system and situated at the heart of Europe, Switzerland offers our students the opportunity to participate in financial forums and engage in stimulating financial discussions, both with world-renowned faculty and seasoned professionals.