The MFE is an intensive and challenging program that picks up pace quite early on. We’d strongly recommend that students start investing some of their time, between now and Fall, towards preparing for the program.
The coursework is very hands-on and requires programming in multiple languages. Below, we’ve put together a list of some resources that we think might help students (especially those with limited programming background, or those that need math refreshers) develop working level familiarity with some of these tools (staple for any FE).
UCLA Anderson MFE Math & Programming Bootcamp
UCLA Anderson offers a Math and Programming Bootcamp to prepare you for the Financial Engineering curriculum. These online sessions are being offered at no cost to all incoming UCLA Anderson MFE students.
Course Description: This bootcamp is specifically designed to review Programming in Python, Probability, Statistics, Linear Algebra, Calculus, and Optimization concepts used in the MFE program.
The class will consist of 3 sessions per week (approx. 2 hours each) for 6 weeks and will include in-class work. This is a non-credit/non-graded class.
Reference books: Students are not required to purchase textbooks for these sessions. However, the following books are for reference:
- Numerical Recipes: The Art of Scientific Computing, 3rd Edition, by W. H. Press and S. A. Teukolsky;
- Mathematics for Economists, 2nd edition, by C. P. Simon and L. E. Blume;
- Methods of Multivariate Analysis, 2nd edition, by Rencher;
- Econometric Analysis, 6th edition, Greene;
- Introduction to Differential Equations, 2nd edition, by R. Miller;
- Introduction to Partial Differential Equations with Applications, by E. C. Zachmanoglou and D. W. Thoe.
View Syllabus (*From Summer 2021 - new version coming soon.)
Prerequisites: prior knowledge of linear algebra, multivariate calculus, differential equations, numerical analysis, advanced statistics and probability
Students are encouraged to attend live online sessions, but will also be able to review lectures anytime during the week once they have been recorded.
Students will receive an email with website and login information the week prior to the first day of class.
Finance and Economics Prep
The following finance and economic prep enhances a candidate's competitiveness for placement in internships and full-time positions.
- Read "Options, Futures and Other Derivatives" by John Hull
- Read "Heard on the Street: Quantitative Questions from Wall Street Job Interviews" by Timothy Falcon
- Read "Corporate Finance" by Ivo Welch
- Read "A Practical Guide to Quantitative Finance Interviews" by Xinfeng Zhou
- Read "My Life as a Quant: Reflections on Physics and Finance" by Emanuel Derman
- Read "How I Became a Quant: Insights from 25 of Wall Street's Elite" by Barry Schachter
- Prepare for and pass the CFA Level I exam by June before starting the MFE Program. Students will not have time to prepare for the CFA exam during the MFE Program.
- Pass a course in Macroeconomics.
C++ Programming for Financial Engineering
Prior to the start of the MFE Program, it is recommended that all incoming MFE students complete C++ Programming for Financial Engineering offered by QuantNet. Important to note, the MFE program focuses on programming in Python and R. However, C++ is still very important for your job search as it continues to be a language required by many firms in financial services. For example, Morgan Stanley's test for summer internship that will take place in early October is in C++. Therefore, we would encourage you to register for the QuantNet C++ prep course if you are not proficient in C++.
Course Description: The Online Course is designed for people interested in pursuing graduate studies in financial engineering and covers essential C++ topics with applications to finance. With an emphasis on financial applications for quantitative finance, the course is also useful to professionals interested in learning one of the main programming languages used in the quantitative financial industry. The following topics will be covered:
- Basic C/C++ Language and Syntax
- Object-Oriented Programming (OOP) in C++
- Inheritance and Polymorphism
- Generic Programming in C++ and Standard Template Library (STL)
- An Introduction to Boost C++ Libraries
- Applications in Computational Finance
This 16-week course consists of 10 levels where students build their cohesive knowledge upon previously mastered material. Access to each level is granted upon successful completion of the previous level's homework and quiz. Each student is assigned a personal Teaching Assistant who will provide timely personalized feedback on homework as well as input on coding improvement. Upon successful completion of the course, students who pass the final exam and obtain a 70% or higher average will be issued a Certificate of Completion. A Certificate of Completion with Distinction will be awarded to students with 90% or higher average.
Prerequisites & Technical Requirements: No programming experience is required in order to follow this course. The course has been structured in such a way that you learn C++ from the ground up. You can freely download the Visual Studio Express compiler from the Microsoft site.
Other Programming Prep
Suggested Programming Prep:
In an effort to help you prepare for our program we have teamed up with DataCamp to provide you with a subscription to state of the art programming prep at no cost. You will be able to register your account once you have activated your UCLA Anderson email address.
Recommended DataCamp Courses: Select from the courses below to prepare yourself as needed.
- Skill Tracks: https://www.datacamp.com/tracks/skill
- Tableau (Good for visualizations and plots)
- Tableau Fundamentals: Tableau Fundamentals (datacamp.com)
- Career Tracks: https://www.datacamp.com/tracks/career
- Courses (shorter: approx. 4-hour courses): https://www.datacamp.com/courses
- Introduction to R: Introduction to R Online Course | DataCamp
- Intermediate R: Intermediate R | DataCamp
- Introduction to Tidyverse: Introduction to the Tidyverse | DataCamp
- Introduction to Data Visualization with ggplot2: Introduction to Data Visualization with ggplot2 | DataCamp
R Programming Workshop: A required R workshop is scheduled following orientation and prior to the start of classes. The workshop series will cover important programming skills that will be used in the first quarter and throughout the MFE Program.
View Here for free courses offered by the UCLA Institute for Digital Research and Education.
Other Math Prep
Suggested Math Courses to Prepare for MFE:
- Linear Algebra (through MIT): Linear Algebra | Mathematics | MIT OpenCourseWare
- Multivariable Calculus (through MIT): Multivariable Calculus | Mathematics | MIT OpenCourseWare
- Multivariable Calculus (through Udemy): Calculus III (Multivariable Calculus) | Udemy
- Differential Equations (through Udemy): Differential Equations In Depth | Udemy
- Complete Linear Algebra (through Udemy): Master Linear Algebra: From Theory to Implementation | Udemy
- Complete Linear Algebra (through Coursera): Mathematics for Machine Learning: Linear Algebra | Coursera
- Probability (through Coursera): An Intuitive Introduction to Probability | Coursera
- Advanced Statistics: (through UCLA Extension): Advanced Statistics and Quantitative Methods | UCLA Continuing Education Online (uclaextension.edu)
- Other Resources for Math Courses:
- NetMath at Illinois
- Stanford Center for Professional Development
- Partial Differential Equations: Mathematics 476: Courses: Athabasca University (must earn 80% or higher in this course)