Prep Before You Start

UCLA Anderson MFE offers a Python Workshop to prepare you for the Financial Engineering curriculum. These required online sessions are being offered at no cost to all incoming UCLA Anderson MFE students.

Course Description: This workshop is specifically designed to review Programming in Python. It will cover general Python, Python for Data Science, and Machine Learning.

The class will consist of an introductory session and 7 lectures. Each lecture will be 3 hours and include a break. Assignments will be given, and the instructor will be available for support throughout the course.

Schedule:

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.

Required Course Materials:

Students should rent or purchase the e-book version of the textbook:

Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud 
by Paul Deitel and Harvey Deitel (©2020 Pearson Education e-book)

Below are the Kindle, Pearson+, VitalSource and RedShelf links to the e-book versions of the textbook:

- Amazon Kindle
- Pearson+ (U.S. only)
- VitalSource
- RedShelf

UCLA Anderson MFE offers a Math Bootcamp to prepare you for the Financial Engineering curriculum. These required online sessions are being offered at no cost to all incoming UCLA Anderson MFE students.

Course Description: This bootcamp is specifically designed to review math concepts used in the MFE program.

Schedule:

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.

View Course Syllabus

UCLA Anderson MFE offers an R Workshop to prepare you for the Financial Engineering curriculum. These required sessions will be held in person on campus following Orientation and prior to the start of classes.

Course Description: This workshop is specifically designed to review Programming in R. The workshop will consist of 3 sessions. Each session will be 3 hours and include a 1-hour break in the middle.

Last Year's Schedule for Reference (New Schedule Coming Soon):

Students will receive an email with additional information prior to the workshop.

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.

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.

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 can take Python, R, SQL courses at your own pace. You will need to use your @anderson.ucla.edu or @ucla.edu email to access the group.

Recommended DataCamp Courses: Select from the courses below to prepare yourself as needed.

• Skill Tracks: https://www.datacamp.com/tracks/skill
  • R
    • R Programming: R Programming (datacamp.com)
    • Importing & Cleaning Data: Importing & Cleaning Data with R (datacamp.com)
    • Data Visualization: Data Visualization with R (datacamp.com)
    • Tidyverse Fundamentals: Tidyverse Fundamentals with R (datacamp.com)
  • Python
    • Python Fundamentals: Python Fundamentals (datacamp.com)
    • Python Programming: Python Programming (datacamp.com)
  • SQL
    • SQL for Business Analysts: SQL for Business Analysts (datacamp.com)
    • SQL for Database Administrators: SQL for Database Administrators (datacamp.com)
  • Tableau (Good for visualizations and plots)
    • Tableau Fundamentals: Tableau Fundamentals (datacamp.com)

• Career Tracks: https://www.datacamp.com/tracks/career
  • R
    • R Programmer: R Programmer (datacamp.com)
  • Python
    • Python Programmer: Python Programmer (datacamp.com)

• Courses (shorter: approx. 4-hour courses): https://www.datacamp.com/courses
  • R
    • 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
  • Python
    • Introduction to Python: Introduction to Python | DataCamp
    • Intermediate Python: Intermediate Python | DataCamp
    • Data Manipulation with pandas: Data Manipulation with pandas | DataCamp
  • SQL
    • Introduction to SQL: Introduction to SQL | DataCamp
    • Joining Data in SQL: Joining Data in SQL | DataCamp

View Here for free courses offered by the UCLA Institute for Digital Research and Education.

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)

• Lab 1: Thursday, May 23, 2024 - The Power of Integrity - Register Here
• Lab 2: Thursday, June 6, 2024 - The Power of Authenticity - Register Here

*Optional but recommended leadership series. Online via Zoom.

These required sessions will be held online via Zoom. They are designed to help prepare you for quantitative interviews in the financial engineering field.

Last Year's Schedule for Reference (New Schedule Coming Soon):

Students will receive an email with additional information prior to the start of QIPS.