Foundations of Financial Engineering
Issued by
WorldQuant University
Badge earners complete 2 graduate-level courses (Financial Markets and Financial Data). Through hands-on examples, they master practical concepts such as credit risk, volatility, correlation, leverage, liquidity, and regulation. They integrate traditional and alternative data sources, including news, social media, and climate data. They apply quantitative techniques for structuring, normalizing, visualizing, summarizing, transforming, and analyzing data with Python to build financial models.
- Type Learning
- Level Advanced
- Time Months
- Cost Free
Skills
Earning Criteria
-
Earners of this badge have successfully completed the first two courses of the Master of Science in Financial Engineering Program at WorldQuant University. They have earned 8 semester credit hours, maintained a cumulative average score of 80% or higher, completed the program within the required time frame, and demonstrated that they are able to:
-
Build hands-on skills by applying linear algebra techniques using Python to summarize and filter both structured and unstructured financial data, to prepare data sets for models in econometrics, machine learning, and deep learning.
-
Develop a quantitative and computational toolkit of visualizations and data transformations that prepares data for further investigation of the challenges of credit risk, volatility, liquidity, nonlinearity, leverage, regulation, and model failure with ethical principles in mind.
-
Analyze and solve financial problems by applying collaborative and critical thinking skills and develop clear and concise technical and non-technical reports to clearly communicate results.