Two new courses!

We're back with two great courses! Learn quantitative risk managment and how to write efficient code! In the newest course in our applied finance curriculum, you learn all about quantitative risk management, or QRM. In Writing Efficient R Code, you'll learn how to optimize your code to be lighting fast! [DataCamp]( New Courses! [Risk Management]( Quantitative Risk Management in R Taught by Alexander J. McNeil, Professor at the University of York. In Quantitative Risk Management (QRM), you will build models to understand the risks of financial portfolios. This is a vital task across the banking, insurance and asset management industries. [Play now ›]( Writing Efficient R Code Taught by Colin Gillespie, Assoc. Prof. at Newcastle University, Consultant at Jumping Rivers The beauty of R is that it is built for performing data analysis. The downside is that sometimes R can be slow, thereby obstructing our analysis. For this reason, it is essential to become familiar with the main techniques for speeding up your analysis. [Play now ›]( Quantitative Risk Management: What You'll Learn Chapter 1: Exploring market risk-factor data Learn how to form return series, aggregate them over longer periods and plot them in different ways. Chapter 2: Real world returns are riskier than normal Learn about graphical and numerical tests of normality, apply them to different datasets, and consider the alternative Student t model. Chapter 3: Real world returns are volatile and correlated Learn about volatility and how to detect it using act plots. Chapter 4: Estimating portfolio value-at-risk (VaR) In this chapter, the concept of value-at-risk and simple methods of estimating VaR based on historical simulation are introduced. [Discover QRM ›]( Writing Efficient R Code: What You'll Learn Chapter 1: The Art of Benchmarking In order to make your code go faster, you need to know how long it takes to run. Chapter 2: Fine Tuning: Efficient Base R R is flexible because you can often solve a single problem in many different ways. Some ways can be several orders of magnitude faster than the others. Chapter 3: Diagnosing Problems: Code Profiling Profiling helps you locate the bottlenecks in your code. Chapter 4: Turbo Charged Code: Parallel Programming Some problems can be solved faster using multiple cores on your machine. This chapter shows you how to write R code that runs in parallel. [Be Super Fast ›]( DataCamp Inc. 2067 Massachusetts avenue Cambridge MA 02140 [Unsubscribe](