What is data science?
Data science has emerged as a leading career path across many sectors, including quantitative finance. It combines statistical techniques and mathematical finance with empirical research and programming methods to analyze large data sets, obtain insights on patterns, and make predictions for future trends, risks, and investment opportunities. Data science encompasses several stages in what is sometimes called the data science life cycle. These stages include:
- Data acquisition which includes data capture and data extraction
- Data maintenance which includes data cleansing and architecture
- Data processing which includes classification, clustering, and modelling
- Data analysis which includes exploration, regression, and prediction
- Data reporting which includes visualization, decision making, and communication
As part of the current data science revolution, machine learning offers a set of techniques for manipulating and learning from data, where computers themselves are set up to discover patterns and make decisions within a set of parameters. Machine learning proceeds by taking in data, training on that data, testing relationships, and developing solutions to problems. Although it requires knowledge to implement high quality machine learning research frameworks, there are many techniques to draw on, from supervised and unsupervised learning to deep learning, reinforcement learning, neural nets, and other methods that can help inform decision-making processes and handle complex data sets in a way that is not attainable through human analysis alone.
Taken together, data science and machine learning are powerful tools for those skilled in probability, statistics, and programming and there is high demand for professionals with appropriate education and experience.
Data science in quantitative finance
Data science has become a prominent feature of quantitative finance, which has always drawn on mathematical and statistical approaches for investment and risk analysis.
Within investment roles, typical tasks include pricing, valuation, portfolio construction and optimization, trading, and risk analysis. When completing these activities, traditionally, quants used Excel and C or its variants for working with financial data. As the field evolved, quants also started using the statistical programming language R for data science. More recently, there has been a significant push towards Python for data science, along with the more general trend towards machine learning in quantitative finance as a set of useful techniques for handling vast amounts of data that are generated by the markets across all asset classes. Since so much of quant finance is dependent on historical and empirical analysis, data science is a natural fit for quants. Examples of specific applications include:
- Time series analysis, including correlation, where relationships between variables are discerned, and cointegration, where the closeness of their movements together is evaluated
- Performance measurement, where the risk and reward of a given investment are weighed and assessed over time
- Risk management, where the volatility of a single asset, group of assets, or the broader financial markets is analyzed and the portfolio is hedged or the risk mitigated through other means
- Simulations, including Monte Carlo simulation, where a wide range of outcomes are generated to show both norms and outliers, creating additional insights for those making investment or risk-related decisions.
Courses in data science
In order to develop skills and a deep understanding about the use of data science in quant finance, job candidates will typically study related fields (math, statistics, finance, or programming) for their undergraduate degrees and then specialize further with professional certification programs. The demand for data scientists is strong and high-quality certificate programs, like the Certificate in Quantitative Finance (CQF), are an excellent way for those eager to stay in the job market to complete their education while remaining employed full-time.
Careers in data science
Over the past decade, data scientists have become an essential component of the quant finance industry. People who excel in this space are highly trained, data-oriented thinkers with strong technical skills and a desire to learn more. They work with large data sets, design algorithms, and develop machine learning platforms with their peers in engineering to process and synthesize information and produce insights and strategies for their colleagues in portfolio management, risk management, and trading. The role of data scientist has been noted as one of the top jobs in the US and around the world for several years and the employment picture for quants in this light should be positive for years to come.
Specific job titles and responsibilities for data scientists in quant finance include:
The role of a data scientist draws on a combination of technical roles, including statistician, scientist, mathematician, and computer programmer. The job entails collecting, cleaning, analyzing, and interpreting vast data sets through predictive modeling and machine learning techniques to detect patterns, trends, and relationships in data sets.
Data analysts use descriptive statistics to evaluate problems, create data visualizations, and develop insights based on empirical analysis. They may assist with collecting and cleaning data sets, and supporting the senior members of the data science team. Similar to the skill sets of data scientists, data analysts will have strong statistical and mathematical training, programming skills in R, SAS, or Python, knowledge of machine learning techniques, and experience with data management/wrangling, including visualization.
Data engineers build systems that collect, manage, validate, and convert raw data into high-quality, usable information for data scientists to study. They will have even deeper knowledge of programming languages, including Python and Java, for example, and they will also have expertise in databases and their management including SQL variants and Apache Hadoop.
Education is essential for anyone interested in pursuing a career path in data science. With modules and advanced electives focused on data science and machine learning, the CQF is a valuable asset for quant finance professionals looking to enter this field. Designed to be completed alongside full-time work, the CQF teaches the practical implementation of cutting-edge quant finance and machine learning techniques used by industry practitioners. As one CQF alumni, Mehdi Bourai, put it,
The data science portion of the program was very interesting. I had significant experience working with data but finding ways to make better use of the data we have at our disposal is critically important. So, with that in mind, the CQF helped me move to a different level in my career. I am now at Amundi in ESG investment and using a great deal of alternative data for data modeling, data cleansing, and analytics.
Given the high demand and professional perks of this exciting field, it is an ideal time to consider the CQF, which offers rigorous training in the mathematics, finance, and programming needed to launch a career in data science for quant finance. For more information on the program, download the CQF brochure today.