From data analysis to data science

Data science can be traced back to the convergence of statistics, mathematics, and computer science, emerging as a response to the increasing volume, velocity, and variety (commonly known as “The 3 V’s”) of data, and the necessity to extract valuable insights efficiently. 

In the past, data analysis primarily dealt with small datasets completely observable to the naked eye. However, organisations are now generating data repositories that get significantly bigger every year. Thus, it became evident to data analysts that traditional methods were inadequate to cope with the sheer volume of datasets, prompting them to explore alternative methods of data processing and analysis, which came to define the data science discipline.

Data science is now ubiquitous in retail, security, healthcare and many more industries. One of the key factors driving the popularity of data science is the utilisation of data visualisations and, particularly, artificial intelligence (AI). AI encompasses three stages:

• Stage 1 is artificial narrow intelligence (ANI), which involves machine learning (ML) techniques that specialise in one area and solve one problem.
• Stage 2 is artificial general intelligence (AGI), representing a computer system that possesses intelligence comparable to that of a human being.
• Stage 3 is artificial superintelligence (ASI), referring to an entity surpassing human intelligence in every field.

ChatGPT

ChatGPT, a highly specialised ML model, is a prominent example of a Large Language Model (LLM) capable of engaging in human-like conversations. Its proficiency extends across various tasks, including composing emails, writing articles, and programming code. The development of ChatGPT is a product of the latest advancements in data science, leveraging vast amounts of publicly available data sources, such as Wikipedia, news articles, and research papers, to create content. 

Data scientists have harnessed the power of ML models to efficiently generate accurate summaries and extrapolate insights when provided with a question or prompt. While ChatGPT’s abilities are impressive, it lacks the application of knowledge across diverse tasks and domains, which distinguishes it as ANI rather than possessing the broader capabilities exhibited by humans.

AI in systematic investing

At Prescient Investment Management, we have amassed millions of data points and thousands of financial and economic time series. To derive meaningful insights, we employ numerous processes that incorporate quantitative models grounded in rigorous mathematics. In addition to our quantitative analytics, we have embraced data science methods and frameworks to extract valuable information from our data. 

Benefiting from our 25-year track record, we possess a collection of proven research and models that serve as benchmarks for the latest AI tools. This allows us to incorporate AI in a controlled manner, augmenting our existing models, creating alternatives to existing quantitative models, and, after thorough testing, even develop entirely new models that were previously unattainable through traditional quantitative approaches.

It is crucial to clarify that, while we employ machines and algorithms as supportive mechanisms, they do not autonomously make investment decisions. Our human expertise and judgement remain integral in utilising these tools effectively and making the final investment decisions. For systematic investing, data science, together with tools such as ChatGPT, is necessary to traverse the data landscape and is no different than the calculator lying on your desk. DM

Author: Zubair Patel – Co-Head of Data Science at Prescient Investment Management.

Disclaimer: 

• Prescient Investment Management (Pty) Ltd is an authorised financial services provider (FSP 612).
• The value of investments may go up as well as down, and past performance is not necessarily a guide to future performance.
• There are risks involved in buying or selling a financial product.
• This document is for information purposes only and does not constitute or form part of any offer to issue or sell or any solicitation of any offer to subscribe for or purchase any particular investments. Opinions expressed in this document may be changed without notice at any time after publication. We therefore disclaim any liability for any loss, liability, damage (whether direct or consequential) or expense of any nature whatsoever which may be suffered as a result of or which may be attributable directly or indirectly to the use of or reliance upon the information.