Data science and machine learning are intricately linked
Chad Gouws, Data Analyst at PBT Group
Data scientists must identify business areas that can be improved on, determine the most efficient solutions to do so, and build machine learning (ML) models to best enable this. Understanding data and maintaining it become critical to accomplish this. By integrating ML with data science, these specialists can significantly optimise the operating environment.
Even though it is artificial intelligence (AI) that has been garnering significant media interest in recent months, ML provides the nuts and bolts for companies to realise the automation of tasks that normally require human intelligence. For its part, ML can be defined as the use and development of computer systems that are able to learn and adapt without being explicitly programmed. These systems use algorithms and statistical models to analyse and draw inferences from patterns in data.
Of course, none of this can happen without having data science in place. This interdisciplinary field uses scientific methods, processes, and algorithms to extract insights from structured and unstructured data. In turn, this knowledge is applied to solve business problems. ML can only provide quality insights if it receives quality data. Therefore, without a foundation built on clean, consistent, and quality data, little (if any) meaningful insights can be produced.
At the same time, the data scientist needs ML because it is virtually impossible for a person to comprehend and accurately predict outcomes from the vast amounts of complex data the organisation has at its disposal. This shows that the success of ML and data science is largely dependent on each other, so it is always important to consider each of the fields from the other’s perspective.
A skilled approach
A data scientist therefore needs to be well-versed in mathematics and statistics, especially as it relates to the ML models they are working with. They also need a solid understanding of computer science concepts, mostly around programming languages such as Python, R, and SQL. These are the tools they use to perform research, query data, and build datasets and ML models.
The data scientist should also have an excellent business sense to understand what it is that makes the business successful, where it can be improved on, and what the likely options to achieve that outcome are. ML can be considered the applied statistics to accomplish this. It is the integration of computer science and various fields of mathematics, where computer science concepts are used to build robust mathematical models that can solve a set of similar and related problems.
The data scientist is concerned with ensuring that the ML model achieves the objectives of the project. Again, this is where a business skill set is perhaps the most crucial skill to have. To succeed with the ML model development, the data scientist must have a reasonable understanding of the problem at hand and the objectives of the project. Without this in place there is little likelihood of success for any data science programme and ML model.
To this end, virtually 80% of a data scientist’s time is spent exploring, cleaning, and preparing data. Getting this done correctly is a vital part of the process. Once completed, the data scientist can then start with the ML model development. They can test and compare various models and then optimise the most promising candidate to roll out into the production environment.
One of the most effective ways to see adoption of these ML models is through data visualisation. Telling a story with the data enables business leaders to make more informed decisions that can benefit the organisation. Apart from preparing data, this visualisation is perhaps the most important step in helping to ensure a project’s success.
So, even though the ML models are important, their success relies heavily on the data team’s ability to understand and provide clean, structured data with excellent information that allows the model to make accurate predictions. With ML being domain or business agnostic, these models can be used in different context to maximise their potential.
Data science and ML are interdependent on one another and become critically important for the success of a data-driven organisation. However, it all comes down to the quality of the data being used.