Data Science vs Machine Learning: What’s the Difference?

Data Science is concerned with methods of analysis that allow people to gain insights from complex data. It encompasses statistics, data analysis, together with data quality, provenance, governance, ethics, and regulation. This entire process, plus the evaluation of the filtered data for building patterns and finding trends, enables recommendations to be made and optimisations to occur, generating business innovations. For example, when shopping online, someone might select preferences and filter products, resulting in a few recommended products – this process is a result of data science. Data Science overlaps with AI and Machine Learning.

However, whilst data science is based on the data, learning (e.g. our ability to learn a new skill or learn to recognise a new type of object) is the focus for machine learning, and that is where the difference lies. Machine learning is concerned with non-linear predictive models inferred from data and applied in a wide range of fields, including AI (e.g., for predicting protein folding from amino acid sequences, equity movements in financial markets, novel images of natural scenes). This enables a machine to generate powerful predictive models without handcrafting by humans. The model frameworks are quite simple in themselves, with the predictive power coming from the regularities in the data that they capture.

At its core, data science deals with structured and unstructured data, aiming to use a scientific approach to extract meaning and insights from this data. This involves using data to find all viable solutions and then finding the best way to progress from these alternatives. Data science creates a system that interrelates these and helps the business to move forward. However, machine learning uses techniques to learn from the data and predict future outcomes.

Machine Learning involves a series of commands, details, or observations as inputs to prepare for potential predictions without human involvement. Machine learning and data science techniques can work synonymously. For example, data science might help to define recent problems that can be solved using machine learning techniques and statistical analysis, as opposed to structured and unstructured data.

Data science relies on human creativity and intuition to understand complex problems. Without humans, data science would operate on an incomplete picture. Data scientists in business work in teams to mine big data for information that can help to predict consumer behaviour and identify new opportunities. They also aid many organisations with setting best practice for the collection of data, using analysis tools and interpreting data. Some skills a data scientist might need are:

• Statistics
• Data mining and cleaning
• Data visualisation
• Unstructured data management techniques
• Programming languages such as R and Python
• Understand SQL databases.

This is because algorithms often struggle to grasp some realities that only the human mind can comprehend as they follow mathematics. For example, during the COVID-19 pandemic, certain algorithms could not understand the rationale for changing buying behaviours.

However, in machine learning, whilst human involvement can predict anomalies and manage the systems, it is arguably less so than data science, depending on the level of supervision. An unsupervised learning system is based on algorithms that learn from data with unlabelled elements looking for patterns or relationships between them. This does not require human involvement. However, supervised systems, which are based on algorithms that learn from data with tagged elements, require human involvement. This is because expected input and output data are provided. Therefore, algorithms can be classification algorithms, to classify objects within classes, or regression algorithms, to predict a numerical value. Someone monitoring these systems is called a Machine Learning Engineer, who requires skills such as:

• Computer science fundamentals
• Statistical modelling
• Data evaluation and modelling
• Understanding and application of algorithms
• Natural language processing
• Data architecture design
• Text representation techniques.