In terms of artificial intelligence technologies, large language models are optimised for text (the clue is in the name!), deep learning was primarily aimed at processing images and other media and data analytics deals with numerical data
The term data analytics refers to the science of analysing raw data to make conclusions about information. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption. Data analytics can be used by different entities, such as businesses, to optimise their performance and maximise their profits. This is done by using software and other tools to gather and analyse raw data.
In practice, data analytics covers a wide spectrum of techniques from simple data analysis to really complex analyses.
Data analytics may be broken down into four basic types:
Descriptive analytics: This describes what has happened over a given period of time. Has the number of learners studying on an online learning platform gone up or down? Are sales stronger this month than last?
Diagnostic analytics: This focuses more on why something happened. It involves more diverse data inputs and a bit of hypothesising. Did the latest changes to the learning programme improve learner satisfaction and performance? Did that latest marketing campaign impact sales?
Predictive analytics: This moves to what is likely going to happen in the near term. What happened to learner engagement the last time we had a flu outbreak? How many weather models predict a hot summer this year? Will that discourage learners from studying inside, at their computers?
Prescriptive analytics: This suggests a course of action. For example, we should offer free flu vaccinations to staff if the likelihood of a serious flu outbreak is above 50%,
Data analytics underpins many quality control systems in the business world. It’s nearly impossible to optimise something if you aren’t properly measuring it, whether it’s your weight or the number of staff days lost to ill-health.
Data analysts can use several analytical methods and techniques to process data and extract information. Some of the most popular methods include:
- Time Series Analysis: Tracks data over time and solidifies the relationship between the value of a data point and the occurrence of the data point. This data analysis technique is usually used to spot cyclical trends or to project financial forecasts.
- Regression Analysis: This entails analysing the relationship between one or more independent variables and a dependent variable. The independent variables are used to explain the dependent variable, showing how changes in the independent variables influence the dependent variable.
- Factor Analysis: This entails taking a complex dataset with many variables and reducing the variables to a small number. The goal of this manoeuvre is to attempt to discover hidden trends that would otherwise have been more difficult to see.
- Cohort Analysis: This is the process of breaking a data set into groups of similar data, often into a customer demographic. This allows data analysts and other users of data analytics to further dive into the numbers relating to a specific subset of data.
- Monte Carlo Simulations: Models the probability of different outcomes happening. They’re often used for risk mitigation and loss prevention. These simulations incorporate multiple values and variables and often have greater forecasting capabilities than other data analytics approaches.
In the same way as the massive and growing amount of data has facilitated the training and development of large language models, so it has enabled the development of a type of data analytics known as Big Data. At its most basic, big data analytics is the process of examining large, complex datasets to extract valuable insights and knowledge.
The Big in Big data doesn’t just refer to the quantity of data but to five characteristics often referred to as the “5 V’s”: volume, velocity, variety, veracity, and value.
- Volume: The sheer amount of data is vast.
- Velocity: Data is generated and processed at high speeds.
- Variety: Data comes in various formats (structured, semi-structured, unstructured).
- Veracity: The accuracy and reliability of the data are important.
- Value: The insights derived from the data must be meaningful and actionable.
Together, they correspond to a major increase in the complexity of data analytics. At the same time, the sheer quantity of data available provides new opportunities analogous to the training sets for large language models.
Big data analytics uses a range of tools and techniques, including machine learning algorithms that allow computers to learn from data, data mining to discover patterns and relationships in data, traditional statistical analysis methods to analyse data and data visualisation to create visual representations of data to illustrate what is happening.
Big Data Analytics is designed to improve decision-making by uncovering hidden insights, so that organisations can make more informed data-driven decisions. This in turn should increase efficiency by helping to identify areas for improvement and streamline processes.
By collecting and analysing data on learners’ interactions, performance, preferences, and behaviours, developers can design adaptive learning paths. Data analytics helps identify each student’s strengths, weaknesses, pace, and preferred learning style, enabling the system to suggest relevant content, adjust difficulty, or recommend remedial resources. For example, if a learner struggles with a particular concept, the platform can automatically provide additional materials or practice exercises.
Big data allows developers to track which modules or resources are most (or least) engaging and effective. Analytics can reveal where learners drop off, spend extra time, or frequently ask for help, highlighting areas where course content can be improved, clarified, or made more engaging.
By monitoring metrics such as login frequency, assignment submission times, quiz results, and forum participation, predictive analytics can flag learners who may be disengaging or falling behind. Developers can set up automated alerts or interventions to encourage these learners or connect them with instructors for support.
Data analytics can streamline grading, track performance patterns, and provide instant, tailored feedback based on learner responses. This not only saves time but also helps learners understand their mistakes and guides their learning effectively. With aggregated usage data, developers can optimise technical resources (like server bandwidth or video streaming quality) and improve platform scalability. Analytics can inform decisions about where to invest in content updates, support personnel, or new features.
Analysing collective data across courses and cohorts can reveal trends, common challenges, and evolving learner needs. Developers can use these insights to innovate curriculum design, test new features, and measure the impact of interventions.
Tools to help developers leverage their data are often built into learning platforms. Systems like Coursera or Khan Academy use data analytics to recommend personalised review exercises and next modules based on a student’s learning history.