Basic concepts of mathematical statistics for IT professionals

It is hard to imagine a successful IT project without mathematical statistics. This tool is useful for data analysis, machine learning, and many other complex tasks. Let's say you work with large amounts of data. How can you determine which data is relevant and which is not worthy of attention? How do you make accurate predictions based on the available information? This is where concepts like mean, variance, correlation, and probability come in handy. Knowing how to apply these concepts of mathematical statistics to IT will greatly increase your work efficiency and enable you to make informed decisions. Let's explore the theoretical aspects together! In addition, in this article, we will focus on examples and variants of practical application, and consider real-life cases from the IT industry.

Fundamental concepts of mathematical statistics for IT

1. Mean.
   The mean or mathematical expectation is one of the simplest and yet most important concepts of mathematical statistics for IT. It shows the average result from a data set and is often used to analyse typical values in large amounts of information. This concept of mathematical statistics is often used in IT.
   • Performance analysis. The average can be used to estimate the approximate time to complete certain operations. For example, to analyse how long it takes to load web pages.
   • Detection of anomalies. With this mathematical statistic, you can easily detect deviations in the data. If certain values are significantly different from the average, this may indicate problems or errors that require special attention.
   • Resource planning. For example, knowing the average load on servers during the day can help you plan their use more efficiently and ensure stable operation of your systems.
2. Dispersion.
   Variance helps determine how much the values in a data set deviate from the mean. It is the main indicator of information variability. In the context of mathematical statistics for IT, variance is important for understanding the stability of systems. A low value indicates that the system is stable and predictable, while a high variance may indicate problems. This concept of mathematical statistics also has other applications in IT.
   • Performance evaluation. Variance is used to analyse the performance of a system or application. For example, if you are measuring server response time, high variance can indicate unpredictable delays that need to be investigated further.
   • Risk prediction. In the field of cybersecurity, variance can help assess risks. A large variation in the data usually indicates possible vulnerabilities or abnormal behavioural patterns that require immediate intervention by a specialist.
3. Correlation.
   Correlation measures the relationship between two variables. It is an important mathematical statistics tool that is often used in IT to identify patterns.
   • Analysis of relationships between data. Correlation allows you to identify relationships between different data sets. In particular, you can study the connection between user activity on the website and their purchases to understand what exactly contributes to the increase in sales.
   • Improving machine learning models. In machine learning modelling, the concept of mathematical statistics is used to select significant variables. Variables with a high correlation are used to build more accurate and advanced models.
   • Monitoring and diagnostics of systems. Correlation in IT helps to identify problems in systems by analysing the relationships between different metrics. For example, if server load correlates with response time, it may indicate the need to optimise resources.
4. Probability.
   Probability determines how likely it is that a certain event will occur. This numerical characteristic of mathematical statistics is the basis for making many decisions under conditions of uncertainty.
   • Prediction. Using this indicator in IT, you can predict the behaviour of systems and users. For example, the probability of successful completion of a transaction in an online store helps to plan inventory. And in email systems, probability is used to determine whether a message is spam.
   • Risk analysis in IT. Mathematical statistics make it easier to understand the degree of risk in different situations. Cybersecurity specialists assess the probability of attacks and take preventive measures to protect systems.
   • Optimisation of algorithms. In the field of machine learning, probability is used to build classification and regression models that are needed to predict outcomes based on available data.

So, if you want to succeed in IT, you can't do without mathematical statistics. At OPTIMA College, it is one of the compulsory subjects in the Computer Science course. Let's learn the most interesting things together!