Nowadays, a lot of attention in e-commerce is paid to improving user experience. Due to a high competition on the market, e-commerce websites must provide services focusing on usability and quality of service. For this purpose they can detect possible problems by using data which represents customer behavior while navigating the website, described by the sequence of actions performed by a customer on the portal. The goal of this article is to propose an approach to apply process mining techniques for mining website logs, to discover user paths and patterns often seen in a website. Patterns retrieved from user's most frequent browsing behavior are then utilized to analyze usability issues. The paper presents a general model for improving an e-commerce website based on the application of process mining techniques. The findings of the article showcase that it is possible to analyze and improve a website based on results achieved by applying process mining techniques on the web logs the site produces. The usefulness of provided model is proven on logs from a Polish e-commerce portal.

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Data analysis and processing skills are currently required by a multitude of job offers and cover a wide variety of applications. Although mostly shaped by the development of new technologies, programming languages and libraries, they are a necessity in the world of digital economy and entrepreneurship. A multitude of reports by large consulting companies such as Deloitte predict a sharp increase in demand for data science and AI roles in the future of not only the IT sector, but also the entire economy. The following questions arise: “What skillset do these innovators that use artificial intelligence and advanced analytical skills have?” and “What skills and requirements truly make a data scientist and are they are any different to that of data analysts, data engineers or software developers and programmers?”, moreover, “What is the demand for these specialists and are the university programs educating future specialists in this field or are the skills too new and need to be taught solely by business practice?” . To answer these questions, this article applies Natural Language Processing (NLP) techniques of machine learning to characterize and extract from the offers key skills important for data centred roles. The research was carried out on a preprocessed sample of 72 thousand job offers from the IT sector posted in 2019. A SVM linear classifier was applied to extract the most distinguishing technical skills and characterize the possibility of the automated classification of job postings, which resulted in about 85% precision and recall values for classifying data analyst, data scientist and data engineer roles and about 90% for classifying python developer roles.