Many higher education institutions in the world have switched to online learning due to the ongoing COVID-19 pandemic, which also has greatly contributed towards an increase of MOOCs enrollments across various disciplines. There are many factors that can influence the learning trajectory in MOOCs settings, and in order to gain a deeper understanding of learners' experience, we employ a quantitative research method, in which sentiment analysis and topic modeling are applied. In this perspective, learners' reviews from the learning platform Coursera are examined to identify the main topics associated with the course and the learners' attitudes and opinions towards these topics. For this purpose, a total of 28,281 reviews scraped from five courses within the field of data science are analyzed, and consequently nine course topics for which learners have commented on are found. The identified topics include: content, delivery, assessment, learning experience, tools, video material, teaching style, instructor skills and course provider. Next, each topic is assigned a sentiment score using a lexicon-based approach, and the topics which mostly affect the learning experience are finally determined and discussed.