The academic content and curriculum of the Data Science and Analytics undergraduate programme aims to provide students with a comprehensive data science education. The programme is usually four years long and consists of eight semesters. Each semester is enriched with different courses, starting with basic courses and progressing towards advanced courses.
In the first years, mathematics, statistics and programming courses are offered to provide students with a solid foundation. Mathematics courses include calculus, linear algebra and differential equations. Statistics courses cover topics such as basic statistics, probability theory and statistical methods. Programming courses teach programming languages such as Python and R, which are widely used in the world of data science. These courses enable students to develop skills in data processing, analysis and visualisation.
In the following semesters, students focus on more specific data science and analytics courses. These courses include topics such as data mining, machine learning, big data analytics, database management and data visualisation. Data mining courses teach pattern recognition techniques and the extraction of valuable information from large data sets. Machine learning courses cover basic concepts such as supervised and unsupervised learning algorithms, regression, classification and clustering. Big data analytics courses introduce big data technologies and platforms such as Hadoop and Spark. Database management courses focus on SQL and NoSQL databases, while data visualisation courses teach data visualisation techniques with tools such as Tableau and matplotlib.
Throughout the programme, students are offered a variety of project-based courses and hands-on laboratory work. In this way, students have the opportunity to apply the theoretical knowledge they have learnt in practice. Project-based courses require students to develop and present solutions to real-world problems. Applied laboratory work gives students the experience of working directly with and analysing data sets.
The programme also includes ethical and social responsibility issues. Topics such as the ethical dimensions of data science applications, data privacy, security and social impacts of data analysis are covered as part of the courses. These courses enable students to understand the ethical and social dimensions of data science applications and make informed decisions on these issues.
Elective courses are also an important part of the curriculum. Students can gain in-depth knowledge on the subjects they want to specialise in by taking various elective courses according to their interests. Elective courses may include topics such as natural language processing, deep learning, business intelligence, marketing analytics and financial analytics.
Finally, many programmes include a capstone project or thesis. These projects require students to conduct extensive research or practice using the knowledge they have acquired throughout their studies. Capstone projects are often carried out in the form of industry collaborations or academic research and allow students to gain professional-level project experience prior to graduation.
The academic content and curriculum of the Data Science and Analytics degree programme provides students with a broad range of knowledge and skills. This programme trains students to become professionals specialised in the field of data science, able to think analytically and make data-driven decisions.