There has been unprecedented growth in the field of artificial intelligence (AI) and data science. This has revolutionised so many industries and how we solve problems and make decisions. There is a higher demand for AI and data-driven insights in so many sectors which has given rise to specialised degree programmes that can prepare students for careers in these evolving fields.
If you are considering looking for degrees for data science and artificial intelligence, it is very important to consider the curriculum offered by the course provider. A critical part of this education is a strong foundation in mathematics and statistics. And these degree programmes will include linear algebra, calculus, statistical methods and probability theory in the coursework equipping students with the analytical frameworks and mathematical tools to understand and interpret data. They can build productive models and design algorithms with this foundation. It is also important to have proficiency in programming languages if you are planning to become a professional in AI and data science field. The curriculum will generally have hands-on experience with languages such as SQL, Python etc. so that the students are able to write code in order to implement machine learning algorithms, analyse data and manipulate it. This knowledge is essential when developing AI applications.
Coursework can also cover principles of software engineering
And best practices when it comes to writing scalable and efficient code. Raw data is most often unstructured and you need to pre-process it and clean it before you can use it for modelling and analysis. And with these degree programmes, students are taught wrangling and pre-processing of data which include cleaning data, data normalisation, transformation etc. The students in these courses will work with different data sources and formats so that they are able to prepare it for analysis and modelling. The degree programmes also cover a range of techniques when it comes to machine learning which can include reinforcement learning, deep learning, supervised and unsupervised learning.
These machine learning algorithms can then be applied by the students to solve real world problems. Data visualisation is a way to explore, interpret and communicate insights from data. And in AI and data science programmes, students learn how to create effective data visualisations by using tools and libraries. This allows them to design informative charts, dashboards and graphs that are visually appealing. And these formats can communicate complex insights and data driven narratives.
The volume of data generated is vast
And it is important for organisations to process and analyse insights from large scale data sets. Students are therefore introduced to big data technologies and platforms so that they can efficiently extract insights from large scale data. The ethical and legal considerations in AI and data science technologies will be included in the curriculum as well. Some of the ethical issues discussed are data privacy, fairness, bias, accountability and transparency in AI and data driven decision making. Students will also learn about industry standards and regulatory frameworks when it comes to the responsible use of AI and data technologies.
