Learning about data science
What do our data experts of the future think about the opportunities for business and communities. Meet some of today's students.
1. Name, where are you from, what are you studying?
My name is Miruna Clinciu. I am from Romania and I am currently studying the new Masters Degree in Artificial Intelligence at the University of Aberdeen. It is a very challenging programme covering Big Data, Data Science and Machine Learning.
2. Why did you decide to get involved in data science?
I am a Mechatronics Engineering graduate with no prior knowledge of data science. In my undergraduate degree, I studied the body and nervous system of a robot. I became interested in understanding how the brain of a robot might work. I started to read articles about Machine Learning, Reinforcement Learning and predictive models derived from big data and this led me to apply for the MSc Degree in AI at Aberdeen University.
3. What kinds of topics did you cover as part of your course?
In my first semester I studied:
Foundations of AI – Presents the fundamental techniques of Artificial Intelligence, used in system such as Google Maps, Siri, IBM Watson, as well as industrial automation systems.
Machine Learning – Machine Learning theories and algorithms, used in a wide range of applications such as face detection, anomaly detection
Evaluation of AI Systems – Knowledge of evaluation concepts, tools, techniques and technologies used to determine the effectiveness of AI systems across multidisciplinary applications developed for both controlled and real world environments.
Engineering of AI Systems – Knowledge and practical skills for AI system building. Presents the fundamental tools and techniques to equip software developers with solid programming expertise.
In the second semester I will study: Data Mining and Visualisation, Natural Language Generation, Software Agents and Multi-Agent Systems, Knowledge Representation and Reasoning.
4. What work opportunities do you see with your training – what would you like to/what are you doing now?
I’m looking for a new challenge that can help me broaden my knowledge and gain valuable work experience.
5. How do you think data science is relevant to small to medium sized businesses and communities?
I believe that data science is helping to solve every-day problems. Machine learning is very good at problem solving in environmental sciences, where huge amounts of information from monitoring the Earth’s various systems is being generated and used to, for example, identify how the effects of the climate change impact animal migration patterns.
6. What real life examples of innovative data usage have you seen/studied that you think demonstrates its benefits to society and/or business?
During my Machine Learning course, I completed a project about sentiment analysis and text classification, to create a model that can learn from an available data set and automatically categorise information making decisions and predictions on what is most likely to appear next. Used in social media, sentiment analysis and text classification are useful tools for tagging content or products using categories as a way to personalise browsing or to identify related content on a website.