DATA9001 Fundamentals of Data Science - 2025
General Course Information
Course Code : DATA9001
Year : 2025
Term : Term 2
Teaching Period : T2
Course Details & Outcomes
Course Description
This course provides a broad overview of Data Science as a platform. for further studies in Data Science and an understanding and appreciation of Data Science in the modern world.
Students will study the fundamentals of Data Science as it is applied in Computer Science, Economics, and Mathematics and Statistics. They will be introduced to topics such as databases, data analytics, data mining, Bayesian statistics, statistical software, econometrics, machine learning and business forecasting.
The content of this course will be delivered via weekly live lectures with academics from three different Schools: The School of Mathematics and Statistics, the School of Economics and the School of Computer Science and Engineering. These concepts will be further explored through a series of tutorials/workshops.
Course Aims
The aim of the course is to provide a broad overview of probability theory, different statistical methods, regression analysis, and modern data science techniques. This course will provide a platform. for further studies in Data Science and Machine Learning.
Course Learning Outcomes
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Course Learning Outcomes
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CLO1 : Apply probability rules in a given setting to calculate key quantities.
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CLO2 : Use key theoretical tools to explore the properties of random variables.
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CLO3 : Apply key methods of statistical inference in applied settings.
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CLO4 : Use R/RStudio to perform statistical computations and simulations.
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CLO5 : Apply various data visualisation tools, perform. regression analysis and draw causal inference from data.
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CLO6 : Apply fundamental data science techniques and tools, including machine learning,
Naïve Bayes classification, Decision trees, K-NN, unsupervised learning and neural networks.
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Course Learning Outcomes
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Assessment Item
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CLO1 : Apply probability rules in a given setting to calculate key quantities.
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• Statistics Assignment
• Final Exam
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CLO2 : Use key theoretical tools to explore the properties of random variables.
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• Statistics Assignment
• Final Exam
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CLO3 : Apply key methods of statistical inference in applied settings.
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• Statistics Assignment
• Final Exam
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CLO4 : Use R/RStudio to perform. statistical computations and simulations.
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• Statistics Assignment
• Final Exam
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CLO5 : Apply various data visualisation tools, perform
regression analysis and draw causal inference from data.
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• Economics Assignment
• Final Exam
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CLO6 : Apply fundamental data science techniques and tools, including machine learning, Naïve Bayes
classification, Decision trees, K-NN, unsupervised learning and neural networks.
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• Computer Science Assignment
• Final Exam
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