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PX5016: INTRODUCTION TO DATA SCIENCE (2024-2025)

Last modified: 23 Aug 2024 15:46


Course Overview

The goal of this course is to introduce the student into the field of data science. You will improve your data literacy, understanding the different types of existing data and data structures, and the kind of problems that can be solved using it. You will learn the tools and techniques necessary to obtain the data, store it and manipulate it. You will learn tools and techniques to preprocess it and prepare it for analysis, statistical characterization and visualization. Then, you will be introduced to simple modelling techniques aimed at providing answers for the problems you want to solve. The final lectures are dedicated to introduce the MySQL and Mongo relational and non-relational databases, respectively.

Course Details

Study Type Postgraduate Level 5
Term First Term Credit Points 15 credits (7.5 ECTS credits)
Campus Aberdeen Sustained Study No
Co-ordinators
  • Dr Murilo da Silva Baptista

What courses & programmes must have been taken before this course?

  • Either Master Of Science In Health Data Science or Master Of Science In Data Science
  • Any Postgraduate Programme

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

Are there a limited number of places available?

No

Course Description

The goal of this course is to introduce the student into the field of data science. Firstly, you will improve your data literacy, understanding the different types of existing data and data structures, and the kind of problems that can be solved using data science. I will give emphasis to the structure of data and the concepts behind data pre-processing. Secondly, I will cover time series and temporal data analysis, modelling and prediction, including a short introduction to sound and image handling. Pre-processing of data (e.g., cleaning, querying, explorations, transformations, smoothing, discretizing) will follow. Finally, I will introduce the techniques for data science (data analysis, data analytics, descriptive analysis, diagnostic analysis, evaluating your model), the use of supervised and unsupervised approaches to model data based on machine learning, finishing the course with an introduction to the use of relational and non-relational databases.  


Details, including assessments, may be subject to change until 30 August 2024 for 1st term courses and 20 December 2024 for 2nd term courses.

Summative Assessments

Class Test - Multiple Choice Questions

Assessment Type Summative Weighting 20
Assessment Weeks Feedback Weeks

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Learning Outcomes
Knowledge LevelThinking SkillOutcome
Sorry, we don't have this information available just now. Please check the course guide on MyAberdeen or with the Course Coordinator

Class Test - Multiple Choice Questions

Assessment Type Summative Weighting 30
Assessment Weeks Feedback Weeks

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Learning Outcomes
Knowledge LevelThinking SkillOutcome
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Class Test - Multiple Choice Questions

Assessment Type Summative Weighting 50
Assessment Weeks Feedback Weeks

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Learning Outcomes
Knowledge LevelThinking SkillOutcome
Sorry, we don't have this information available just now. Please check the course guide on MyAberdeen or with the Course Coordinator

Formative Assessment

There are no assessments for this course.

Resit Assessments

Online Exam

Assessment Type Summative Weighting 100
Assessment Weeks Feedback Weeks

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Learning Outcomes
Knowledge LevelThinking SkillOutcome
Sorry, we don't have this information available just now. Please check the course guide on MyAberdeen or with the Course Coordinator

Course Learning Outcomes

Knowledge LevelThinking SkillOutcome
ConceptualAnalyseTo have a comprehensive overview of the whole data science cycle.
ProceduralApplyTo prepare and organize the data so that data format is appropriate for further analysis.
ProceduralUnderstandLearn introductory concepts to pre-access the data to learn about main features of the data.
FactualCreateLean to visualize and present the data together with its corresponding analysis.
ReflectionAnalyseTo learn the basic fundaments to make sense out of the data. To explore and analyse data from descriptive, inferential statistics, and statistical models, and also from machine learning methods.
FactualUnderstandExploring the available data, to understand how to obtain the data or to generate our own.
ConceptualUnderstandLearn how to build simple databases (mySQL and Mongo) and interact with them.

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