Last modified: 23 Aug 2024 15:46
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.
| Study Type | Postgraduate | Level | 5 |
|---|---|---|---|
| Term | Second Term | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Aberdeen | Sustained Study | No |
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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.
| Assessment Type | Summative | Weighting | 20 | |
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| Assessment Weeks | Feedback Weeks | |||
| Feedback | ||||
| Knowledge Level | Thinking Skill | Outcome |
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| Assessment Type | Summative | Weighting | 30 | |
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| Assessment Weeks | Feedback Weeks | |||
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| Knowledge Level | Thinking Skill | Outcome |
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| Assessment Type | Summative | Weighting | 50 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback | ||||
| Knowledge Level | Thinking Skill | Outcome |
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There are no assessments for this course.
| Assessment Type | Summative | Weighting | 100 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback | ||||
| Knowledge Level | Thinking Skill | Outcome |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Analyse | To have a comprehensive overview of the whole data science cycle. |
| Procedural | Apply | To prepare and organize the data so that data format is appropriate for further analysis. |
| Procedural | Understand | Learn introductory concepts to pre-access the data to learn about main features of the data. |
| Factual | Create | Lean to visualize and present the data together with its corresponding analysis. |
| Reflection | Analyse | To 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. |
| Factual | Understand | Exploring the available data, to understand how to obtain the data or to generate our own. |
| Conceptual | Understand | Learn how to build simple databases (mySQL and Mongo) and interact with them. |
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