Last modified: 31 May 2022 13:05
In this course we study the typical workflow for a data analysis project. We will learn how to access and collect data, how then to clean the data, and organise it in databases to prepare it for later analysis.
We will then perform descriptive and exploratory data analysis and finally visualise the results and create a report.
| Study Type | Postgraduate | Level | 5 |
|---|---|---|---|
| Term | First Term | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Aberdeen | Sustained Study | No |
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A typical data analysis project consists of several steps that make up a workflow.
In this course we will first discuss how to obtain data. There are many different ways to obtain data, from online repositories, web scraping and API communication, to the interaction with data bases such as mySQL and Mongo. We will also describe how we can measure our own data and make them computational.
The next step is typically to clean the data and to get it into a format that is suitable for subsequent analysis. We will discuss how structured and unstructured data can be used and how we can move data up a hierarchy of data quality levels.
We will then learn how to build simple databases (mySQL and Mongo) and interact with them.
Information on contact teaching time is available from the course guide.
1st Attempt
One group project, with a report (50%)
Oral Examination (25%)
Coding Component (25%)
Under special circumstances, a student can do the project alone.
Alternative Resit Arrangements
Individual Report (100%)
There are no assessments for this course.
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| 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. |
| Conceptual | Understand | Learn how to build simple databases (mySQL and Mongo) and interact with them. |
| Procedural | Apply | To prepare and organize the data so that data format is appropriate for further 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 | Analyse | To have a comprehensive overview of the whole data science cycle. |
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