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PX5008: INTRODUCTION TO DATA SCIENCE (2019-2020)

Last modified: 25 Sep 2019 09:58


Course Overview

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.

Course Details

Study Type Postgraduate Level 5
Session First Sub Session 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?

  • Any Postgraduate Programme (Studied)
  • Master Of Science In Data Science

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

None.

Are there a limited number of places available?

No

Course Description

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.


Contact Teaching Time

Information on contact teaching time is available from the course guide.

Teaching Breakdown

  • 4 Lectures during University weeks 10 - 11
  • 3 Lectures during University week12
  • 2 Practicals during University weeks 10 - 12

More Information about Week Numbers


In light of Covid-19 and the move to blended learning delivery the assessment information advertised for courses may be subject to change. All updates for first-half session courses will be actioned no later than 1700 (GMT) on 18 September 2020. All updates for second half-session courses will be actioned in advance of second half-session teaching starting. Please check back regularly for updates.

Summative Assessments

Report: Individual

Assessment Type Summative Weighting 80
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

Oral Presentation: Group

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

Formative Assessment

There are no assessments for this course.

Resit Assessments

Report: Individual

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.
FactualUnderstandExploring the available data, to understand how to obtain the data or to generate our own.
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.
ConceptualUnderstandLearn how to build simple databases (mySQL and Mongo) and interact with them.
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.
FactualCreateLean to visualize and present the data together with its corresponding analysis.

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