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PX5010: STATISTICS AND TIME SERIES ANALYSIS (2021-2022)

Last modified: 16 Aug 2021 13:54


Course Overview

This is an introductory course in statistics and statistical methods for data analysis.

We will introduce descriptive statistics, ANOVA, GLMs, correlations, spectra, wavelets, etc.

This will allow us to perform typical analysis that underlie most modern data science questions.

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 Ekkehard Ullner

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

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

What other courses must be taken with this course?

None.

Are there a limited number of places available?

No

Course Description

In this course we will introduce the bases of statistics and statistical modelling.

We will discuss the basics of descriptive statistics, means, variances, quantiles, distributions, etc, following standard textbooks in this area.

Furthermore, this course will introduce ANOVA and GLMs.

It will also introduce standard methods of time series analysis such as correlations, spectra wavelets etc. Some more advanced methods on time series modelling and forecasting will also be discussed.

We will also use (at a very ad hoc level) various approaches of machine learning such as clustering, distribution learning, outlier detection etc.


In light of Covid-19 this information is indicative and may be subject to change.

Contact Teaching Time

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

Teaching Breakdown

  • 4 Practicals during University weeks 18 - 20

More Information about Week Numbers


In light of Covid-19 and the move to blended learning delivery the assessment information advertised for second half-session courses may be subject to change. All updates for second-half session courses will be actioned in advance of the second half-session teaching starting. Please check back regularly for updates.

Summative Assessments

Exam

Assessment Type Summative Weighting 50
Assessment Weeks Feedback Weeks

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Learning Outcomes
Knowledge LevelThinking SkillOutcome
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Computer Programming Exercise

Assessment Type Summative Weighting 20
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Learning Outcomes
Knowledge LevelThinking SkillOutcome
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Tutorial/Seminar Participation

Assessment Type Summative Weighting 10
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 20
Assessment Weeks Feedback Weeks

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Learning Outcomes
Knowledge LevelThinking SkillOutcome
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Formative Assessment

There are no assessments for this course.

Course Learning Outcomes

Knowledge LevelThinking SkillOutcome
ProceduralApplyCarry out basic statistical analyses using a modern statistical computing language.
ProceduralEvaluateUnderstand basic concepts in statistics and times series analysis.
ReflectionEvaluateDevelopment of codes in a modern statistical computing language to solve problems using statistics and times series.

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