Last modified: 31 May 2022 13:05
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.
| Study Type | Postgraduate | Level | 5 |
|---|---|---|---|
| Session | First Sub Session | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Aberdeen | Sustained Study | No |
| Co-ordinators |
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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.
Information on contact teaching time is available from the course guide.
1st Attempt
2x Online Tests (50% each)
Alternative Resit Arrangements
Resubmission of failed elements (pass mark carried forward)
There are no assessments for this course.
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Procedural | Apply | Carry out basic statistical analyses using a modern statistical computing language. |
| Procedural | Evaluate | Understand basic concepts in statistics and times series analysis. |
| Reflection | Evaluate | Development of codes in a modern statistical computing language to solve problems using statistics and times series. |
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