Module Details |
| The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module. |
| Title | Introduction to Data Science | ||
| Code | COMP229 | ||
| Coordinator |
Dr V Kurlin Computer Science Vitaliy.Kurlin@liverpool.ac.uk |
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| Year | CATS Level | Semester | CATS Value |
| Session 2021-22 | Level 5 FHEQ | First Semester | 15 |
Aims |
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1. To provide a foundation and overview of modern problems in Data Science. |
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Learning Outcomes |
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(LO1) describe modern problems and tools in data clustering and dimensionality reduction, |
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(LO2) formulate a real data problem in a rigorous form and suggest potential solutions, |
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(LO3) choose the most suitable approach or algorithmic method for given real-life data, |
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(LO4) visualise high-dimensional data and extract hidden non-linear patterns from the data. |
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(S1) Critical thinking and problem solving - Critical analysis |
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Syllabus |
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1. Descriptive Statistics (3 lectures): average, range, median, mode, quartiles, sample deviation and variance, box plot. 2. Introduction to probability (3 lectures): probability axioms, combinatorial probabilities, probability paradoxes. 3. Probability distributions (3 lectures): uniform distribution, beta distribution, normal distribution. 4. Hypothesis testing (3 lectures): confidence intervals, P-value, statistical significance. 5. Bayesian statistics (3 lectures): conditional probabilities, Bayes formula, Bayesian vs frequentist approaches. 6. Linear regression (3 lectures): scatterplots and correlation, linear approximation to data, regression formulae. 7. Clustering (3 lectures): types of clustering algorithms, optimisation for k-means clustering, Lloyd’s algorithm. 8. Linear maps (3 lectures): matrices of linear maps, scaling, reflections, rotations, compositions. 9. Invariants of linear maps (3 lectures): determinant a nd eigenvalues of a matrix, a change of a linear basis. 10. Dimensionality reduction (3 lectures): principal component analysis and singular value decomposition. |
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Teaching and Learning Strategies |
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Teaching Method 1 - Lecture Teaching Method 2 - Tutorial Due to Covid-19, in 2021/22, one or more of the following delivery methods will be implemented based on the current local conditions. (a) Hybrid delivery (b) Fully online delivery and assessment (c) Standard on-campus delivery |
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Teaching Schedule |
| Lectures | Seminars | Tutorials | Lab Practicals | Fieldwork Placement | Other | TOTAL | |
| Study Hours |
30 |
10 |
40 | ||||
| Timetable (if known) | |||||||
| Private Study | 110 | ||||||
| TOTAL HOURS | 150 | ||||||
Assessment |
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| EXAM | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
| (229) Written examination | 70 | |||||
| CONTINUOUS | Duration | Timing (Semester) |
% of final mark |
Resit/resubmission opportunity |
Penalty for late submission |
Notes |
| 4-5 formative assessments (marked by demonstrators) - using problems similar to exam questions, without a contribution to the final mark. | 0 | |||||
| (229.1) Class test | 30 | |||||
Recommended Texts |
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| Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module. | |