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Unit information: Introduction to Data Science in 2020/21

Please note: you are viewing unit and programme information for a past academic year. Please see the current academic year for up to date information.

Unit name Introduction to Data Science
Unit code EMAT20011
Credit points 10
Level of study I/5
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Professor. Cristianini
Open unit status Not open
Pre-requisites

EMAT10100, EMAT10704, EMAT10007, EMAT10006, EMAT20920

Co-requisites

None

School/department Department of Engineering Mathematics
Faculty Faculty of Engineering

Description

This unit will introduce core data analysis skills and concepts. Students will acquire fundamental data science skills including importing and exporting data, data visualisation, detecting statistical patterns, and testing their statistical significance. They will be introduced to key concepts from statistics and machine learning such as classification and regression, clustering and linear manifolds, time series analysis, multi-dimensional probability distributions, feature selection and generation, and network analysis. They will be exposed to different types of data and patterns and gain experience in data analysis using suitable software tools and packages.

Intended learning outcomes

Upon successful completion of the course, students will be able to

  1. Explain and use key concepts in machine learning and statistics
  2. When presented with a data set, identify and apply suitable data analysis methods.
  3. Use data visualisation methods to gain insight into data, and to explain their findings.
  4. Test the statistical significance of any patterns the identify.
  5. Use software packages/toolboxes for data analysis.

Teaching details

Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, practical activities supported by drop-in sessions or online computer laboratories and problem sheets.

Assessment Details

1 Summative Assessment, 100% - Coursework. This will assess all ILOs.

Reading and References

Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman, Mining of Massive Datasets, Cambridge University Press

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