Undergraduate Course: Algorithmic Foundations of Data Science (UG) (INFR11279)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Year 4 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course follows the delivery and assessment of Algorithmic Foundations of Data Science (INFR11156) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11156 instead. |
| Course description |
This course follows the delivery and assessment of Algorithmic Foundations of Data Science (INFR11156) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11156 instead.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | Students MUST NOT also be taking
Algorithmic Foundations of Data Science (INFR11156)
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Other requirements | This course follows the delivery and assessment of Algorithmic Foundations of Data Science (INFR11156) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11156 instead. |
Information for Visiting Students
| Pre-requisites | This course has the following mathematics prerequisites:
1 Calculus: limits, sums, integration, differentiation, recurrence relations
2 Graph theory: graphs, digraphs, trees
3 Probability: random variables, expectation, variance, Markov's inequality, Chebychev's inequality
4 Linear algebra: vectors, matrices, eigenvectors and eigenvalues, rank
5 Students should be familiar with the definition and use of big-O notation, and must be comfortable both reading and constructing mathematical proofs using various methods such as proof by induction and proof by contradiction. |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- demonstrate familiarity with fundamentals for processing massive datasets.
- describe and compare the various algorithmic design techniques covered in the syllabus to process massive datasets
- apply the learned techniques to design efficient algorithms for massive datasets
- apply basic knowledge in linear algebra and probability theory to prove the efficiency of the designed algorithm
- use an appropriate software to solve certain algorithmic problems for a given dataset
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Reading List
The main textbook for the course is:
Avrim Blum, John Hopcroft, and Ravindran Kannan: Foundations of Data Science.
https://www.cs.cornell.edu/jeh/book.pdf |
Additional Information
| Course URL |
https://opencourse.inf.ed.ac.uk/afds |
| Graduate Attributes and Skills |
As the outcome of the course, a student should be able to apply the learned mathematical knowledge to analyse and process massive datasets, and use these tools to solve algorithmic problems occurring in practice. |
| Keywords | Not entered |
Contacts
| Course organiser | Dr He Sun
Tel: (0131 6)51 5622
Email: |
Course secretary | Miss Toni Noble
Tel: (0131 6)50 2692
Email: |
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