Undergraduate Course: Algorithmic Foundations of Data Scence (UG) (INFR11277)
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 |
It is RECOMMENDED that students have passed
Algorithms and Data Structures (INFR10052)
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | 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. |
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. |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2024/25, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 20,
Seminar/Tutorial Hours 9,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
69 )
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| Assessment (Further Info) |
Written Exam
75 %,
Coursework
25 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
The course assessment consists of a written exam, and a course work.
The written exam is to test a students understanding about the algorithms design and analysis techniques discussed in class, as well as a students ability to apply the learned techniques to design and analyse new algorithms. This corresponds to the Intended Learning Outcomes 1-4.
The coursework is to test a students ability to solve more complicated algorithmic problems occurring in practice, and use an appropriate software to analyse massive datasets. This corresponds to the Intended Learning Outcomes 3-5.
Written Exam = 75%
Practical Exam = 0%
Coursework = 25% |
| Feedback |
A sample solution of the coursework will be released one week after the coursework's deadline. In addition to the feedback of the coursework, we will provide students with solutions of the exercise questions proposed in class or listed in the main reference book. We will also provide students with 1-hour drop-in session every week to answer students questions related the content of every weeks lectures. |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
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| Main Exam Diet S2 (April/May) | Algorithmic Foundations of Data Scence (UG) (INFR11277) | 2:00 | |
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
| 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: H.Sun@ed.ac.uk |
Course secretary | |
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