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.

Entry Requirements (not applicable to Visiting Students)
Prerequisites 

Corequisites  
Prohibited Combinations  
Other requirements  None 
Information for Visiting Students
Prerequisites  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 bigO 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

Academic year 2024/25, Available to all students (SV1)

Quota: None 
Course Start 
Semester 2 
Timetable 
Timetable 
Learning and Teaching activities (Further Info) 
Please contact the School directly for a breakdown of Learning and Teaching Activities 
Assessment (Further Info) 
Written Exam
100 %,
Coursework
0 %,
Practical Exam
0 %

Additional Information (Assessment) 
The course assessment consists of a written exam.
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.
Written Exam = 100% 
Feedback 
Not entered 
Exam Information 
Exam Diet 
Paper Name 
Hours & Minutes 

Main Exam Diet S2 (April/May)  Algorithmic Foundations of Data Science (UG) (INFR11279)  2:120  
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

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  Miss Yesica Marco Azorin
Tel: (0131 6)50 5194
Email: ymarcoa@ed.ac.uk 

