DD1328 Fundamentals of Computer Science for Scientific Computing 9.0 credits

In this course, the students will build on their knowledge of algorithms, data structures and program design, learn basics of parallel and distributed programming and prepare for courses in computational mathematics, machine learning and theoretical computer science. Programming languages in the course are Python and Go.

Algorithms and data structures: A systematic overview of the concepts abstract data types, stacks, queues, lists, trees, searching, sorting and recursion based on the knowledge the students acquired in the course Fundamentals of programming. Hashing. Priority queues. Search trees. Simple syntactical analysis. Algorithm analysis.

Program Design: Program quality. Abstraction. Modularisation. Testing. System calls. Standard modules.

Theory and practical exercises concerning fundamental parallel programming and software development, including HTTP, client-server architecture, and accessibility.

After passing the course, the student should be able to

• systematically test programs to discover errors
• use abstraction as a tool to simplify the programming
• analyze and compare algorithms with regard to time and memory usage
• determine the correctness of iterative and recursive algorithms
• describe and implement fundamental algorithms and data structures
• ​develop, ie., depending on the grade level, choose, combine, modify, or construct a suitable algorithm and data structure to solve a given problem
• write and use simple BNF syntax
• design and implement simple parallel programs
• explain the fundamental principles of HTTP and client-server architecture, and implement simple client-server programs with regard to accessibility

in order to

• become good at solving problems using programming
• be able to use computational methods in application projects
• be prepared for advanced courses in courses in computational mathematics, machine learning, and theoretical computer science.

• become a good problem solver using programming
• be able to use computational methods in application projects
• take advanced courses in computational mathematics, machine learning and theoretical computer science.

Knowledge and skills in programming corresponding to completed course DD1337/DD1310-DD1319/DD1321/DD1331/DD1333/DD100N/DD100V/ID1018/ID1022.

• LABB - Programming Assignment, 2.0 credits, grading scale: A, B, C, D, E, FX, F
• LABC - Programming Assignment, 2.0 credits, grading scale: A, B, C, D, E, FX, F
• HEM3 - Take-home Assignment, 3.0 credits, grading scale: P, F
• LABA - Programming Assignment, 2.0 credits, grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

If the course is discontinued, students may request to be examined during the following two academic years.

The examination components LABA, LABB, and LABC comprise both laboratory assignments that must be presented in written and oral form, as well as workshop sessions with associated preparatory tasks. The examination component HEM3 assesses the parallel programming part of the course.

Christian Lindeborg

• All members of a group are responsible for the group's work.
• In any assessment, every student shall honestly disclose any help received and sources used.
• In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

In this course, the EECS code of honor applies, see:

http://www.kth.se/en/eecs/utbildning/hederskodex.