Computational data science 2026

MSc studies in Data Science, Faculty of Mathematics and Information Science, Warsaw University of Technology

This course covers methods for data analysis and mining, statistics, machine learning, and artificial intelligence from a computational perspective. We will introduce, analyse, and implement key algorithms and data structures used for finding similar groups of objects, learning parameters of supervised machine learning models, conducting simulation experiments, etc. We will focus not only on implementing re-usable algorithms from scratch (acquiring programming skills needed to implement any algorithm when a ready-to-use implementation of a method is unavailable, its high-level R/Python prototype is too slow to run, or a customised modification thereof is required), but also calling methods from existing C/Fortran libraries (standing on the shoulders of giants, appreciating the effort of the open-source community). As a by-product, we will better understand the implementation of statistical software packages (R, Python with NumPy and Pandas, etc.).

Course prerequisites: Structured data processing (R and Python), Data structures and algorithms (sorting and searching), Numerical methods, Introduction to machine learning.

Schedule – Summer semester 2026

• Lectures+Pracs (“Workshops”): Tuesdays, 16:15–20:00, 216 MiNI

  • classical, blackboard-based lectures; students are expected to participate actively in the classes, take notes, discuss/brainstorm all ideas, and overall happily relish all the food for thought mindfully seasoned and served by yours truly;

  • bring your own laptop for we will be implementing, testing, and applying the algorithms presented in the lectures too!

• Office hours: 550@MiNI; I’m there on most days

• Written assignments: Week 8 and 14

• Project delivery and presentation: Week 15

1. 2026-02-24

🤔 Topics

• Introduction to the course: why lower-level programming for data science?

• Introduction to the C programming language (a minimalist subset of C17 for C23 is not universally supported yet):

  • variable declarations, common scalar types

  • arithmetic, comparison, and logical operators

  • if, switch, goto

📚 References

• Recommended reading (choose either; the latter is an extended version of the former; however, sometimes it does not mark the GNU extensions to the C language clearly enough):

  • (!) T. Rothwell, J. Youngman, and others, The GNU C Reference Manual (skip the parts devoted to the GNU C extensions)

  • (!) The GNU C Language Manual

• Further reading:

  • The C Reference

  • comp.lang.c Frequently Asked Questions

  • J. Gustedt, Modern C, Manning, 2019

  • (*) Programming Languages – C. International Standard ISO/IEC 9899:2018

• Extras:

  • In praise of basic research and general curiosity: How did places like Bell Labs know how to ask the right questions?

  • I want you to be curious: Hacker News

🏠 Homework

• On your laptop, ensure you have access to a GNU/Linux environment with gcc, Python, and R installed; see the Software section below

• Recall the following terminal/Bash commands:

  • cd, pwd, ls, ls -l, mkdir, cp, cp -i, mv, rm, rm -rf, rm --help, man

  • echo, touch, cat, cat > file, less, head, tail, nano, how to quit vi

  • chmod, chown, whereis, ln, top/htop, df, du, tar, zip/gzip/bzip2/xz,

  • time, sleep, bg, fg, ps, kill, nice

  • diff, grep (also: rg–ripgrep), find, sed, rename, uniq, sort

2. 2026-03-03

🤔 Topics

• Introduction to the C programming language (cont’d):

  • while, do..while, and for loops

  • Cython and numba

  • defining own functions

  • ‘hello world’ as a terminal app

  • x86 assembly basics

  • static functions and variables

  • the C preprocessor basics

📚 References

see Week 1

• Extras:

  • FFmpeg Assembly Language Lessons

🏠 Homework

TBA

3. 2026-03-10

🤔 Topics

• Introduction to the C programming language (cont’d):

  • benchmarking Python, Cython vs C cont’d

  • the C preprocessor cont’d

  • one program, many source and header files; the linker

📚 References

see Week 1

• Extras:

  • PEP 659 – Specializing Adaptive Interpreter

  • PEP 744 – JIT Compilation

  • Optimising compiler

🏠 Homework

TBA

4. 2026-03-17

🤔 Topics

• Introduction to the C programming language (cont’d):

  • record types (structures), typedef, unions and enums

  • static arrays

  • pointers

  • dynamic memory allocation

  • …TODO…

📚 References

TBA

🏠 Homework

TBA

5. 2026-03-24

🤔 Topics

TBA

📚 References

TBA

🏠 Homework

TBA

6. 2026-03-31

🤔 Topics

TBA

📚 References

TBA

🏠 Homework

TBA

7. 2026-04-14

🤔 Topics

TBA

📚 References

TBA

🏠 Homework

TBA

8. 2026-04-21

🤯 Written Assignment 1

TBA

🤔 Topics

TBA

📚 References

TBA

🏠 Homework

TBA

9. 2026-04-28

🤔 Topics

TBA

📚 References

TBA

🏠 Homework

TBA

10. 2026-05-05

🤔 Topics

TBA

📚 References

TBA

🏠 Homework

TBA

11. 2026-05-19

🤔 Topics

TBA

📚 References

TBA

🏠 Homework

TBA

12. 2026-05-26

🤔 Topics

TBA

📚 References

TBA

🏠 Homework

TBA

13. 2026-06-02

🤔 Topics

TBA

📚 References

TBA

🏠 Homework

TBA

14. 2026-06-09

🤯 Written Assignment 2

TBA

🤔 Topics

TBA

📚 References

TBA

🏠 Homework

TBA

15. 2026-06-16

🤯 Project delivery

TBA

🤔 Topics

TBA

📚 References

TBA

🏠 Homework

TBA

Assessment methods and regulations

There are two written assignments (33+33 points) and an individual programming project+presentation (33 points).

During the written assignments, electronic devices must not be used. However, a single A4-sized sheet of hand-written notes can be brought along.

AI tools (ChatGPT, Copilot, Claude Code etc.) are not allowed in the project part. The use of non-permitted materials results in a failing grade as per the University’s Study Regulations.

Class attendance is compulsory. Five pts will be deducted from the final result for each unjustified absence.

The final grade will reflect the extent to which your written and programming assessment tasks met the prescribed quality criteria.

The total result of ≤50 pts translates to the 2.0 grade; (50, 60] – 3.0; (60, 70] – 3.5; (70, 80] – 4.0; (80, 90] – 4.5; and >90 – 5.0.

Software

We’ll need a GNU/Linux operating system with root access, e.g., Ubuntu, Kubuntu, Lubuntu, Linux Mint – pick the one you find more appealing, it’s a matter of taste (I’m on openSUSE Tumbleweed, but it’s less beginner-friendly).

If you use a different operating system family, you can run Linux on a virtual machine; see, e.g., how to run Ubuntu using VirtualBox.

Programming languages: C, C++ (gcc/clang), R, Python (CPython, Cython), Fortran; on Ubuntu, run in the terminal:

sudo apt-get update
sudo apt-get -y upgrade
sudo apt-get -y install r-base-dev python3-dev pandoc
sudo apt-get -y install cython
sudo apt-get -y install jupyter-notebook

References

The list is non-exhaustive. More references will be provided in the lectures (also: see above).

1. T.H. Cormen, C.E. Leiserson, R.L. Rivest, C. Stein, Introduction to Algorithms, MIT Press and McGraw-Hill, 2022

2. W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery, Numerical Recipes. The Art of Scientific Computing, 3rd ed., Cambridge University Press, 2007

3. S. Marsland, Machine Learning: An Algorithmic Perspective, Chapman&Hall/CRC, 2015

4. A. Blum, J. Hopcroft, R. Kannan, Foundations of Data Science, 2018

5. R.A. van de Geijn, E.S. Quintana-Orti, The Science of Programming Matrix Computations

6. V. Eijkhout, The Art of HPC, 2026

7. J.E. Gentle, Matrix Algebra: Theory, Computations and Applications in Statistics, Springer, 2024

8. M. Gagolewski, Deep R Programming, 2026

9. M. Gagolewski, Minimalist Data Wrangling with Python, 2026

10. D. Goldberg, What every computer scientist should know about floating-point arithmetic, ACM Computing Surveys 21(1), 1991, 5–48

11. N.J. Higham, Accuracy and Stability of Numerical Algorithms, SIAM, 2002

12. G.H. Golub, C.F. Van Loan, Matrix Computations, The Johns Hopkins University Press, 2013

13. NIST Digital Library of Mathematical Functions

14. (*) D.E. Knuth, The Art of Computer Programming, Vols. 1–4B, Addison-Wesley, 2023

15. (*) B.W. Kernighan, D.M. Ritchie, The C Programming Language, Prentice Hall, 1988

16. J. Gustedt, Modern C, Manning, 2019

17. G. Barlas, Multicore and GPU Programming, MK, 2022

18. N. Matloff, Parallel Computing for Data Science: With Examples in R, C++ and CUDA, CRC Press, 2016

19. T. Rothwell, J. Youngman, and others, The GNU C Reference Manual (skip the parts devoted to the “GNU extensions”)

20. (*) J. Arndt, Matters Computational: Ideas, Algorithms, Source Code, Springer, 2011

21. The GNU C Language Manual (skip the parts devoted to the “GNU extensions”)

22. The GNU C Library

23. (*) Programming Languages – C. International Standard ISO/IEC 9899:2018

24. R Core Team, Writing R Extensions, 2026

25. R Core Team, R Internals, 2026

Source code of:

1. R (mirror)

2. Python

3. NumPy

4. SciPy

5. Pandas

6. scikit-learn

7. data.table

8. dplyr

9. GNU GSL