Data Science and Machine Learning Basics Lab

Lecturer (assistant)
TypePractical course
Duration6 SWS
TermSommersemester 2021
Language of instructionGerman
Position within curriculaSee TUMonline
DatesSee TUMonline


Admission information


After successful completion of the module, the students: - understand and can explain the basic concepts of data science and applied machine learning; - can solve simple problems regarding the evaluation of scientific work by using and programming with the software tools that were explained in the lecture; - can understand and judge the evaluation of scientific work; - can apply the learned methods on practical tasks.


- Short repetition of the basics of statistics (mean, variance, standard deviation, median, percentiles, distributions, confidence intervals, discrete and continuous data, correlation, ...); - Basics of data visualization (line plots, bar plots, box plots, violin plots, bean plots, heat maps, scatter plots, ...); - Basics of data acquisition (measurements, simulations, public repositories); - Basics of data management and handling (data bases, MySQL, pandas, numpy, ...); - Basics of applied methods of machine learning (linear regression, random forrest, TSNE, ...).


- Basics of Stochastic i.e. statistics as taught in Stochastic Signals (EI0205) - A previous course on programming or algorithms is recommended (and included in the core EE-curriculum with module IN8009) - Being able to read and understand english literature

Teaching and learning methods

The students learn the programming language Python and important data science and machine learning frameworks for Python in order to conduct the programming tasks. In the first phase, students learn and exercise the taught concepts of the lecture by solving programming tasks. In the second phase, students are asked to work on a larger data science project. The students will have to evaluate data sets provided during the lecture course. In addition to the individual methods of the students, consolidated knowledge is aspired by repeated lessons in exercises, practical programming assignments and tutorials. Teaching method: During the lectures, students are instructed in teacher-centered style. The exercises and programming assignments are held in a student-centered way.


The final grade is calculated as follows: - 30% graded programming assignments; - 30% midterm exercise (written or oral); - 40% graded project (report and presentation).

Recommended literature

Selected chapters from the following literature are recommended: - Introduction to Computation and Programming Using Python, John V. Guttag, second edition - The Art of Statistics - Learning from Data, David Spiegelhalter - Statistics Done Wrogn - The Woefully Complete Guide, Alex Reinhart