The Master's in Project Management and Data Science (MPMD)
The English-language postgraduate Master's programme Project Management and Data Science (MPMD) invites applications for a Lecturer position in "Statistical Programming with Python" from winter semester 2026. The teaching assignment comprises 54 teaching units à 45 minutes à 80, ,-) Euro per semester.
The MPMD provides international students with the knowledge to work as data analysts and project managers and to take on complex leadership roles in nationally and internationally active companies and institutions through application-oriented teaching. Graduates of the programme are able to successfully lead data-driven projects in international contexts.
The programme has been offered since 2016 and is in high demand internationally. It is open to both students with a data science background and career changers from other fields who can be expected to be highly motivated. Every semester 35 Master's students from more than 15 countries start their studies in this 4-semester programme at HTW Berlin. Graduates of the MPMD are sought after by global players in the industry as well as by start-ups.
Module Description
"Statistical Programming" is an elective module available to students in their second or third semester. Building on their foundational knowledge acquired in the first semester, this course enables students to deepen their understanding and skills in statistical programming.
By the module's end, students should possess a robust ability to implement statistical procedures in Python within a professional coding environment, and they will be equipped to contribute effectively in programming teams.
Module topics
The Course Covers a Range Of More Advanced Topics
Error-Free, Well-Structured and Transparent Programming in Python
Data access and manipulation using Databases,
Advanced Data visualization techniques
Collaborative Programming
Teamwork using version control systems like Git and platforms such as GitHub
Statistical Techniques
Recap of basic statistics, including descriptive statistics, statistical tests, contingency tables, and correlation
Advanced methods like robust() regression models, outlier detection, variable transformation, missing value imputation, and dimensionality reduction
Techniques including cross-validation, k-fold and bootstrap
Intensive discussion of specific Applications and case-study approach:
Natural Language Processing (NLP)
Time Series Forecasting
Teaching Approach
The course should employ a practice-oriented approach, emphasizing hands-on experience, practical tasks, and collaborative group work. The aim is to equip students with the skills necessary to tackle real-world data science challenges.
Teaching format and environment
Classes will take place in weekly sessions from October to mid- February
The lessons will take place on the Treskowallee campus of HTW Berlin in a hybrid form (on campus plus live streaming)
The language of instruction is English
Your profile
Proven track record in statistical programming with Python
Very good command of written and spoken English
(Online) teaching experience
University degree at Master's level
Ability to consider and integrate gender and diversity aspects
The HTW Berlin aims at equality and a working environment free of discrimination. The HTW Berlin is seeking to increase the proportion of women in research and teaching, and specifically encourages qualified female scholars to apply. Severely disabled applicants with equivalent qualifications will be given preferential consideration.
We look forward to receiving your application
Please send your application or any questions about the position by e-mail to: Sylke Sedelies, service--
Application deadline is 12/03/2026