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Learning Outcomes: Upon completion, participants will have a solid understanding of basic Python programming, data gathering and cleaning, visualization, regression analysis, and the ability to create dynamic web applications and dashboards. This course aims to equip engineers with the tools needed for proficient data analysis and the development of machine learning models, preparing them for advanced studies.

Who Should Enroll: This course is ideal for engineers and aspiring data analysts looking to enhance their data analysis capabilities and delve into machine learning, offering a comprehensive toolkit to tackle real-world data challenges.

Course Overview:

• Introduction to Python: Start your journey by familiarizing yourself with Python's basic syntax, concepts, and data types on datacamp. Perfect for beginners or as a quick refresher for familiars.
• Data Manipulation with Pandas: Dive into Pandas for data cleaning, manipulation, and analysis.
• Visualization Techniques: Learn to create compelling data visualizations using libraries such as Matplotlib and Seaborn, essential for data exploration and presentation.
• Statistical Analysis and Modelling: Get hands-on experience with statistical models and data analysis techniques using libraries like Scikit-learn and Statsmodels, laying the foundation for machine learning applications.
• Introduction to Machine Learning: Explore basic machine learning models, understanding their application and significance in data analysis.
• Databases and APIs: Learn querying databases with SQL and accessing data through APIs, broadening your data sourcing skills.
• Web Frameworks for Data Applications: Discover how to use web frameworks like Flask and Streamlit to build interactive data applications, enabling the practical application of your analysis in real-world scenarios.
• Final Project: Culminate your learning experience by creating a data analysis model and developing a web application for it, showcasing your ability to apply data analysis and machine learning concepts in a cohesive project.

Communication with module coordinators and responding to questions.

• Communicating with module coordinators and responding to questions.
• Check the FAQ section for answers to organizational questions.
• Content-related questions should be asked in the discussion forum so that fellow students have the opportunity to help. If a forum question is not answered by fellow students after one week, the module supervisors will answer the question.
• If your questions still could not be answered, you can write an ISIS message or an email to the module contact person. Make sure to use the mail address issued by the TU Berlin or your university. Otherwise the e-mail will not be considered.
• For complex content-related questions, you will need to visit the consultation hours. Here you can make an appointment.
  
  

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  FAQ

  For organisational questions about our courses (e.g. exam, group work etc.) please have a look at our FAQ.
  
  
  Modul Information
  Dates

• • Lectures: coming soon!
  • consultation hour: on request 
  • Please click on the link to access the webinar: coming soon!

  Modul exam

  • Exam part 1: online course - performed throughout the semester - individual grading, max. 40 out of 100 
  • Exam part 2: case study - publishing date of case study tbd, deadline for submission tbd, group project, max. 60 out of 100

  Grading:
  

  • 1,0:   95 - 100 points 
  • 1,3:   90 - 94,5 points 
  • 1,7:   85 - 89,5 points 
  • 2,0:   80 - 84,5 points 
  • 2,3:   75 - 79,5 points 
  • 2,7:   70 - 74,5 points  
  • 3,0:   65 - 69,5 points 
  • 3,3:   60 - 64,5 points  
  • 3,7:   55 - 59,5 points  
  • 4,0:   50 - 54,5 points 
    
    

  Exam registration

  • Registration deadline: until 30.05.2026 (If you have not registered by the deadline, you will not be able to take the portfolio examination).
  • Registration: via Moses
  • • Exceptions for exam registration:
    • If you are unable to register via Moses, please upload the completed registration form via ISIS (upload function see below).
    • Registration of Nebenhörer and Erasmus students: You can register for the exam by sending an e-mail to Lennart Müller (l.mueller-stein@tu-berlin.de). A short text is sufficient, e.g. "I, [your name] + [university] + [matriculation number] + [study program, if applicable], hereby register for the module examination in module XXX in semester XXX."

  Case Study

  • All participants* are required to participate in the case study, which is a group assignment (see below for link to survey/group assignment).
  • For working on the Case Study the students need to join a group.
  • Group selection: You can assign yourself to a group or be randomly assigned to a group
  • inactive group members have to be reported to the module contact person within the first two weeks after the case study is published