🛳️Community Workshop Series: Data Science Fundamentals – Predicting Titanic Survivors🛳️
This three-part interactive workshop series provides a structured introduction to Data Science and Machine Learning. Participants will progressively build their understanding by working with Google Colab and the Titanic passenger dataset to explore, visualize, clean and ultimately make predictions using Machine Learning. Each session builds on the previous one, with homework assignments between workshops reinforce learning through practical exercises and independent exploration.
When and where
📅Date: 11, 17 & 23 April
🕒Time: 16
CET
📍Location: Course Room Creative Intelligence
Workshop 1: Introduction to Data Science, ML & Google Colab
📅 Friday 11 April
📊Overview of Data Science & Machine Learning: Key concepts and real-world applications
📊Introduction to Google Colab: Navigating the interface, running Python code, and using notebooks
📊Homework: Start Google Colab and run your first Python code snippets
Workshop 2: Data Visualization & Cleaning in Google Colab
📅 Thursday 17 April
🧹Introduction to data visualization: Understanding patterns using graphs and plots
🧹Data cleaning techniques: Handling missing values, filtering, and transforming data
🧹Applying these concepts to the Titanic dataset: Exploring passenger information
🧹Homework: Perform data visualization and cleaning on the Titanic dataset in Google Colab
Workshop 3: Machine Learning & Predicting Titanic Survivors
📅 Wednesday 23 April
🤖 Introduction to Machine Learning models: How models make predictions
🤖Building a predictive model: Training an ML model to predict Titanic survival
🤖Evaluating model performance: Understanding accuracy and improvements
🤖Final discussion and takeaways
🤖Homework: Experiment with different features to improve your model’s accuracy
What you'll get
✅Step-by-step learning: Each session builds on the last, ensuring a structured learning experience.
✅Hands-on practice with Google Colab: Learn to work with Python for Data Science in a cloud-based environment.
✅ Homework assignments for reinforcement: Apply what you learn between sessions to deepen your skills.
✅ No prior experience needed: Designed for beginners, but those with some knowledge can also benefit from structured hands-on learning.
By the end of this progressive learning series, you will have built your own Machine Learning model in Google Colab and gained practical experience in Data Science and AI!
Who we are
💡Lukas is a Senior Project Manager with extensive experience in Machine Learning and IT project management. He holds a Machine Learning Specialist certification from RWTH Aachen and is currently pursuing an MSc in Sustainability, Entrepreneurship & Technology. Throughout his career, Lukas has led complex projects across various industries, working with companies such as Stolz & Laufenberg Projectmanagement and DB Fernverkehr AG. At HGK Logistics & Intermodal, he was responsible for IT project portfolio management, leading a team of project managers.
💡Fabian is the team lead of the growth division at awork, a B2B SaaS project management software. On a daily base he is running experiments in acquisition, activation, retention and engagement. Within his scope of ownership is qualitative and quantitative user research, analyzing large sets of user data for nuggets of information and mapping this with data from user interviews.