This week, we’re zooming in on one of the most underrated skills in data science: communicating your project effectively.

Week 4 Focus: Intro to MLOps & Deployment. Topics include packaging your model (move from experimentation to a reproducible, shareable format), setting up basic infrastructure with Docker, FastAPI, or simple cloud deployment, and thinking in pipelines (automating data inputs, retraining, and model monitoring).

Week 3 focus: Intro to Modeling. Now that your data is cleaned and explored, it’s time to build your first model. This session covers training a basic model (regression or classification), evaluating performance with simple metrics, and starting to think about iteration and improvement.

Kickoff and project setup for Week 2 of Build & Learn: Data Science Meetup. Overview of Full Project Track and Self-Learning Track, with guidance on how to start a data science project and prepare for a 7-week journey.

Kickoff for Build & Learn: Data Science Meetup — Week 1. Focus on setting up your tools, getting GitHub ready, choosing a project, and committing to building.

This week’s focus is storytelling and presentation in data science, with emphasis on communicating your project effectively.

move from experimentation to a reproducible, shareable format.

explore tools like Docker, FastAPI, or simple cloud deployment.

how do you automate data inputs, retraining, or model monitoring?

Week 3 Focus: Intro to Modeling. Train a basic model (regression or classification); Evaluate performance using simple metrics; Start thinking about iteration and improvement.

Kickoff session for Week 1: Setting up your coding environment and GitHub, choosing a project using updated guides, and committing to building for the 7-week Build & Learn program. Hosted by Lindsey, senior data scientist.

Week 6 Focus: Storytelling & Presenting Your Work. This session focuses on communicating your data science project effectively, within the looped 7-week program. Feel free to jump in wherever you are; your project, your pace.

move from experimentation to a reproducible, shareable format.

explore tools like Docker, FastAPI, or simple cloud deployment.

how do you automate data inputs, retraining, or model monitoring?

This session covers packaging your model for reproducibility, setting up basic infrastructure (Docker, FastAPI, or simple cloud deployment), and thinking in pipelines (how to automate data inputs, retraining, and monitoring).

Now that your data is cleaned and explored, it’s time to build your first model. This week is all about making something that works — it doesn’t have to be perfect. You’ll train a basic model (regression or classification) and evaluate performance using simple metrics, then start thinking about iteration and improvement.

Session focused on data, exploratory data analysis (EDA), and basic feature engineering as part of a 7-week data science project program. Includes guidance on finding/collecting datasets, quick EDA to understand the data, and starting feature engineering.

Kickoff and project setup for Week 1 of the Build & Learn: Data Science Meetup.

Live demos from participants as part of the final showcase day; presentations and feedback.

Overview of the community-driven program designed to take you from idea to working project to portfolio-ready presentation in 7 weeks, with a final showcase day. Date: 2025-04-05. Time: 11:00 AM–1:00 PM. Location: Octopus Bar, Pestalozzistraße 5-8, 13187 Berlin. Hosted by Lindsey, a Senior Data Scientist.