Data Science Pipeline
- Analysing your research questions from data science perspective Helping to get data (APIs, web scraping, databases, files)
- Scrubbing and cleaning your data (Python, R)
- Exploratory data analysis (Python, R)
- Modelling your data (training neural networks) (Python, R)
- Data visualization and storytelling (Python, R)
- Consulting on publishing a data paper in a data journal FAIR Sharing and archiving your data, models and visualizations in data repositories
Codes and algorithms
- Writing a reproducible open-source code (Python, R)
- Code organization and documentation at GitHub
- Reviewing your software repo FAIR sharing and archiving your software in data repositories
- Consulting on publishing a software paper in a software journal
Infrastructure for data science
- Consulting on computational resources, high performance computing (HPC)
- Consulting on free cloud infrastructure and programs (Kaggle, Google Colab, Google Cloud for education, TPU Research Cloud)
- Consulting on deploying deep learning models in production
Low-code and no-code data science
- Consulting on low-code libraries (PyCaret, H20 AutoML, Auto-ViML, TPOT, AutoKeras)
- Consulting on no-code tools (Google Cloud Auto ML and ML KIT, Runway AI, Lobe, CreateML, RapidMiner, DataRobot)
- Consulting on researching with chat bots (ChatGPT and its alternatives)
Networking
- Helping to search cooperation partners
- Helping in organizing data science meetups
- the Mannheim Center for Data Science
