Students typically create their portfolios out of simple, clean datasets. These projects are great to learn the basics, but they allow you to circumvent the most essential data science skills. Data on the job requires extensive cleaning, exploration, and feature engineering to become usable for your models. Projects in the professional space will also require steps beyond the initial creation and evaluation of a model.
This hands-on skill-building webinar is for starting and intermediate data science students with a basic knowledge of Python. By the end of this session, you will know:
- Where and how to look for your data
- Some basic data engineering techniques to keep in mind
- How to use pandas to load, clean, and engineer your datasets
- How to use Matplotlib to plot and explore your data
- How to use Scikitlearn, and Tensorflow to model and evaluate your data
- How to use Pickle, Streamlit, and Matplotlib to turn your data into an interactive dashboard
