Software developers interested to get started with data science are often overwhelmed by the amount of choices: what language to use, which libraries, where to find suitable data?
Also, many tutorials focus on a single technology which can make it difficult to understand the whole scope of a data science project.
We have created an open-source example application that is optimized to serve as a playground for learning and experimentation. Nevertheless it works on a realistic dataset, addresses a typical machine learning task one may encounter on the job (demand forecasting), and applies an industry-standard toolset (Python 3, Pandas, Jupyter Notebook, AWS).
In this session we will run you through the entire workflow of a machine learning application and introduce you to the different phases of a data science project: data exploration, prototyping, validation, and productization. From there on we will guide you to work hands-on on improving prediction accuracy or other features of the application.