2024-08-23 –, Breakout Room
Exploratory data analysis is an important early step in the data science process where our goal is to quickly learn as much as we can about a dataset. While performing this analysis, we aim to "let the data speak for itself" by employing a variety of descriptive statistics and visualisations to spot patterns, identify outliers, and summarise the data. By better understanding the data, we can gain useful insights about our application domain and lay a solid foundation for further application of statistical modelling, machine learning, and AI.
For details please see https://kiwipycon.nz/programme/friday-workshops
This workshop will introduce participants to modern tools available for loading, processing, and analysing data with Python, including Jupyter notebooks for interactive programming, Pandas for tabular data manipulation, and Plotly for creating data visualisations. The focus will be on practical techniques for efficiently exploring a dataset.
During the workshop, we will apply these techniques and tools to perform exploratory data analysis on a publicly available, real-world dataset. Participants will be encouraged to follow along on their own laptop, with hands-on exercises to get stuck in analysing the data.
Please note: The specific Python libraries used in this workshop are subject to change.
Beginner
Ben loves using Python every day in his work as a data scientist to help organisations get more from their data, and he has a passion for teaching others how to get stuff done with Python.
He recently co-delivered the first-ever PyNoon lunchtime Python training course, and has presented at KiwiPycon and other conferences on a range of technologies.
Ben has over 12 years experience in software development and he previously worked as the original software architect for NZ security software startup DataMasque.
For his recently completed PhD thesis, Ben developed machine learning algorithms that can be applied despite common data deficiencies in collaboration with Fisher & Paykel Appliances.