Jupyter Notebook: Interactive Visualization Approaches

Jupyter Notebooks are becoming the Integrated Development Environment (IDE) of choice for data scientists and software engineers. Notebooks provide an excellent way of sharing research, code and documentation, hence promoting reproducible research. With widget libraries like ipywidgets and bqplot, we can now create rich applications, dashboards and tools by just using python code. In this talk, we will see how we can build interactive visualizations in the Jupyter notebook. We’ll cover use-cases including time series analysis, visualizations of machine learning models, visual analytics on web server logs and examples in finance.

Who is this presentation for?
Data Scientists, Software Engineers, Researchers

Prerequisite knowledge:
Basic understanding on machine learning

What you'll learn?
Users will learn how we can perform visual analytics and look at advanced data visualizations

Chakri Cherukuri is a senior researcher in the Quantitative Financial Research group at Bloomberg LP. His research interests include quantitative portfolio management, algorithmic trading strategies and applied machine learning/deep learning. Previously, he built analytical tools for the trading desks at Goldman Sachs and Lehman Brothers. Before that he worked in the Silicon Valley for startups building enterprise software applications. He has extensive experience in scientific computing and software development. He is a core contributor to bqplot, a 2D plotting library for the Jupyter notebook. He holds an undergraduate degree in mechanical engineering from Indian Institute of Technology (IIT), Madras, and an MS in computational finance from Carnegie Mellon University.