Advanced Data Visualizations In Jupyter Notebooks – Chakri Cherukuri

Advanced Data Visualizations In Jupyter Notebooks - Chakri Cherukuri

Jupyter Notebooks are becoming the IDE of choice for data scientists and researchers. They provide the users with a nice exploratory environment where they can quickly research and prototype different models and visualize the results all in one place.

With widget libraries like ipywidgets and bqplot, users can now create rich applications, dashboards and tools by just using python code.

In this talk, we will see how we can build advanced data visualization applications and interactive plots in Jupyter notebooks. We’ll look at use-cases including time series analysis using interval selectors, visualizations of machine learning models, analysis of equity markets and finally tools for building, training and diagnosing deep learning models.

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. 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, an MS in computer science from Arizona State University and another MS in computational finance from Carnegie Mellon University.

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