The Python ecosystem for data science - Landscape Overview
Submitted by Ananth Krishnamoorthy (@akrishnamoorthy) on Thursday, 27 April 2017
Full talk for data engineering track
In their day-to-day jobs, data science teams and data scientists face challenges in many overlapping yet distinct areas such as Reporting, Data Processing & Storage, Scientific Computing, ML Modelling, Application Development. To succeed, Data science teams, especially small ones, need a deep appreciation of these dependencies on their success.
Python ecosystem for data science has a number of tools and libraries for various aspects of data science, including Machine Learning, Cluster Computing, Scientific Computing, etc.
The idea of this talk is to understand what the Python data science ecosystem offers (so that you don’t reinvent it), what are some common gaps (so that you don’t go blue looking for answers).
In this talk, we describe how different tools/libraries fit in the machine learning model development and deployment workflow . This talk is about how these different tools work (and don’t work) together with each other. It is intended as a landscape survey of the python data science ecosystem, along with a mention of some common gaps that practitioners may notice as they put together a stack and/or an application for their company.
Evolving Role of Data Science Teams
Machine Learning vs Real World Data Science
Challenges faced by Data Science Teams
Data Science Workflow
Review of Key Tools
Gaps from a practitioner viewpoint
Machine learning practitioners, startups, new data science teams
Ananth Krishnamoorthy Ph.D. specializes in applying analytical techniques based on mathematical optimization, machine learning, discrete event simulation, and time series analysis, to real world business problems across various industry sectors. He has delivered several business consulting, analytical solution development, and technology implementation projects over the last 17 years.
Ananth is the co-founder of rorodata, a startup that is building a cloud based data science platform. He is also head of Hypercube Analytics, an analytics consulting company. Ananth holds a Ph.D. in Industrial Engineering and Management from Oklahoma State University