Jul 2019
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24 Wed
25 Thu 09:15 AM – 05:45 PM IST
26 Fri 09:20 AM – 05:30 PM IST
27 Sat
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Accepting submissions till 15 Jun 2019, 01:00 PM
1. Meet Peter Wang, co-founder of Anaconda Inc, and learn about why data privacy is the first step towards robust data management; the journey of building Anaconda; and Anaconda in enterprise.
2. Talk to the Fulfillment and Supply Group (FSG) team from Flipkart, and learn about their work with platform engineering where ground truths are the source of data.
3. Attend tutorials on Deep Learning with RedisAI; TransmorgifyAI, Salesforce’s open source AutoML.
4. Discuss interesting problems to solve with data science in agriculture, SaaS perspective on multi-tenancy in Machine Learning (with the Freshworks team), bias in intent classification and recommendations.
5. Meet data science, data engineering and product teams from sponsoring companies to understand how they are handling data and leveraging intelligence from data to solve interesting problems.
For more information about The Fifth Elephant, sponsorships, or any other information call +91-7676332020 or email info@hasgeek.com
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Pushker Ravindra
@rpushker
Submitted Apr 15, 2019
How many times did you feel that you were not able to understand someone else’s code or sometimes not even your own? It’s mostly because of bad/no documentation and not following the best practices. Here I will be demonstrating some of the best practices in Data Science, for R and Python, the two most important programming languages in the world for Data Science, which would help in building sustainable data products.
Integrated Development Environment (RStudio, PyCharm)
Coding best practices (Google’s R Style Guide and Hadley’s Style Guide, PEP 8)
Linter (lintR, Pylint)
Documentation – Code (Roxygen2, reStructuredText), README/Instruction Manual (RMarkdown, Jupyter Notebook)
Unit testing (testthat, unittest)
Packaging
Version control (Git)
These best practices reduce technical debt in long term significantly, foster more collaboration and promote building of more sustainable data products in any organization.
Why Data Science Best Practices?
Why R & Python
Data Science Best Practices
Integrated Development Environment (RStudio, PyCharm)
Coding best practices (Google’s R Style Guide and Hadley’s Style Guide, PEP 8)
Linter (lintR, Pylint)
Documentation – Code (Roxygen2, reStructuredText), README/Instruction Manual (RMarkdown, Jupyter Notebook)
Unit testing (testthat, unittest)
Packaging
Version control (Git)
Conclusion
None
I have BTech in Electrical Engineering from IIT Kanpur, Executive General Management from IIM, Bangalore and PhD in Bioinformatics / Computational Biology from UCD, Ireland. After PhD, I worked as the Head of Software Development at Genome Life Sciences, Chennai. I have more than 10 years of experience in the field of Genomic Data Science at international research organizations including UMH, Alicante (Spain), IGIB, Delhi and Monsanto, Bangalore. Currently, I am leading Data Analytics platform at Monsanto (a subsidiary of Bayer), Bangalore.
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