Infrastructure and tools for data science:

A beginner-friendly introduction

Infrastructure and tools for data science: A beginner-friendly introduction

  • Saturday, 13 July 2019

    1pm – 4pm

  • Ruang & Tempo

    8th Floor, Gedung Tempo
    Jl. Palmerah Barat No.8

  • IDR 100,000

Course Summary

2019 is a great year to learn data science. There has been an enormous growth in data science-related jobs to match the equally relentless pace of growth in big data adoption. Dresner Advisory Services’ Market Study 2017 survey shows a year by year increase in big adoption just north of 50 percent and Glassdoor puts Data Scientist at the top of the list of 50 best jobs in America at the end of 2018.

In this session, in-house course producer Samuel will bring to the stage his wealth of experience in building data science teams and cultivating excellence. This includes the tools and infrastructure, applied processes, learning management and accompanying technology for analytical teams. From his time staying in China and Japan as a consultant to his role as developer-trainer in the last 2 years, we will learn about his own journey, and what challenges lie ahead of budding data scientists.

Syllabus

  • A review on the current landscape of Data Science
    • The whos, whats, and hows
    • The perils of notebook
  • It’s more than R and Python running on a local notebook
    • How to productionize your work
    • “Service-based” data science
    • “Product-based” data science
    • Templates and resources for productionizing data science
  • Serve your machine learning model
    • Cloud infrastructure
    • Tools for machine learning serving
    • Productionize machine learning models
  • Build your first product
    • Ditch that “notebook” mentality
    • Templates and resources for productionizing data science
    • Software engineering or data science?
  • Live Demo
    • From research-based work to production
    • Packaging your code
    • Building apps with machine learning features
    • Building widgets to interact with your machine learning model through an API
    • Q&As