1ª Meetup – Data Analytics :: Explorando o Vale do Silício

Opa! Confesso que nem dormi ainda e estou empolgadíssima com o dia que já vai iniciando. Difícil conseguir dormir com tanta coisa para fazer e tantos conteúdos interessantíssimos para estudar. Impressionante que quanto mais eu estudo mais eu chego a conclusão que nada sei.

Hoje o dia promete ser intenso por aqui! Além de conciliar os projetos do Brasil, a revisão e publicação de conteúdos da plataforma EAD, vou participar da minha primeira Meetup. Pra quem não conhece existe um site chamado meetup.com onde você pode encontrar grupos de usuários e organizar ou selecionar os encontros que você deseja participar.

Eu me inscrevi para participar do encontro do grupo SF Data Science que possui mais de 6 mil membros e vai abordar alguns tópicos bem legais sobre Spark e Machine Learning. Será uma ótima oportunidade para networking e para aprofundar os conhecimentos.

Confere abaixo os temas que serão abordados e o nível dos palestrantes:

Talk 1: Using Spark MLlib To Predict Most Popular Tweets
Spark’s Machine Learning Library (MLlib) enables running Machine Learning algorithms in a scalable way on massive datasets. In this talk we will use Spark and MLlib to analyze tweets and predict the number of stars and retweets that a tweet will get. The talk will include a tutorial on Spark and MLlib.

Prerequisites: 
Beginner. Familiarity with a programming language will be helpful.

What You’ll Learn:  
After this talk you will be able to:
1. Use Spark to process large data sets.
2. Use Spark MLlib to apply Machine Learning algorithms to large data sets.
3. Understand pros and cons of using Spark vs other Machine Learning technologies.

Meet Your Speaker: 
Asim Jalis is a Lead Instructor in Data Engineering at Galvanize. He has worked as a software engineer and instructor at Cloudera, Microsoft, and Hewlett-Packard. He has an MS in computer science from the University of Virginia and an MA in mathematics from the University of Wisconsin—Madison.

Talk 2: Using Zeppelin Notebooks for Spark Streaming and Live Monitoring
We will discuss the rapidly evolving open source Zeppelin notebook project and how it can be used for data science applications, including those with streaming data. Zeppelin notebooks can use the scheduler functionality to update data and generate plots. This allows for live monitoring applications to be rapidly prototyped in a production environment. Simple end to end examples will be discussed.

Prerequisites: 
Intermediate to Advanced. Some experience with Spark and Zeppelin (or notebooks in general) will be helpful.

Meet Your Speaker:  
Jerome Nilmeier is a Data Scientist and Engineer at IBM in the Spark Enablement Team and the Spark Technology Center, where he works on all things Spark related. He contributes to open source, participates in community outreach, and works with clients on Spark in production environments.

Prior to his journey into big data, he was a computational scientist at the Lawrence Livermore and Berkeley National Laboratories. He holds a PhD in Computational Biophysics from UC San Francisco, and a BS from UC Berkeley in Chemical Engineering.

Sobre Viviane Ribeiro

Data Lover. Seasoned sales/technical professional, author, community champion, technical trainer and public speaker with more than 15 years of experience in IT positions where last 11 years were related to Business Intelligence, BIG Data, Cloud and Database technologies (Oracle, PostgreSQL and SQL Server). She has been responsible for successfully creating roadmaps, designing, implementing and managing complex technology solutions for high profile customers.

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