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DataTalks #4: Python on Spark and location-based search

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Shay Palachy A. and inbar n.
DataTalks #4: Python on Spark and location-based search

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DataTalks @ Easy (https://easy.co.il/)

DataTalks (http://datahack-il.com/) #4: Python on Spark and location-based search

Our fourth meetup will be hosted by Easy (https://easy.co.il/), and will provide a look at data science-based approach to solve problems in location-based search and an introduction for Python on Spark.

Language: Hebrew

Location: Ayeka (http://ayeka.co/), Ellifelet 26, Tel aviv.

Schedule:

• 18:00 - 18:15 - Gathering, snacks, mingling

• 18:15 - 18:20 - Opening words

• 18:20 - 19:10 - First talk:
Erez Barshir, Easy - Data Science in location-based search

• 19:10 - 19:20 - A short break

• 19:20 - 20:40 - Second talk:
Alex Landa, Trainologic - Python Spark Intro for Data Scientists

==== Talk #1 ===

Speaker: Erez Barshir, Easy
Title: Data Science in location-based search
Abstract: Local businesses are changing fast. In Israel alone, every two or three minutes some local business changes substantially (open/close/changes location). This means that keeping a dataset of local businesses up-to-date manually is a costly and non-scalable operation. One important aspect of this problem is trying to determine whether a local business is permanently closed. We will examine a data science-based approach to this problem and try to answer some related and more nuanced questions. We will see some of Easy's engineering, real data and code and general approach to such issues.

==== Talk #2 ===

Speaker: Alex Landa, Trainologic
Title: Python Spark Intro for Data Scientists
Abstract: As a data scientist you need to know how to handle large data sets, how to clean them, analyze them and get conclusions from them. Spark is a mandatory tool for that - a distributed computation engine that enables you to run map-reduce tasks using a friendly Python (and Scala) API.

After this talk you will understand what Spark is and how to start using it. We will cover Spark architecture and workflow, understand the usage of RDD and DataFrame APIs and see some hands-on examples.

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