What we're about

We are all motivated and tasked to create a friendly, information sharing community for local talent pool. We also support our strong offline community with online tools to improve engagement and create deep networks so we all could learn from each other. Professionals, join #Big Data Analytics Israel (https://www.meetup.com/Digital-Analytics-Israel/) to help build a strong and vibrant data analytics community. You are welcome too!

Who is this meetup for?

• Data Scientist, Data engineers, analysts, and other data practitioners

• Software engineers, software architects and other looking to implement machine learning in their code

• Non-technical team leads, executives, and other decision makers

• Companies, startups and others looking to leverage data science to grow their business

Our YouTube Channel:



If you are looking for a new opportunity as Data Analyst or Data Scientist - please contact me in private - raya.belinsky.meetup@gmail.com

Upcoming events (2)

Drinks with Data Scientists

Radio EPGB

Join our first Drinks with Data Scientists! Enjoy this great opportunity to connect with your fellow Data Scientists, share knowledge, experiences and mainly have some fun with like-minded people. Let's have an amazing networking time together! Starting at 7:00 pm Invite your friends, or come by yourself and make new ones!!

Validation, Visualization and Decision Making with Data

Google for startups Campus

18:00- 18:30- Gathering, mingling 18:30 - 19:00 - First talk with Yotam Perkal "Dataframe Validation In Python" About: As Machine Learning models rely on data in order to make their predictions, data quality evaluation is a crucial aspect of any ML pipeline. We as Engineers/Data-Scientists, should validate our data in the same manner in which we validate our code. Data errors can lead to: Bad and costly decisions, Inaccurate predictions due to invalid data and Time waste. There is an abundance of different libraries that perform various kinds of data integrity checks. I will specifically focus on Dataframe validation. In this talk, I will present the problem and give a practical overview (accompanied by Jupyter Notebook code examples) of three libraries that aim to address it: Voluptuous - Which uses Schema definitions in order to validate data [https://github.com/alecthomas/voluptuous] Engarde - A lightweight way to explicitly state your assumptions about the data and check that they're actually true [https://github.com/TomAugspurger/engarde] * TDDA - Test Driven Data Analysis [ https://github.com/tdda/tdda] By the end of this talk, you will understand the Importance of data validation and get a sense of how to integrate data validation principles as part of the ML pipeline. Bio: I am a Data Scientist and Security Researcher at PayPal. I am very passionate about Cyber Security and Machine Learning and specifically intrigued by the intersection between the two. Whether it be using ML in order to help solve Cyber Security challenges or exploring the challenges in securing ML applications. 19:10 - 19:45 - Second talk with Michael Kogan "Introduction to Graph Algorithms". The talk will be held in Hebrew. About the talk: Graph algorithms are the powerhouse behind analytics for connected systems. These algorithms use the connections between data to evaluate and infer the organization and dynamics of real-world systems. In this session we'll make an intro to the world of Graph thinking and the algo's we use for different use cases. Bio: Head of Data and Analytics Solutions at Yael Group 19:50 - 20:30 - Third talk/workshop with Nimrod Ruby "Critical thinking - intuitive judgment". The talk will be held in Hebrew. About the talk: We will examine how we make judgments under uncertainty and explain how various biases can distort our consideration of evidence. Based on the studies and publications of Prof Amos Tversky RIP and Prof. Daniel Kahneman (Nobel prize winner). Bio: B.Sc. Industrial Engineering, MBA COO / CEO / VP Operations in global industrial companies. Thinker, in continuous search for improving my skills.

Past events (14)

Women in Big Data

Google for startups Campus

Photos (55)