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You're reading from  Hands-On Data Analysis with Pandas - Second Edition

Product typeBook
Published inApr 2021
Reading LevelIntermediate
PublisherPackt
ISBN-139781800563452
Edition2nd Edition
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Stefanie Molin
Stefanie Molin
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Stefanie Molin

Stefanie Molin is a data scientist and software engineer at Bloomberg LP in NYC, tackling tough problems in information security, particularly revolving around anomaly detection, building tools for gathering data, and knowledge sharing. She has extensive experience in data science, designing anomaly detection solutions, and utilizing machine learning in both R and Python in the AdTech and FinTech industries. She holds a B.S. in operations research from Columbia University's Fu Foundation School of Engineering and Applied Science, with minors in economics, and entrepreneurship and innovation. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.
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Simulating login attempts

Since we can't easily find login attempt data from a breach (it's not typically shared due to its sensitive nature), we will be simulating it. Simulation requires a strong understanding of statistical modeling, estimating probabilities of certain events, and identifying appropriate assumptions to simplify where necessary. In order to run the simulation, we will build a Python package (login_attempt_simulator) to simulate a login process requiring a correct username and password (without any extra authentication measures, such as two-factor authentication) and a script (simulate.py) that can be run on the command line, both of which we will discuss in this section.

Assumptions

Before we jump into the code that handles the simulation, we need to understand the assumptions. It is impossible to control for every possible variable when we make a simulation, so we must identify some simplifying assumptions to get started.

The simulator makes the...

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Hands-On Data Analysis with Pandas - Second Edition
Published in: Apr 2021Publisher: PacktISBN-13: 9781800563452

Author (1)

author image
Stefanie Molin

Stefanie Molin is a data scientist and software engineer at Bloomberg LP in NYC, tackling tough problems in information security, particularly revolving around anomaly detection, building tools for gathering data, and knowledge sharing. She has extensive experience in data science, designing anomaly detection solutions, and utilizing machine learning in both R and Python in the AdTech and FinTech industries. She holds a B.S. in operations research from Columbia University's Fu Foundation School of Engineering and Applied Science, with minors in economics, and entrepreneurship and innovation. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.
Read more about Stefanie Molin