#100DaysOfCode - Week12

For my twelfth week of the #100DaysOfCode Challenge, I leveraged my previous data science work into machine learning exercises.

Machine Learning

Machine learning involves using automated analysis of data to make predictions. When you send a text, shop on Amazon, or browse Netflix, you're presented with recommendations about your next word to write, your next purchase to make, or your next movie to watch. These recommendations are made possible through the use of machine learning. For example, a data scientist may use known characteristics about a customer--such as age, location, and previous purchases--to build a model for making predictions about future purchases.

Supervised Learning

Algorithms developed using both input data and output data are said to be "supervised" because the desired outcomes are used to train the model. As a result, evaluating the accuracy of an algorithm developed through supervised learning is often straightforward. One way to test the accuracy of an algorithm is to split up a data set, train the algorithm on part of the data and then test its predictions against the known results already included in the remaining data. Continuing with the example in the previous paragraph, the data scientist gathers customers' shopping histories, uses them to train an algorithm that predicts subsequent purchases, and then compares those predictions against those customers' known subsequent purchases. In that way, the accuracy of the model is tested, and the effectiveness of the algorithm is known.

Data Governance and Strategy

Algorithms are powerful in ways that are becoming increasingly apparent all around us. From fighting financial crime to selling pet food, the world is full of opportunities for professionals who can find answers within the data. Unfortunately, organizations also are finding many challenges in implementing these solutions because managing large quantities of data and knowing how the data can be used to solve problems are difficult activities.

Furthermore, large quantities of data need to be maintained and protected. My work involves helping organizations protect valuable data such as financial records, personal health information, and intellectual property. And from the looks of things, there's going to be a lot more of this type of work in the future.

So if this type of career sounds good to you, feel free to reach out to me. I'm here to help!

Rob Valdez, CPA, CISA, CISM, is a risk advisory services manager in Kaufman Rossin’s Boca Raton, Florida, office and provides cyber risk and compliance services, including PhishNet by Kaufman Rossin, a security awareness training service. Rob can be reached at rvaldez@kaufmanrossin.com.