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How Machine Learning Can Help Identify Cyber Vulnerabilities

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People are undoubtedly your company’s most valuable asset. But if you ask cybersecurity experts if they share that sentiment, most would tell you that people are your biggest liability.

Historically, no matter how much money an organization spends on cybersecurity, there is typically one problem technology can’t solve: humans being human.  Gartnerexpects worldwide spending on information security to reach $86.4 billion in 2017, growing to $93 billion in 2018, all in an effort to improve overall security and education programs to prevent humans from undermining the best-laid security plans. But it’s still not enough: human error continues to reign as a top threat.

Sourced through Scoop.it from: hbr.org

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10 Tips for Building Effective Machine Learning Models – insideBIGDATA

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In this contributed article, Wayne Thompson, Chief Data Scientist at SAS, provides 10 tips for organizations who want to use machine learning more effectively. Machine learning continues to gain headway, with more organizations and industries adopting the technology to do things like optimize operations, improve inventory forecasting and anticipate customer demand.

Sourced through Scoop.it from: insidebigdata.com

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IBM Recognizes Aegis School of Data Science for Building Skills in Analytics & Data Science /PR Newswire India/

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BENGALURU, September 6, 2017 /PR Newswire India/ — IBM Recognizes Aegis School of Data Science for Building Skills in Analytics & Data Science.

Aegis was recognized for ‘Building Capabilities in the areas of Analytics & Data Science for the students and working professionals using IBM Software’ by Yeo Hwee Lee, Director, Software Services, Software Group Asia Pacific and Paramantapa Dasgupta, Career Education for IBM India & South Asia.

Sourced through Scoop.it from: www.prnewswire.co.in

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Data Science and the Data Science Process – Wintellect

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Before we get into the fun part of working with data, let’s break down how data science involves more than just statistics, why it’s becoming more important, and the data science process. Data Science vs. Statistics In short, data science is extracting knowledge from data. But how is that different between statistics? Data science encompasses…

Sourced through Scoop.it from: www.wintellect.com