Best of Upside's Data Digest
Articles examine data science pitfalls, benefits of big data for American Express, acquiring data for machine learning, cybersecurity and AI, and IoT.
- By Quint Turner
- July 7, 2016
The Biggest Data Science Pitfalls
(Source: Dataconomy)
Effective data science requires more work than gathering data and throwing algorithms at it. This article explains three common problems many enterprises run into with data science projects.
American Express Benefits from Big Data
(Source: Forbes)
Big data is still in relative infancy, but some enterprises are using it to great effect. Spokespeople from the credit card giant American Express describe the challenges and benefits of their big data projects in this article.
Acquiring Data for Machine Learning
(Source: Dataconomy)
Your new enterprise might start without any data to its name, which is a major problem if you want to build a machine learning or AI application. This article offers five sources for creating data sets from scratch -- useful for either training machine learning algorithms or for other advanced analytics.
Problems Created by IoT
(Source: Datamation)
This article covers seven major concerns those developing for or using the Internet of Things will face as it gets more popular.
The Future of Cybersecurity is AI
(Source: Information Age)
Few enterprises can afford a full-time cybersecurity team. This article explains how integrating automation and AI into cybersecurity will improve businesses' abilities to identify and avoid threats.
How to Secure IoT During Development
(Source: IT Pro Portal)
With few standards, securing IoT devices during development can be difficult. This article is from a maker of smart home devices; it discusses the practices used to initially secure their smart homes and then keep them safe.
About the Author
Quint Turner is an editorial intern at TDWI and an undergraduate English student at Skidmore College. Follow his blog at pungry.com.