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Data Science - Salaries and References
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I'm currently looking into big data because it's very interesting to me.
Claire Corthell published a Data Science curriculum that is like an encyclopedia. http://datasciencemasters.org/
The US government publishes a site called http://www.data.gov/ with over 160,000 datasets. "The home of the U.S. Government’s open data Here you will find data, tools, and resources to conduct research, develop web and mobile applications, design data visualizations, and more." As I browsed around, I found a link to the TIGER database of the census. It's a database I was familiar with when I first got out of college.
The image you see at the beginning of this article is from Citymapper, an app that uses public data you can get from Data.gov. I'm about to download it. From the comments, it's a lifechanger for some people.
Are you working with big data? Tell me about it!
O'Reilly Media is offering unlimited 30-day access to the Safari library of over 30,000 books and videos from over 200 publishers and imprints. Keith Spurr called Safari the “best technical learning resource on the internet.” You’ll have instant access to hundreds of books and videos on all aspects of big data--check out this recommended data reading list. Keep up to date on Safari-related news @safari or @safaribot.
Link: http://www.oreilly.com/pub/cpc/1551
O’Reilly Releases their 2015 Data Science Salary Survey
For the third consecutive year, O’Reilly Media conducted an anonymous survey to expose the tools that successful data scientists and engineers use, and how those tool choices might relate to their salary.
Want to know what you need to know to earn the big bucks? Knowledge of certain tools can increase your salary more than getting a Ph.D. Curious what clusters of tools are most commonly used together? Or what job titles pay the best? It's all there.
Gain insight from these potentially career-changing findings, and plug your own variables into one of the linear models to predict your own salary.
Comment
Happy 10th year, JCertif!
Notes
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Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.
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InfoQ Reading List
Presentation: How to Unlock Insights and Enable Discovery Within Petabytes of Autonomous Driving Data
Kyra Mozley discusses the evolution of autonomous vehicle perception, moving beyond expensive manual labeling to an embedding-first architecture. She explains how to leverage foundation models like CLIP and SAM for auto-labeling, RAG-inspired search, and few-shot adapters. This talk provides engineering leaders a blueprint for building modular, scalable vision systems that thrive on edge cases.
By Kyra MozleyArticle Series - AI Assisted Development: Real World Patterns, Pitfalls, and Production Readiness
In this series, we examine what happens after the proof of concept and how AI becomes part of the software delivery pipeline. As AI transitions from proof of concept to production, teams are discovering that the challenge extends beyond model performance to include architecture, process, and accountability. This transition is redefining what constitutes good software engineering.
By Arthur CasalsHow CyberArk Protects AI Agents with Instruction Detectors and History-Aware Validation
To prevent agents from obeying malicious instructions hidden in external data, all text entering an agent's context must be treated as untrusted, says Niv Rabin, principal software architect at AI-security firm CyberArk. His team developed an approach based on instruction detection and history-aware validation to protect against both malicious input data and context-history poisoning.
By Sergio De SimoneAnthropic announces Claude CoWork
Introducing Claude Cowork: Anthropic's groundbreaking AI agent revolutionizing file management on macOS. With advanced automation capabilities, it enhances document processing, organizes files, and executes multi-step workflows. Users must be cautious of backup needs due to recent issues. Explore its potential for efficient office solutions while ensuring data integrity.
By Andrew HoblitzellTracking and Controlling Data Flows at Scale in GenAI: Meta’s Privacy-Aware Infrastructure
Meta has revealed how it scales its Privacy-Aware Infrastructure (PAI) to support generative AI development while enforcing privacy across complex data flows. Using large-scale lineage tracking, PrivacyLib instrumentation, and runtime policy controls, the system enables consistent privacy enforcement for AI workloads like Meta AI glasses without introducing manual bottlenecks.
By Leela Kumili
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