Where should you put your data scientists?
Stand-alone, embedded, or integrated teams? It depends on what you value.
Data science
Data science ideas and resources.
Data Scientists: Generalists or specialists?
The answer depends on your company’s stage of maturity.
Building data science teams: Preparing your organization
How should you prepare when assembling and integrating a data science team into your organization? In this video training segment, Paco Nathan offers tips to consider in the early stages, including designating the right executive sponsor and encouraging basic hands-on data science training for management.
Causality and data science
When using data to find causes, what assumptions must you make and why do they matter?
Spanning the stages of data analytics
Sharing metadata is key to managing the data pipeline
We need open and vendor-neutral metadata services
Comprehensive metadata collection and analysis can pave the way for many interesting applications.
Specialized and hybrid data management and processing engines
A new crop of interesting solutions for the complexity of operating multiple systems in a distributed computing setting.
Improving corporate planning through insight generation
Data storage and management providers are becoming key contributors for insight as a service.
Uncovering patterns in data science tools, tasks, and compensation
The 2015 Data Science Salary Survey reveals insights for data professionals.
How advanced analytics are impacting sports
The expanding role of data analytics in a trillion-dollar industry.
Old-school DRM and new-school analytics
Piracy isn’t the threat; it’s centuries old. Music Science is the game changer.
2016 Data Science Salary Survey
As a data professional, you are invited to share your valuable insights. Help us gain insight into the demographics, work environments, tools, and compensation of practitioners in our growing field. All responses are reported in aggregate to assure your anonymity. The survey will require approximately 5-10 minutes to complete.
The music science trifecta
Digital content, the Internet, and data science have changed the music industry.
Data and social good
Using data science to improve lives, fight injustice, and support democracy
Handling missing data
Considerations for managing missing data and a look at how Pandas tools can address missing data in Python.
Operations in Pandas
Python Data Science Handbook: Early Release
Data Indexing and Selection
Python Data Science Handbook: Early Release
Introducing Pandas Objects
Python Data Science Handbook: Early Release
Preface to Python Data Science Handbook (Early Release)
Python Data Science Handbook: Early Release
Graphs in the world: Modeling systems as networks
See, extract, and create value with networks.
I ♥ Logs: Apache Kafka and Real-time Data Integration
This webcast talk will discuss how logs and stream-processing can form a backbone for data flow, ETL, and real-time data processing.
Connected play
How data-driven tech toys are — and aren’t — changing the nature of play.
Data Science from Scratch
Today’s messy glut of data holds answers to questions no one’s even thought to ask.
Data Science in the Cloud with Microsoft Azure Machine Learning and R
This report covers the basics of manipulating data, as well as constructing and evaluating models in Azure ML, illustrated with a data science example.