Advancing Procurement Analytics
Learn how your company can significantly improve procurement analytics to solve business questions quickly and effectively.
Data science
Data science ideas and resources.
The industrialization of analytics
Analytic Ops—DevOps for data science—makes data analysis into a continually evolving process to meet business needs.
In Search of Database Nirvana
Rohit Jain takes an in-depth look at the possibilities and the challenges for companies that long for a single query engine to rule them all.
Why ACID transactions matter in an eventually consistent world
Systems with weak consistency guarantees can be expensive in unexpected ways.
Improving operations using data analytics
How combining data and applying time-series techniques can provide insights into a company’s operational strengths and weaknesses.
All the Data and Still Not Enough!
Claudia Perlich discusses tricks to the art of predictive modeling in situations where the right data is scarce.
Getting Started with Deep Learning using Keras and Python
Experiment with deep learning neural networks using Keras, a high-level alternative to TensorFlow and Theano. Get started by focusing on model structure, and avoid the complexity of numerical programming on GPUs. Play faster and go deep.
Probabilistic data structures in Python
Use approximations with error bounds to trade-off system resources, e.g., memory or compute time -- especially for large-scale analytics and streaming data.
3 ideas to add to your data science toolkit
Techniques to address overfitting, hyperparameter tuning, and model interpretability.
Creating meaningful metrics that get your users to do the things you want
Buddy Brewer, Steve Souders, and Mark Zeman illustrate how identifying and focusing deeply on a few meaningful metrics facilitates far better decision-making.
Predictive maintenance meets predictive analytics
Danielle Dean introduces the landscape and challenges of predictive maintenance applications in the manufacturing industry.
Breaking down data and technical silos
Specialized technical tools are great, but sometimes a general contractor is the best approach.
The elements of anomaly detection in the Internet of Things
Predictive-maintenance modeling requires a lot of work, but some can be automated.
Data Infrastructure for Next-Gen Finance
With a focus on engineering and infrastructure, this O’Reilly report examines the tools and best practices that leading financial firms are using to migrate data to the cloud, build customer event hubs, and adhere to new rules for governance and security.
Don’t underestimate “easy” when it comes to data access
The difference between failure and success may be the difference between making analytics possible and making it straightforward.
Modeling your big data enterprise architecture after the human body
How decoupling, optimization, and specialization resemble connective systems in our bodies.
Organizing big data with the crowd
Using real-world cases, Lukas Biewald describes microtasking, where it fits in the crowdsourcing landscape, and how data scientists and developers can tap into the crowd to collect and process data sets.
10 signs of data science maturity
How well prepared is your organization to innovate, using data science? In this report, two leading data scientists at Booz Allen Hamilton describe 10 characteristics of a mature data science capability.
Good cities, not just smart cities
Daniele Quercia discusses mapping city scents, computational social science, and using sharing economy data to help shape city regulations.
The preoccupation with test error in applied machine learning
Measure your model’s business impact, not just its accuracy.
Getting Started with Deep Learning using Keras and Python
Experiment with deep learning neural networks using Keras, a high-level alternative to TensorFlow and Theano. Get started by focusing on model structure, and avoid the complexity of numerical programming on GPUs. Play faster and go deep.
Ditch the data dump
How in-page analytics and design thinking produces a rich, functional product.
Data’s international impact on manufacturing
A new O’Reilly report explores global trends in data analytics for the Industrial IoT.
Why interpretability matters in data analytics
The results our algorithms produce must be approachable for an ever-growing audience.