We believe that useful training is a balance between high level concepts and pragmatic skills and tools, applied to questions and data in your domain area.

We offer conceptual training in biostatistics, that incorporates hands-on training using R software. We can customize our courses to use your data to address your questions. 

You have experimental or observational data, and you want to know what's significant, what's important, and what you can conclude. 

• Descriptive stats
• High-throughput screening

You have omics data. (Lots of omics data!) We help you to gain insight into your systems and can analyze:

• SNP/variant calls
• RNAseq transcriptomics
• Metabolomics

You have an idea, and you want to design an experiment to effectively and efficiently test your idea. We help with:

• Design-of-experiment analysis (DOE) to determine what experimental factors matter most.
• Power Analysis to determine number of replicates.

You want to best leverage your multivariate data. We offer

• Regression modeling
• Predictive modeling
• Unsupervised learning to uncover patterns in your data

You have old data and are curious about patterns, trends, and relationships. We delve into the data to provide guidance on future research.

Our strategic consulting advisory services are designed to help our clients address this question.  Here, we work with Biotechnology and Life Science Leaders and focus on determining how their data, and data science, can help them achieve their R&D and business goals.

We work with Biotech and Life Science Leaders to assess their data science platform. 

Typically, we structure our engagements as projects. Because each project is unique and customized to the needs of our clients, we price each project individually. 

Previous engagements have involved:

• An assessment of measurement systems and data quality in Research Operations
• Developing data analytic workflows in the cloud (AWS)
• Database design to support data science activities (data capture, storage, analysis and reporting)
• Automating data science workflows
• Using data to optimize high-throughput experiments
• Designing & optimizing "scale-down" assays using predictive modeling

 The Data Science Review is a comprehensive diagnostic assessment of your (proposed) data science platform. It is a project that includes:

• Technology Platform Diagnostic and Review
• Data Audit
• Roadmapping

The package is attractive to small and  mid-sized life science companies, or functional groups within large organizations, coming to grips with new data streams & new types of analyses.

Engagements typically run 2-4 weeks and are priced at $18,000.