Data science training

Develop your people while you develop your tech

In complex, scientific domains, learning a new field is much harder than learning data science. This difficulty means that, with training, the best data scientists are the domain experts you already have. With our training-via-consulting approach, you will:

  • Develop better data scientists than you could hire
  • Reduce turnover by fostering trust and loyalty
  • Eliminate recruiting, interviewing, relocating, and on-boarding
  • Complete your data science projects months sooner
  • Eliminate inefficient consultant-to-client project turnover

The problem

In complex and scientific domains such as your own, the majority of the difficult work is in:

  1. Knowing the problem
  2. Being familiar with the data
  3. Knowing which questions to ask
  4. Knowing which knowledge is valuable to pursue

These are the skills required for effective data science. Better understanding leads to better solutions.

However, acquiring the base competency necessary to function in complex domains may take years. If an entry-level position in molecular breeding requires ten years of education, how much time should we expect to spend on-boarding new data science hires? How much mentoring will they need from your experts and stakeholders, and over how long a period? How much will your data science efforts eat into your core business capacity?

Our solution

We believe the best data scientist are the domain experts you have. These are people who never stop learning. Data science is just another tool to learn. We believe the best way to learn to do data science in the real world is to do data science in the real world.

Together with your stakeholders, we first identify a worthy project. Then we build it with your team, mentoring your subject-area experts in data science methodology, software development, project management, and best practices.

In addition to traditional machine learning methods, we teach science-appropriate probabilistic methodologies. These statistical approaches are key because transparent knowledge is vital for decision support, and science cannot occur in a black box.