Ingredients for Data Science

We can develop meaningful solutions for the problems you care about by using data science to turn data into knowledge, insight, and understanding. A typical data science project involves a variety of technical and interpersonal skills and looks something like this:

  1. Define your problem and the people affected by it
  2. Determine potential meaningful solutions to your problem
  3. Identify data sources and gather your data
  4. Tidy and prepare your data
  5. Explore and analyze your data
  6. Communicate your data story to decision makers and stakeholders

Surrounding each of these processes are technical skills such as programming and statistics, and interpersonal skills such as relationship building and storytelling.

How I Can Help

I have expertise in the technical and interpersonal skills needed to make your project a success at any stage of the data science lifecycle.

I am also experienced training others in these areas if you want to level up your own data science skills.

Statistical Methods and Modelling

I am an experienced data scientist comfortable applying a variety of statistical methods and models to turn data into prediction or inference.

R and R Shiny Development

I am an experienced R programmer comfortable using my expertise for:

  • Data engineering
  • Creating automated, reproducible data pipelines and reports
  • Building Shiny applications (interactive data applications; dashboards)
  • Developing R packages

Connecting and Communicating with Stakeholders

When we use data science to solve a problem you care about, we have a responsibility to you, your audience, and the people your data comes from to develop solutions that are meaningful to them. Connecting and communicating with stakeholders at all stages of the data science lifecycle plays a central role in achieving this.

My background in psychology research and communications has given me the experience and perspective to successfully navigate these relationships.