Business Skills Training

for Data Scientists

I give trainings covering important business skills for data scientists.  The full training covers two days, but single and half-day trainings can be custom-built from the following modules (click modules for descriptions)

    • Understanding the business organization and key stakeholders surrounding the data scientist.
    • Understanding the place of analytics within the broader organization, including the main types of analytics deliverables as well as the sponsorship, funding and approval of analytics projects.
    • Developing solutions in a manner that maximizes their ability to deliver final business value.
  • We cover the processes of establishing and managing of stakeholder expectations and discuss how to continue execution and delivery of projects in a way that builds trust

  • We cover the basic principles of visualizing analytic results, laying the groundwork for how to best design visual representations of analytic insights within a business environment

  • This module describes in further detail the best ways to design tables and graphs for best communication of critical data insights while minimizing distracting visual clutter

  • This module provides an overview and specific examples of cultural dimensions and discusses how they can impact the data scientists’ work within an organization

  • In this module, we discuss how perceptions and hidden agendas influence interactions with stakeholders and give suggestions to help data scientists in navigating these and other hidden currents within the corporate environment

  • This module introduces principles of privacy and data governance. We examine case studies and discuss how laws differ by country.  We also highlight potential dangers in these areas that can arise from the use of analytics and discuss considerations for the work place.

  • In this module, we cover oral communication (including PowerPoint presentations), listening skills, and the process of reaching agreements with peers, management, and stakeholders

  • In this module we briefly discuss how  python compares with other development tools used  for data science.  We then introduce some of the main libraries, including pandas and scikit learn.

  • In this module we introduce the basics of Rapid Miner, one of the most popular tools for rapid prototyping of analytic models.

Some feedback from past participants

“The lecturer gave great insight in how this is applied to a project and your work

“The soft skill lessons are fun and useful and a nice switch from the hard skill lessons”

 

I really like the calm and clear style of the lecturer, he does a great job bringing across the message and does a very good job in keeping you engaged”

“Awesome”

 

 Email for more information.