Communicating your work and results with laypeople can be a challenge — here are five things to watch out for
For many data scientists, often the coding and the analytics are the easy part. The challenge comes when you have to communicate the results of your work to non-data scientists. In many cases those individuals are clients or customers, or they hold positional superiority in the organization. This means that it’s important to get the communication right. If they leave the room or Zoom with the wrong conclusions, or just plain confused, you risk all your previous work being for nothing.
The goals of any communication of your work or results should be threefold:
Over my time doing analytics in the corporate space, I’ve learned a few things to avoid in order to maximise your chances of achieving these three goals.
Often it is assumed that everyone in the discussion is already clear on what the purpose of the discussion is and what problem is being addressed, but more often than not this common understanding does not exist. Participants often don’t have the context, or have forgotten a previous discussion, or have come out of a previous discussion with a different idea of purpose or objective.
Launching straight into analytics without ensuring that the context and objective is clear can cause all sorts of problems later in the discussion. The way people absorb content is deeply tied to how they relate it to an objective or goal. If people perceive that objective or goal differently, it’s likely any subsequent material you share will be absorbed differently by different people, sowing confusion and leading to inefficient use of discussion time.
I usually spend the first ten or so minutes of a one hour discussion simply clarifying the context and objectives. Why are we having this discussion? What specific question or problem are we trying to solve? If possible try to state the question being addressed using a single statement, ensure that there is agreement on this, and if possible try to relate this statement to a previous discussion to ensure that there is traceability in the event of a disagreement. By ensuring that this is clarified up front, you gain the capability to get the conversation back on track if it digresses later.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
We provide an updated list of best online Masters in AI, Analytics, and Data Science, including rankings, tuition, and duration of the education program.
Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.
Why should you learn R programming when you're aiming to learn data science? Here are six reasons why R is the right language for you.
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.