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Communication is an Important Skill for a Data Scientist

Data science analytic skills are important but another key skill is communicating. You can have the best ideas or the most creative findings but if you cannot communicate your ideas to your colleagues, manager, investors, or executives then your value to an organization is limited. To this point, communication of your findings can be as important as your findings. Additionally, there are many ways to communicate findings and we, as data scientists, need to master them all.

How to Communicate Data Findings

Informal Reporting

What are the best way to communicate findings? It depends on the venue. Sometimes the venue is a “drive by” conversation with a colleague, an impromptu meeting with an executive, or an informal gathering at the office. In this case, the quick pitch or “elevator pitch” is optimal. Like the article abstract in academia, it delivers a bottom line up front (BLUF) to catch the attention of the listener and make them want to hear more. The goal of the quick pitch is to get the listener to want to connect later with time to hear more of your work.

Formal Reporting

Other times, more formal written communication is required. This comes in many forms. The short form is most akin to the “status update” emails often requested. Again, BLUF is the key with concise discussion that enables a clear understanding of findings and what the findings mean to the organization. Often, managers have hundreds of emails to read in a day; concise explanations are appreciated and most likely to enable them to synthesize your findings for others or apply your ideas in higher-level meetings.

Long Form Data Reporting

In other situations, the more formal long-form reporting is required. This longer version of the synopsis covers the academic equivalent of a research project or paper. The components are an executive summary, introduction, context (literature review), question, method, findings, and conclusion. Tables of inputs, figures, references and assumptions are all laid out and carefully explained. There is a lot of time spent making sure the report is deep enough to show that all aspects were considered but not so long as to keep people from reading and using the information. This is a key balance and often hard to attain without practice and feedback.

Preparing for the Presentation

Separate and distinct from the quick-pitch, short and long-form writing is the presentation. The data scientist uses the art of data visualization to tell the data’s story and persuade. Preparation for the presentation includes the other types of communicating discussed; each causes you to mentally prepare for the presentation in different ways. The quick pitch helps you identify what gets the best reaction from others. The short-form writing helps you generate concise ideas and your BLUF messaging. Long-form writing help you have a deep understanding of every aspect of your work. The formal presentation, when done well, is a thing of beauty that combines deep and thoughtful analysis with robust and dynamic data visualizations using software like Tableau, Alteryx or SPSS. Additionally, the presenters understand the motivations and bias of the audience to enable more effective communication.

Utica College’s Data Science Program

Each of these communication pathways are different enough to require practice in each. Throughout Utica College’s Masters of Science in Data Science program, students practice all four communication pathways. It starts in the first course you take, DSC 501, Introduction to Data Science. Students practice all four communication pathways as a part of their course project and weekly course work. Throughout the program, the communication is emphasized, practiced, and assessed. Video submissions are used to submit pitches and presentations. A whole course is dedicated to the development of data communication skills: DSC 611, Data Visualization. This course is taken just prior to a student’s capstone experience to enable them to have the skill to properly communicate their thesis or capstone project findings. To graduate students present their capstone project or thesis and receive feedback. It is an exciting and rewarding event that reveals your mastery of the data science coursework and practiced communication skills.