5  Communication in Data Science

This chapter was written by Sara Clokey.

5.1 Introduction

Hi! My name is Sara, and I am a junior double majoring in Applied Data Analysis and Communication. The topic of my presentation today, Communication in Data Science, combines my academic and professional interests while underscoring the importance of ‘soft skills’ like public speaking, for example, within STEM fields like data science.

5.2 Importance & Application of Communication

Data science as a career path has exploded within the last decade. Some fields that offer data science positions include:

• Finance
• Healthcare
• Media production
• Sports
• Banking
• Insurance
• E-Commerce
• Energy
• Manufacturing
• Transportation
• Construction

Because data science is applicable in so many industries, it is essential that data scientists have the skills and experience to communicate their work with others who do not have the same technical education. As analyzed by Radovilsky et al. (2018), job listings within the field of data science often include qualifications like “strong interpersonal skills” and “demonstrated presentation and communication ability,” highlighting the pervasive need for this skill set.

Within these industries, collaboration and teamwork are often at the forefront. Inexperience with data should not prevent your colleagues from being able to contribute to shared projects, and strong communication skills can mitigate this challenge!

5.3 General Communication Skills

5.3.1 Verbal Communication Skills

Verbal Communication: “The use of sounds and language to relay a message” (Yer, 2018).

Verbal Communication Tips:

• Make a good first impression
• Use appropriate language (jargon, metaphors)
• Prioritize brevity
• Practice beforehand
• Allow room for questions

5.3.2 Non-Verbal Communication Skills

Non-Verbal Communication: “Information, emotion, a movement that is expressed without words and without the help of language” (Grillo & Enesi, 2022).

Non-Verbal Communication Tips:

• Utilize vocal variety (pitch, rate, volume)
• Avoid distracting hand and body movements
• Make eye contact
• Pay attention to proxemics

5.3.3 Visual Communication Skills

Visual Communication: “Any communication that employs one’s sense of sight to deliver a message without the usage of any verbal cues” (Fayaz, 2022).

Visual Communication Tips:

• Prioritize clarity
• Use proper labeling and scaling
• Create visual contrast (colors, shapes, fonts)
• Choose the most appropriate visual representation

5.3.4 Written Communication Skills

Written Communication: “Any form of communication which is written and documented from the sender to the receiver” (Prabavathi & Nagasubramani, 2018).

Written Communication Tips:

• Clearly state your goal with a thesis statement
• Maintain professionalism (contractions, slang)
• Proofread and utilize peer editing
• Follow a specific structure
• Balance concision with analysis

5.4 Communication in Data Science

Often, data scientists must communicate “technical conclusions to non-technical members”; this may be colleagues in other departments, like marketing, or supervisors at the managerial level. Here are some tips for effectively communicating project results specifically in the field of data science.

5.4.1 Identify your Audience

Who are you sharing information with? Is it a room of data scientists like this one? Is it full of students who want to learn about data science? Is it a group of executives looking to make a funding decision?

• Consider the context and prior knowledge (technical jargon)
• Consider the motivation for listening

5.4.2 Utilize Data Visualization

One of the most effective methods of communicating results in data science, especially to those without technical coding knowledge, is data visualization techniques (Vandemeulebroecke et al., 2019). Python uses the package matplotlib to produce these visualizations, including:

• line plots
• bar plots
• box plots
• histograms
• heat maps
• pie charts

These visualizations allow complex statistical projects to be simplified into a single graphic, focusing on project results and implications rather than methodology. Ensure that data visualization techniques are free of technical jargon and clearly label all visual aspects.

5.4.3 Focus on Data Communication Skills

The following skill sets highlight technical data communication that will be more common in projects with other data scientists to communicate about your data.

• Coding communication: Python, R, Julia, JavaScript, SQL, etc.
• Analysis communication: creating a storyline, descriptive versus diagnostic versus predictive analytics, problem identification
• Data management: collection, cleaning/transformation, storage
• Data visualization

5.4.4 Give Space for Questions and Feedback

Within professional spaces, data scientists should provide time for their clients, supervisors, and colleagues to ask questions about their work and subsequent findings.

• Pause for questions throughout the presentation
• Offer contact information for continued collaboration
• Provide a structure for anonymous feedback
• Schedule follow-ups if necessary

Teamwork is often at the heart of data science projects within industries, and open communication makes this teamwork run much more smoothly.

5.5 Further Learning

While pursuing degrees in the data science field, consider taking Communication courses at UConn that can bolster your understanding and skill set. Some applicable communication courses include:

• COMM 2100: Professional Communication
• COMM 3110: Organizational Communication
• COMM 3430: Science Communication
• COMM 5655: Human-Computer Interaction
• COMM 5900: Professional Communication

Effective communication also requires practice. Here are some ways to practice these skills while earning your degree:

• Fully participate in group projects
• Seek presentation opportunities (class, conferences, etc.)
• Explain data science coursework to peers outside of your program
• Explore internship opportunities that involve collaboration with other departments

Pragmatic. (2024). Communication skills for data science. https://www.pragmaticinstitute.com/resources/articles/data/communication-skills-for-data-science/.

Fayaz, O. (2022). Principles of visual communication.

Grillo, H. M., & Enesi, M. (2022). The impact, importance, types, and use of non-verbal communication in social relations. Linguistics and Culture Review, 6, 291–307.

Prabavathi, R., & Nagasubramani, P. C. (2018). Effective oral and written communication. Journal of Applied and Advanced Research, 10, 29–32.

Radovilsky, Z., Hegde, V., Acharya, A., & Uma, U. (2018). Skills requirements of business data analytics and data science jobs: A comparative analysis. Journal of Supply Chain and Operations Management, 16, 82–101.

Vandemeulebroecke, M., Baillie, M., Margolskee, A., & Magnusson, B. (2019). Effective visual communication for the quantitative scientist. CPT Pharmacometrics Syst Pharmacol, 10, 705–719.

Yer, S. (2018). Verbal communication as a two-way process in connecting people. Social Science Research Network.