Are you seeking a career in data science? If so, developing your communication skills is crucial to increase your chances of landing a data science role. As a data scientist, you’ll be relied upon to clearly communicate technical conclusions to non-technical members, such as those working in marketing and sales.
Why is having strong communication skills so critical in data science?
Here are some of the reasons why communication is a fundamental skill in data science:
Communicate data science results skillfully
As a data scientist, it’s essential to make sure you know how to communicate data science knowledge to individuals who aren’t versed in data. Transferring knowledge across departments is crucial, so it’s vital to share insights and analyses in simple, clear terms that don’t overwhelm individuals with jargon or technical details.
Work with others effectively
You may spend lots of time working alone with a computer, analyzing algorithms and datasets. However, you may also find yourself working with others. You may work alongside data analysts or other scientists as part of a team, especially when handling large datasets or working on big projects. Beyond this, you may also frequently work with other teams of professionals who don’t work with data. Thus, it’s essential to be an excellent communicator to work with others effectively.
Hold attention with excellent data presentation skills
As a data scientist, you may have to present your findings to clients or colleagues with presentations. Hence, clear and effective communication is essential. You need to present complex analyses to others in a short time without rushing. You should also be able to create attention-grabbing and accessible data visualizations.
Seven ways to improve your data science communication skills
Here are a few ways to improve your data science communication skills:
1. Identify your audience and speak their language
Tailoring communication to your audience can increase the likelihood that your recommendation will be convincing. To make the strongest appeal among business stakeholders, consider understanding who they are and what their priorities are. Usually, a company’s decision-makers are very busy with many priorities competing for their attention, especially in fast-growing companies. Thus, connecting the new recommendations and insights to your target audience’s existing objectives and goals is one of the easiest ways to capture their attention. Providing a short explanation of why the insight is important, framed in terms of the possible impact on the critical performance metrics of the audience, is a simple and concise way of highlighting the relevance and value of an insight to their performance success.
For instance, if your insight is about API latency and your audience is the engineering team responsible for that API, it would be best to use relevant domain terminologies or metrics because the audience already has the technical context or knowledge necessary to understand the analysis fully. Likewise, if the audience is finance decision-makers, it would be wise to frame the insight in the context of potential EBITDA (earnings before interest, taxes, depreciation and amortization) impact, a financial metric, making the insight more easily understood and relevant.
2. Use the TL;DR approach to clearly communicate what matters
One way to grab your audience’s attention and highlight the relevance of an insight to the business is to use the TL;DR approach (short for “Too Long; Didn’t Read”) at the beginning of every analysis. This approach is a clear, concise summary of the content (typically one line) that frames essential insights in the context of impact on key business metrics. It helps you define the bottom line, making it easier for the company’s decision-makers to recognize the value of your insights and learn more.
Having clear, actionable titles can give your audience an idea of what’s to come, so they’ll be ready to pay attention to the details of your presentation. You can also apply the TL;DR approach to any subheadings you used in your presentation materials, analyses or charts.
Two strategies can make this approach easier to implement:
- Prevent ambiguity and ensure that all subtitles or analyses look like the title of a newspaper article. Although you may be tempted to have a slide titled “Problem,” that is much less appealing than something more specific like “The problem with decreasing website click-through rates.”
- Consider leading with the recommendation instead of just the data: This gives your audience the bottom line faster and catches their attention. For instance, instead of saying something like, “50% of first-time visitors to a website don’t click on an item”, you can say, “Improving item recommendation can increase first-time visitor click-through rate by 50%”.
3. Use visualizations whenever applicable
Spreadsheets, illustrations, graphs and charts can work wonders for an otherwise dull report. Today’s computers, including laptops and many tablets and smartphones, come preloaded with intuitive design applications for this very purpose. Utilize these applications to your advantage whenever applicable, as they can make it easy to display datasets, highlight statistics and draw attention to the most critical points you’re trying to make.
When utilizing visualizations, make sure to avoid confusing the audience. Avoid presenting unnecessarily complex visualizations, as this can distract your audience from the critical insight and make the overall communication of an insight less effective. For instance, a facet grid or correlation matrix can be an efficient way to explore relationships in data, but presenting a dense visualization might confuse business stakeholders and distract you from communicating the key insights. Even an insight initially discovered using an advanced visualization strategy can often be summarized with a simple table or chart, which will be easier for all audiences to understand.
4. Gather questions and feedback
Before you finalize your project or end your report, consider soliciting direct feedback from your audience. It doesn’t matter if you have to prompt them to ask you questions or if they’re impatient to put your knowledge to the test—this form of interaction can help you improve your communication skills and establish a successful career as a data scientist.
5. Use a structured communication strategy
A structured communication strategy can go a long way in driving alignment with your audience. Consider using a three-step communication strategy:
- ‘Telling’ your audience the subject of your presentation.
- Actually ‘telling’ your audience.
- Synthesizing what they were just ‘told’.
This communication strategy is beneficial for a meeting with cross-functional participants, as analytics recommendations and insights can sometimes get technical or granular, making it harder for all participants to follow along successfully. Thus, it’s essential to summarize the agenda upfront and recapitulate the conclusions at the end of the meeting.
A structured communication model can give your audience many opportunities to understand the top-level topics and not get lost in the details they didn’t fully understand. Additionally, using a framework to communicate the five Ws—What, Who, Why, Where and When—can help you provide consistency to the communication and allow you to put insights into context.
6. Focus on the result
Make sure not to get bogged down with the technical details of any specific project. Also, don’t overload your audience with information from the beginning. Instead, start by drawing attention to the result and work backward. Rather than explaining the technical requirements or specifications of a new process or application, consider describing the final benefits. This allows you to capture your audience’s attention immediately and helps you address any other concerns and gain the necessary approval.
7. Continue communication until the recommended actions are complete
As a data scientist, you may sometimes move on to other projects after sharing your insights. This may create a disconnect between you and the team executing those insights, causing delays or sometimes misinterpretations and driving suboptimal results. Thus, to minimize these risks, it’s crucial to have a proactive communication plan for the later stages of a project.
For instance, for an analysis driving actionable insights, ensuring the communication channels are open and conducting regular follow-ups can help keep track of the progress and provide efficient execution. This regular communication may involve asking for status updates, answering questions, highlighting road blockers or iterating towards an even better solution.
Are you ready to improve your data science communication skills today?
There has never been a better time to improve your data science communication skills. As a data scientist, you need strong communication skills to round you off and make your insights or analyses accessible to the rest of the company. Communicating effectively with professionals in other departments can provide opportunities that ensure your career longevity within the company. The Data Incubator offers an intensive training boot camp where you can learn from industry-leading experts to develop your communication skills.
To help you achieve your dreams, take a look at the programs we offer:
- Our Data Science Engineering Bootcamp bridges the gap between data engineering and data science.
- Become a proficient data scientist with our Data Science Bootcamp.
- Expand your data experience and get ready to apply for the data science fellowship with our Data Science Essentials part-time online program.
Ready to develop your communication skills with us? Contact our admissions team if you have any queries regarding the application process.