Applied Statistics, Data Science and Quality in Clinical Bioinformatics Copy —
Training activity: 16

Details

Visualise non-statistical data for an end user.

Type

Developmental training activity (DTA)

Evidence requirements

Evidence the activity has been undertaken by the trainee​.

Reflection on the activity at one or more time points after the event including learning from the activity and/or areas of the trainees practice for development.

An action plan to implement learning and/or to address skills or knowledge gaps identified.

Reflective practice guidance

The guidance below is provided to support reflection at different time points, providing you with questions to aid you to reflect for this training activity. They are provided for guidance and should not be considered as a mandatory checklist. Trainees should not be expected to provide answers to each of the guidance questions listed.

Before action

What are the intended outcomes of the training activity?

• How will you demonstrate your ability to arrange and store data so that it is properly structured for programmatic analysis, such as by using data frames, data structures or by designing a relational database?
• What specific steps will you take to perform programmatic data analysis to prepare the dataset for visualisation without relying on statistical tests?
• How will your final implementation effectively summarise the results to your stakeholders to support their specific clinical or laboratory decision-making?
• What do you need to know about the end user’s requirements before you begin designing the visualisation to ensure it is fit for purpose?
• How does this task help you contextualise the structure of genomic data and the importance of communicating insights derived from that data?

What do you anticipate you will learn from the experience?

• What specific insights do you hope to gain regarding the selection of effective visualisation methods (e.g., using ggplot or matplotlib) that are most suitable for genomic datasets?
• What do you anticipate learning about the limitations and strengths of presenting raw data trends versus statistical summaries when communicating with non-bioinformatician stakeholders?
• Based on what you already know about programming, what new programmatic techniques do you expect to master to automate the generation of these visualisations?
• What do you think the types of genomic or bioinformatics data that benefit from visual representation beyond statistical summaries are?

What actions will you take in preparation for the experience?

• Have you discussed the specific dataset and end-user profile with your Training Officer to ensure your planned visualisation style and delivery method are appropriate?
• What steps are you taking to handle potential challenges, such as cleaning “messy” data or managing missing values before they are stored and accessed programmatically?
• Which programming libraries or documentation do you need to review beforehand to ensure your code is efficient and well-documented?
• How have you planned to validate the data being visualised to ensure that the summary you provide to stakeholders is accurate and reliable?
• How do you feel about the prospect of designing a custom data solution, and can you clearly identify the point at which a technical or design hurdle might require you to seek specialist advice?
• Will you research existing visualisation conventions in clinical genomics? Will you create mockups or wireframes before implementation?
• Who is the intended end user, and what is their level of technical expertise? How will this influence your design choices?

In action

What are you doing?

• How are you currently approaching the arrangement and storage of your data to ensure it is structured appropriately for programmatic access?
• Why have you chosen a specific data model (e.g. a relational database like PostgreSQL or a dataframe-based approach) for this particular dataset?
• As you perform the programmatic data analysis, what specific decisions are you making regarding data cleaning or filtering before the visualisation stage?
• What aspects of your coding practice feel intuitive—such as writing basic loops or using pattern matching—and which parts require more conscious effort, such as implementing complex data visualisations?
• How are you ensuring that your choice of visualisation method is appropriate for an end user and effectively communicates the data without relying on statistical tests?

How are you progressing with the activity?

• How effective are your current programmatic actions in creating a visualisation that clearly summarises the results for your stakeholders?
• What specific challenges are you facing as the activity unfolds, such as encountering unexpected data formats, software errors, or difficulties in presenting raw data trends clearly?
• What are you learning in the moment about the interplay between how data is stored and how easily it can be interrogated and visualised?
• How does this specific task connect to your existing skills in programming and scripting, or previous experiences with genomic datasets?
• Are the plots you are generating effectively communicating the intended information to the end user, or are they revealing unexpected patterns that require further analysis?
• What design choices are you making regarding colour, scale, labelling, and interactivity? How might these affect interpretation or accessibility?

How are you adapting to the situation?

• If your initial choice of plot (e.g., a bar chart) is not revealing the data patterns effectively, what alternative visualisations or plotting parameters are you considering?
• Are you encountering any logic or syntax errors in your code, and how are you adapting your troubleshooting approach to resolve these in real-time?
• What support or guidance might you need in this moment—perhaps from a Training Officer regarding the end user’s technical background—to ensure the summary is accessible?
• Are you working within your scope of practice, and can you identify the exact point at which a technical issue (such as database connection failures or complex UI requirements) requires you to seek specialist advice?
• If you find that your data storage model is making analysis difficult, are you willing to revisit and adjust youdesign to better support the final output?
• How is the visualisation performing with real data? Are there any rendering issues, performance bottlenecks, or unexpected visual artefacts? How are you addressing these?

On action

What did you notice?

• How would you summarise the key steps you took to move from raw data to a final visualisation, specifically regarding how you arranged and stored the data for analysis?
• What were the main sources and formats of your data, and how did your programmatic analysis prepare this information for the end user?
• Which specific visualisation methods did you implement, and did you notice any particular challenges in making the data clear for your stakeholder?
• Did the end-user’s requirements change during the development process, and how did you notice these shifting needs impacting your technical plan?

What did you learn from the activity?

• What technical skills related to visualisation libraries, data manipulation for plotting, or user interface design did you develop or improve?
• Were there any unexpected challenges in representing data that was not derived from a statistical test, and what did you learn about presenting raw trends or distributions effectively?
• How did your reflection-in-action influence the outcome—for example, did you adjust plot parameters like scales, colours, or labels in real-time to improve clarity for the stakeholder?
• How does this experience of summarising results for stakeholders relate to your future requirements for post-programme practice, such as supporting clinical decision-making?
• Did you gain a better understanding of how the modularity of bioinformatics processes (storing, then analysing, then visualising) affects the efficiency of your workflow?

What will you take from the experience moving forward?

• What areas for continued development have you identified, such as a need to master more complex plotting libraries or refine your stakeholder engagement techniques?
• How can you apply the learning from this activity—specifically the ability to programmatically derive insights—to your routine clinical practice or the development of other bioinformatics tools?
• What next steps will you take to support the assimilation of this learning, such as discussing your visualisation choices with your Training Officer or seeking further feedback from the end user?
• What transferable skills, such as technical communication or data integrity management, have you developed that will be valuable in your future role as a Clinical Bioinformatician?
• What additional support or resources might you need to further develop your expertise in clinical data science?
• Are there specific visualisation techniques, libraries, or design principles you want to explore further?

Beyond action

Have you revisited the experiences?

• How have you evaluated and re-evaluated your experience of planning and implementing this data visualisation in light of subsequent learning and feedback from end users?
• How does your experience with this training activity compare to later training activities involving more complex datasets or different types of stakeholder communication?
• Have you revisited your initial reflections for this task as part of a broader review of the S-BG-S3 module to identify how your understanding of data storage and programmatic analysis has evolved?
• Have you engaged in professional storytelling with peers or colleagues regarding how you approached visualising raw data for a non-technical audience, and has their perspective changed your view of your original design?
• How does your performance in this task compare with the observable behaviours you demonstrated during assessments, such as plotting the results of a statistical data analysis or presenting results to clinicians?
• Have you encountered different visualisation approaches in published literature, clinical reports, or tools used by colleagues? What design principles have you assimilated into your own practice?

How have these experiences impacted upon your current practice?

• How have you applied the skills gained in arranging and storing data programmatically to other clinical or research projects?
• In what ways has your ability to summarise results for stakeholders through effective visualisation improved your ability to support clinical decision-making in your routine work?
• How has this experience shaped your current approach to programmatic data analysis, and have you noticed an improvement in the efficiency of your data handling?
• In what ways did this specific activity support your preparation for module assessments like Direct Observations of Practical Skills (DOPS) or Observed Clinical Events (OCEs) involving data presentation?
• How do you now integrate the principles of user-centred design when creating bioinformatics tools or pipelines for your department?

How might these experiences contribute towards your future practice?

• What transferable skills have you developed—such as technical communication, logical problem-solving, or an understanding of data storage modularity—that will be valuable in your future role as a Clinical Bioinformatician?
• Based on your experience with this training activity, what clear actions have you identified for the continued development of your skills in emerging data science methods or advanced visualisation libraries?
• How will your proficiency in programmatically deriving insights from data that isn’t statistically derived contribute to your ability to develop new bioinformatics services or research resources?
• How has this activity influenced your perspective on your scope of practice, particularly when determining the most effective way to communicate complex genomic findings to diverse stakeholders?
• How will your foundation in data modelling and interrogation enable you to tackle more advanced challenges in Clinical Bioinformatics as you progress in your career?
• Consider how the principles of clear, accurate, and accessible visualisation extend beyond software development to reporting, presentations, and other aspects of professional communication in clinical bioinformatics.

Relevant learning outcomes

#	Outcome
# 1	Outcome Arrange and store data for programmatic analysis.
# 2	Outcome Perform programmatic data analysis.
# 4	Outcome Summarise results of data analysis to stakeholders.