[Data Analysis] Presenting findings (9/9)

6 min readAug 2, 2024

A guide for beginner Data Analysts on presenting analysis results. Covers organising findings, creating visualisations with Python, structuring a presentation, crafting a narrative, and preparing for questions

[This guide is part 9 of an 9-article walkthrough.]

Key concepts:

Visual Studio Code · Python · Jupyter Notebook · Data analysis · Data cleaning · Data portfolio piece · Presentation

Welcome to the final part of the 9-part series! You’ve cleaned your Iris dataset, performed your analysis, and now it’s time to showcase your findings. In this guide, we’ll walk through creating a compelling presentation of your Iris dataset analysis results. Let’s dive in!

To remind ourselves where in the process data cleaning comes into play, here is a general outline of the data analysis process:

Define Objectives: Clearly understand the goals of your analysis.
Data Acquisition: Obtain the dataset you’ll be working with.
Data Exploration: Explore the dataset to get an initial understanding of its structure and content.
Data Cleaning: Preprocess the data to ensure its quality and consistency.
Data Visualization: Create visualizations to gain insights into the data.
Feature Engineering: Create new features or transform existing ones to enhance the dataset’s predictive power.
Statistical Analysis (if applicable): Conduct statistical tests or analyses to answer specific questions or hypotheses.
Machine Learning (if applicable): Split the data into training and testing sets, select an appropriate algorithm & train and evaluate the model’s performance using metrics like accuracy, precision, recall, or F1-score.
➡️ Present solution: Interpret the findings in the context of your objectives. Document your analysis process and create a report or presentation summarising your analysis.

In this guide, we will be looking at step 9: presentation of results.

A presentation of findings from the Iris Dataset

Step 1: Organize Your Findings

Before we jump into creating slides, let’s organize our key findings. Open your Jupyter notebook in VS Code and run through your analysis.

Note down:

The main objective of your analysis
Key statistics about the dataset
Any visualisations you created
Insights or patterns you discovered
Conclusions drawn from your analysis

If you’ve been following this 9-article walkthrough, you should already have the above prepared.

Step 2: Choose Your Presentation Tool

For this guide, we have used Matplotlib & Seaborn to create our visualisations within VS Code. We will then use Canva to create our presentation, where we will upload all of our visualisations.

❗️Note: You can use any presentation software you’re comfortable with to assemble your slides (PowerPoint, Google Slides, Keynote, etc.).

Step 3: Visualization Best Practices

Creating effective visualizations is crucial for communicating your findings clearly. Here are some best practices to keep in mind when designing your charts:

Choose the Right Chart Type

Use bar charts for comparing categories
Use line charts for showing trends over time
Use scatter plots for showing relationships between two variables
Use pie charts sparingly, and only for showing parts of a whole

An example scatter plot to show relationships between variables

Keep It Simple

Avoid cluttering your charts with unnecessary elements
Use clear, easy-to-read fonts
Remove gridlines if they’re not essential

An example histogram with the gridlines removed

Use Color Effectively

Choose a consistent color palette throughout your presentation
Use color to highlight important data points or categories
Ensure sufficient contrast between colors for readability
Consider colourblind friendly palettes

# Example: Using a colourblind friendly palette
sns.set_palette(“colorblind”)

Label Clearly

Always include a clear title that explains what the chart shows
Label axes clearly and include units where applicable
Use legends when necessary, but try to label data directly if possible

# Create figure and set size
plt.figure(figsize=(10, 6))

# Plot data
sns.boxplot(x=’species’, y=’sepal_length’, data=df)
plt.title(‘Distribution of Sepal Length by Species’)

# Set labels
plt.xlabel(‘Species’)
plt.ylabel(‘Sepal Length (cm)’)

# Display datapyth
plt.show()

An example chart with labels and title clearly added.

Scale Appropriately

Start y-axes at zero for bar charts to avoid misrepresentation
Use consistent scales when comparing multiple charts
Consider using logarithmic scales for data with large ranges

An example chart where the axis starts at 0

Highlight the Data

Minimise chart junk (unnecessary decorative elements)
Use data-ink ratio principle: maximize the ink used to present data

Order Data Logically

Order categories in bar charts by value for easier comparison
In time series, always order data chronologically

Provide Context

Include a brief explanation or key takeaway with each chart
Relate the visualization back to your analysis objectives

An example of context being added to a bar chart visualisation

Remember, the goal of data visualisation is to communicate your findings clearly and effectively. By following these best practices, you’ll create charts that are not only visually appealing but also informative and easy to understand.

Now, let’s move on to creating the presentation structure

Step 4: Create Your Presentation Structure

Now that we have our visualizations, let’s structure our presentation:

Title Slide: Include the dataset name, your name, and the date.
Table of Contents: Show the audience what you plan to cover.
Project Overview: Briefly explain the Iris dataset and your analysis objectives.
Dataset Overview: Present key statistics about the dataset.
Key Findings: List the main insights you discovered. Include the visualizations you created, with brief explanations.
Conclusion: Summarise your findings and their implications.
Next Steps: Depending on the nature of your analysis, suggest potential further analyses or applications.
References: If you have referred to any articles, books or authors whilst making your presentation, make sure to mention them here.

An example table of contents for your analysis

Step 5: Craft Your Narrative

As you assemble your slides, focus on telling a story with your data. Here’s an example of how you might structure your narrative:

“The Iris dataset contains measurements of 150 iris flowers from three species.”
“Our objective was to identify distinguishing features between these species.”
“Looking at the pairplot, we can see clear separation between species, particularly when considering petal length and width.”
“The box plot shows that sepal length varies significantly between species, with Iris Setosa having the shortest sepals.”
“The violin plot reveals that petal width is a strong differentiator, with Iris Setosa having notably narrow petals.”

Step 6: Practice Your Presentation

Once your slides are ready, practice delivering your presentation. Focus on:

Clearly explaining each visualization
Highlighting key insights
Connecting your findings back to your initial objectives
Speaking confidently and at a steady pace

Step 7: Prepare for Questions

Anticipate potential questions your audience might ask. Some examples could be:

“How did you handle any outliers in the dataset?”
“What machine learning models could be applied to this data?”
“How might these findings be applied in botany or horticulture?”

Having thoughtful answers prepared will demonstrate your thorough understanding of the analysis.

**The final result:** a presentation of findings from the Iris Dataset

Conclusion:

Congratulations! 🎉 Creating an effective presentation is a crucial skill for any Data Analyst. By following these steps, you’ll be able to showcase your analysis in a clear, engaging, and professional manner. Remember, practice makes perfect, so don’t be afraid to refine your presentation skills over time.

Happy presenting, and may your data always tell a compelling story!

Reference material

Canva presentation for the Iris Dataset Analysis [link]