A comprehensive analysis and visualization of my LinkedIn network using Python, Jupyter, and Plotly. This project evolved from simple network visualization to include advanced business development analytics for identifying key decision-makers and prospects.
- Tech Stack: Python, Pandas, Plotly, NetworkX, PyVis
- Data Source: LinkedIn Connections (.csv export)
- Interactive Notebook: VisualizingMyLinkedinNetwork.ipynb
- Company Distribution: Treemap showing where colleagues work
- Role Distribution: Hierarchical view of job titles across network
- Interactive Network Graph: Company relationships based on shared position types
- Decision-Maker Identification: Finds VP+ level contacts in data/engineering roles
- Tiered Prospect Segmentation:
- Tier 1: VP+ in Data/Engineering (22 prospects)
- Tier 2: Senior/Manager level in target roles (301 prospects)
- Tier 3: All data/engineering roles (1,254+ prospects)
- Interactive DataFrames: Explore prospects directly in the notebook
Open VisualizingMyLinkedinNetwork.ipynb directly in GitHub to see static renders of all analyses and datasets.
# Clone the repository
git clone https://github.com/TuckerRasbury/02_VisualizingMyLinkedinNetwork.git
cd 02_VisualizingMyLinkedinNetwork
# Install dependencies
pip install pandas plotly networkx pyvis nbformat
# Open the notebook
jupyter notebook VisualizingMyLinkedinNetwork.ipynbDiscover where your professional community is concentrated and identify key industry hubs and talent pools within your network.
Rapidly identify decision-makers and influencers in your target functional areas (Data, Engineering, Analytics). The tiered approach allows you to customize outreach strategies:
- Executive engagement for Tier 1 (VPs and above)
- Technical conversations with Tier 2 (Managers and Senior roles)
- Broad awareness campaigns for Tier 3 (all roles)
- Understand role distribution across your network
- Identify underrepresented functional areas
- Track connection trends over time (via "Connected On" dates)
- ✅ Removed unused imports and dependencies
- ✅ Extracted magic numbers into constants for maintainability
- ✅ Avoided
inplace=Truepatterns in favor of reassignment - ✅ Added
.gitignorefor generated files
- ✅ Decision-maker filtering with customizable criteria
- ✅ Tiered prospect segmentation (VP+, Senior/Manager, All roles)
- ✅ Interactive DataFrames for exploring results
- ✅ Summary statistics for each analysis tier
- ✅ Interactive treemaps showing company and role distribution
- ✅ Responsive Plotly visualizations for exploration
As a budding data analytics professional, reading official and unofficial documentation and producing accessible reports is par the course. As an intellectual exercise, I created a data visualization of my LinkedIn network using an article from Medium as my "documentation". This exercise was also an opportunity to work with an interesting dataset that branches towards #peopledata #peopleanalytics.
I visualized where colleagues in my network work. Goldman Sachs, Blend, Pomona College, and Slalom Consulting are hubs for my colleagues, but still represent a minority. Most of my network is spread out, working at a large number of companies.
To go down a layer deeper into the data, I clicked into Slalom to see the job roles of my colleagues. Hypothetically, if they were all Data Analysts like myself I would have a highly specialized set of colleagues. What's going on in the visual here is that my network is a mix of stakeholders, more senior peers, other data professionals, and in-house recruiters.
Finally, this view shifts our perspective a bit. Above, I sliced my data from the company layer downward. Below, I reorganized my data to see what job titles are most prominent. There are even fewer hubs here than in Part 1, and there is a lot of variance in job titles.
Using the "Connected On" column and a bit of reformatting using pandas, it's possible to identify when I gained the most connections throughout my time using LinkedIn. From there, I could identify the why behind that spike in connections and use that information in growing my network in a meaningful way.
- pandas: Data manipulation and analysis
- plotly: Interactive visualizations
- networkx: Network analysis
- pyvis: Interactive network graphs
- pathlib: Cross-platform file handling
- jupyter: Interactive notebook environment
- Original inspiration: "Visualizing My Linkedin Network" | Medium | Tavish Gobindram Software Engineer
- LinkedIn Data Privacy: LinkedIn Connection Data Export Guide