⏱️ Introduction: Making Sense of Time
Time is one of the most valuable — and least understood — resources.
This notebook focuses on Timesheet Analysis, using real tracking data to understand how time is actually spent across projects, tasks, and activities. Instead of relying on gut feel, it uses data to surface patterns and insights.
This is analytics applied to everyday work.
🎯 Purpose: Turning Logs into Insights
The goal of this notebook is to demonstrate how to:
- Load timesheet and project tracking data
- Clean and normalise real-world records
- Analyse time allocation across activities
- Identify patterns, bottlenecks, and focus areas
- Prepare data for reporting or dashboards
This is the kind of analysis used in productivity tools and internal reporting systems.
🧠 How It Works: From Raw Logs to Analysis
At a high level, the workflow looks like this:
- Import timesheet and project tracker data (CSV / Excel)
- Inspect and clean the dataset
- Standardise dates, durations, and categories
- Aggregate time spent by project or activity
- Produce summaries suitable for insights or visualisation
The notebook treats time as data, not just records.
🧩 The Technical Part: Processing Timesheet Data
A simplified example of the processing logic looks like this:
import pandas as pd
df = pd.read_csv("project_pulse_tracker.csv")
df.groupby("project")["duration_hours"].sum()
Across the notebook, techniques such as the following are applied:
- 📂 Reading CSV and Excel files
- 🧼 Cleaning inconsistent entries
- 📐 Grouping and aggregating time data
- 🔁 Comparing across projects or categories
- 📊 Preparing structured outputs
This mirrors real analytics workflows used in operations and planning.
💡 Key Takeaways: What the Data Reveals
This notebook reinforces several important ideas:
- ⏱️ Time tracking data is only useful when analysed
- 📊 Aggregation reveals hidden patterns
- 🧠 Assumptions about productivity are often wrong
- 🛠 Simple analysis can drive better decisions
Even basic summaries can dramatically improve awareness and planning.
🏁 Conclusion: Data-Driven Productivity
The Timesheet Analysis notebook shows how everyday data can be transformed into meaningful insight:
Don’t just track time — understand it.
With this foundation, the analysis can naturally evolve into:
- Visual dashboards
- Cost and rate modelling
- Capacity planning
- Automated reports
This notebook fits perfectly into a data-driven project management narrative.
🔗 Link to Notebook
Notebook link: Coming Soon