About the project.
Python is a top choice for data science due to its simplicity, readability, and vast ecosystem of powerful libraries. Tools like Pandas, NumPy, and scikit-learn make data manipulation, analysis, and machine learning accessible and efficient. Combined with visualization libraries like Matplotlib and Seaborn, and integration with big data platforms and deep learning frameworks, Python offers a flexible, end-to-end solution for data science tasks. Its large community also ensures strong support and continual development.
See the marketing mix report and explore the different visualizations to understand how Python brings data-driven insights to life.
Project Type
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MarTech & Demand Generation
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AI
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Business Analytics
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Data Science & Engineering
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Machine Learning
Tech Stack / Toolbox
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Python
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NumPy
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Matplotlib
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Plotly
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Seaborn
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Pandas
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A/B Testing
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scikit-learn
My Role
Data Scientist & Marketing Analyst
In this project, I:
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Collected and cleaned marketing campaign data from multiple sources (CRM exports, ad platform data, web analytics).
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Applied Python (pandas, NumPy, scikit-learn, matplotlib, seaborn, Plotly) to explore campaign performance and customer behavior.
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Built segmentation models and cohort analyses to identify high-value audiences and optimize spend allocation.
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Performed A/B testing analysis to measure the impact of creative, channel, and targeting strategies.
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Developed interactive dashboards and visual reports to communicate findings and recommendations.
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Automated recurring analysis workflows for scalability and repeatability.
