SQL & Tableau applied in a Data Science Bootcamp
This project focuses on analyzing and visualizing data from Magist, specifically targeting tech products. The main objectives are to evaluate customer satisfaction, delivery times, and revenue trends. By analyzing this data, i aim to provide insights on customer experience and optimize business strategies
- SQL: For querying and extracting relevant data from the dataset.
- Tableau: Used for creating detailed and interactive visualizations to present the findings.
- Data Analysis and Visualization: Improved skills in extracting and analyzing large datasets. Gained experience in using Tableau for creating informative and interactive visualizations.
- Customer Insights: Learned how to interpret customer reviews and satisfaction scores to derive business insights.
- Delivery Performance Analysis: Developed methods to evaluate and visualize delivery times, identifying patterns and areas for improvement.
- Communication Skills: Practiced explaining technical findings to non-technical stakeholders through clear and concise visualizations and summaries.
- Data Cleaning: Faced challenges with inconsistent and incomplete data.
- Complex Queries: Writing complex SQL queries to extract relevant data was challenging but manage to get through it.
- Visualization Clarity: Ensuring that visualizations were clear and effectively communicated insights was initially difficult. Improved this by refining visualizations based on feedbacks.
- Importance of Data Quality: This project highlighted the importance of data quality and the need for thorough data cleaning and validation processes.
- Cross-functional Collaboration: The project emphasized the value of collaborating with non-technical stakeholders to ensure that the insights derived aligned with business needs.
- Continuous Learning: Working on this project reinforced the importance of continuous learning and adapting to new tools and techniques in the field of data science.
- Collaborative Work: Working as a team was enjoyable and productive. It allowed us to leverage each other's strengths, share task, and making sure we deliver a good analysis.