As a Data Analyst Intern, I was responsible for analyzing data and providing insights to help make informed decisions.
Key Features
Data Analysis: Analyzed data and provided insights to help make informed decisions.
Data Visualization: Created visualizations to help understand data trends and patterns.
Data Modeling: Developed data models and performed data analysis to identify patterns and trends.
Data Integration: Collaborated closely with designers by setting up a shared design library in Figma. This library was synchronized with the codebase, ensuring design handoffs were seamless and that design tokens remained consistent across both platforms.
Documentation and Usage Guidelines: Developed comprehensive documentation with Storybook to showcase components, usage patterns, and best practices, ensuring the design system is easy to adopt by other teams.
Technologies Used
Excel: For data analysis and visualization.
Python: For data analysis and visualization.
SnowFlake: For data analysis and visualization.
Tableau: For data analysis and visualization.
Challenges and Learnings
As this was my first time working with Python for data analysis and visualization, I faced a steep learning curve. The project required me to quickly learn new libraries and frameworks, such as Pandas and Matplotlib, and to apply them to real-world data.One of the biggest challenges I faced was dealing with the quality of the data. The data was raw and had not been cleaned or processed, which made it difficult to work with. I had to develop a pipeline using Python to clean and process the data, which was a great learning experience.Another challenge was visualizing the data in a meaningful way. I had to experiment with different visualization tools and techniques to find the best way to communicate the insights I had gained from the data.Overall, this project was a great learning experience. I learned a lot about data analysis and visualization, and I gained experience working with new technologies and tools. I also gained a deeper appreciation for the importance of data quality and the challenges of working with real-world data.
