Decoding Stock Performance: Analysis of Market Trends and Patterns

As part of the IBM Data Science Specialization, I undertook a comprehensive project for a startup investment firm, assuming the role of a Data Scientist. The project focused on extracting essential financial data from diverse sources, employing Python libraries and web scraping techniques, with a specific emphasis on popular stocks such as Tesla and GameStop.

My responsibilities included the extraction and processing of historical share prices and quarterly revenue reports for the identified stocks. Leveraging Python libraries such as Pandas, NumPy, and BeautifulSoup for web scraping, I ensured the accurate and systematic compilation of data, laying the foundation for robust analytical insights.

This project underscores my proficiency in data extraction, cleaning, and analysis, demonstrating my ability to contribute substantively to real-world challenges. The financial data collected provides a valuable resource for the investment firm, aiding in the formulation of informed and strategic decisions.

Throughout the project, I applied the principles learned in the IBM Data Science Specialization to manipulate and analyze data effectively. The focus on practical applications of data science methodologies exemplifies my commitment to translating theoretical knowledge into tangible, value-driven outcomes for business objectives.

By showcasing visualizations that highlight trends, patterns, and key metrics within the financial data, this project emphasizes my ability to communicate complex information in a digestible manner.

The financial data utilized in this analysis was sourced from Revenue Data for Tesla and Stock Data for GameStop.

Skills & Tools

Exploratory Data Analysis, Data Visualization, Web scraping, Statistics, Python, Pandas, NumPy, BeautifulSoup