The Samsung Gear Portal Data Extraction and Analytics project is designed to showcase data engineering and analytics skills through a multi-step process involving data mining, migration, transformation, and visualization. The project demonstrates proficiency in working with web data, databases, SQL queries, and visualization techniques using JavaScript, Python, MySQL, and Matplotlib.
-
Data Mining with JavaScript: The first code file utilizes JavaScript as a runtime environment in a web browser to perform data mining on a web page. It leverages JavaScript's DOM manipulation capabilities to extract specific data from the Samsung Gear Portal web page. The extracted data is then exported to a CSV file, providing a structured format for further analysis. This approach allows for flexible access to web data and conversion into a usable format.
-
Data Migration and Transformation with Python and MySQL: The second code file focuses on data migration and transformation using Python and MySQL. It reads data from a CSV file obtained through data mining and performs the necessary transformations. The code establishes a connection to a MySQL database, creates tables to store the extracted data, and inserts the transformed data into the respective tables. It handles data validation, table creation, and enforces foreign key constraints to ensure data integrity. This code showcases proficiency in working with databases and performing ETL (Extract, Transform, Load) operations.
-
Data Visualisation and Analysis with SQL and Matplotlib: The third code file demonstrates the power of SQL queries and data visualisation techniques for data analysis. It utilises SQL queries to retrieve data from the MySQL database, calculate specific insights, and extract relevant information for analysis. The extracted data is then visualised using Matplotlib. The code generates an interactive visualisations to present churn analysis.
- Web Data Extraction: The project leverages JavaScript to extract data from the Samsung Gear Portal web page, allowing access to specific information not easily available through traditional APIs or data sources.
- Data Migration and Transformation: Python and MySQL are used to migrate and transform the extracted data from the CSV file into a structured database format. This process ensures data consistency, integrity, and enables further analysis.
- Efficient Data Analysis: SQL queries are employed to retrieve and analyze data from the MySQL database, allowing for efficient data exploration, aggregation, and calculation of key metrics or insights.
- Insightful Data Visualization: Matplotlib is utilised to create visually appealing and informative charts, graphs, and histograms. These visualizations enhance data understanding and facilitate effective communication of findings to stakeholders.
- Demonstration of Data Engineering and Analytics Skills: The project showcases a range of skills including web scraping, data migration and transformation, SQL query optimization, and data visualization techniques. These skills are highly valuable in data engineering and analytics roles.
-
Run web data extraction: Execute the JavaScript file
web_data_extraction.jsto perform data mining on a web page and export the data to a CSV file. This step collects the necessary data for further processing and analysis. -
Run data migration: Execute the Python file
data_migration.pyto migrate and transform the extracted data into a MySQL database. This step involves creating and populating two tables in the database, ensuring data integrity and handling any necessary data validation. -
Run data visualization: Execute the Python file
data_visualization.pyto perform data analytics and visualization on the migrated data. This step involves querying the MySQL database to retrieve the required data and using Matplotlib to create visualizations such as bar charts or histograms.
The Samsung Gear Portal Data Extraction and Analytics project demonstrates the ability to extract, migrate, transform, analyze, and visualize data from the Samsung Gear Portal using a combination of JavaScript, Python, MySQL, and Matplotlib. By leveraging these technologies, the project showcases essential data engineering and analytics skills required for roles in the industry. It provides a strong foundation for working with real-world data, deriving meaningful insights, and effectively communicating findings to stakeholders.
Please note that the code provided in this project is intended for demonstration and learning purposes only. Users are responsible for complying with relevant laws, regulations, and terms of service when extracting data from websites or any other sources. The code author and contributors do not assume any liability for misuse or violation of legal obligations arising from the use of this code.
It is recommended to obtain proper authorization and ensure compliance with terms of service or usage agreements before extracting data from any website or system.