Python Data Programming Assessment Completion -- 2
Budget: $10 – $30 USD
Project Description: I am looking for an experienced Python developer with skills in web scraping, data analysis, and visualization to complete the remaining sections of my ICT233 End-of-Course Assessment (ECA) project. The assessment involves tasks such as data extraction using BeautifulSoup, data processing with Pandas, SMILES string analysis using RDKit, and network visualization with NetworkX. The project also requires implementing a breadth-first search algorithm.
Tasks to be Completed:
Question 1d: Analyze chemical formulas for minimum and maximum elements/atoms using Pandas and SQLite3.
Question 2a: Visualize correlations between atoms and elements and provide insights.
Question 2c: Use RDKit to convert SMILES strings into adjacency matrices.
Question 2e: Identify unique bond types from SMILES data.
Question 2f: Create a boolean column for aromatic bonds and handle missing SMILES data.
Question 2g: Visualize SMILES data as a network graph using NetworkX.
Question 2h: Implement the breadth-first search algorithm for SMILES adjacency matrices.
Requirements:
Proficiency in Python, web scraping with BeautifulSoup, and data analysis with Pandas.
Experience using RDKit for chemical data processing.
Familiarity with network visualization using NetworkX.
Ability to write clean and efficient code that adheres to the provided Jupyter Notebook template.
Please ensure all work passes the provided assertions and follows the assessment instructions. If you are confident in your skills and can deliver quality results, I look forward to collaborating with you!
Tasks to be Completed:
Question 1d: Analyze chemical formulas for minimum and maximum elements/atoms using Pandas and SQLite3.
Question 2a: Visualize correlations between atoms and elements and provide insights.
Question 2c: Use RDKit to convert SMILES strings into adjacency matrices.
Question 2e: Identify unique bond types from SMILES data.
Question 2f: Create a boolean column for aromatic bonds and handle missing SMILES data.
Question 2g: Visualize SMILES data as a network graph using NetworkX.
Question 2h: Implement the breadth-first search algorithm for SMILES adjacency matrices.
Requirements:
Proficiency in Python, web scraping with BeautifulSoup, and data analysis with Pandas.
Experience using RDKit for chemical data processing.
Familiarity with network visualization using NetworkX.
Ability to write clean and efficient code that adheres to the provided Jupyter Notebook template.
Please ensure all work passes the provided assertions and follows the assessment instructions. If you are confident in your skills and can deliver quality results, I look forward to collaborating with you!