Text Mining

• Apply text mining techniques to extract common themes, keywords, and phrases from grant descriptions and other textual fields. Tools like TF-IDF (Term Frequency-Inverse Document Frequency) or topic modeling (e.g., LDA - Latent Dirichlet Allocation) can be helpful here.
• Use Named Entity Recognition (NER) to identify specific entities such as organizations, locations, or dates within text fields.

Data Quality Assessment:

• Assess the completeness, accuracy, and consistency of the data across listings.
• Identify missing data, outliers, or anomalies that may require special handling or cleaning.

Tools and Techniques:

• Python libraries such as Pandas for data manipulation, NLTK or spaCy for natural language processing, and Scikit-learn for text mining.
• Excel or Google Sheets for initial data inventory and analysis.

Data Quality Assessment:

• Assess the completeness, accuracy, and consistency of the data across listings.
• Identify missing data, outliers, or anomalies that may require special handling or cleaning.

Identify Variability in Naming Conventions:

• Document different naming conventions and categorization systems used across datasets to prepare for synonym mapping and data normalization.