Introduction to Text Data and Natural Language
Learn to work with text data in Python. Master tokenization, cleaning, TF-IDF, word embeddings, sentiment analysis, topic modeling, and when to use classical NLP vs. transformer models.
Working with Geospatial Data in Python
Learn to work with geospatial data in Python. Master coordinate systems, Shapely geometries, GeoPandas, spatial joins, choropleth maps, distance calculations, and common geospatial analysis patterns.
Introduction to Data Catalogs
Learn what data catalogs are and why they matter for data science. Understand metadata management, data discovery, business glossaries, ownership, and how to build a lightweight catalog in Python.
Understanding Data Provenance and Lineage
Learn what data provenance and data lineage mean for data science. Understand how to track data origins, transformations, column-level lineage, and why it matters for debugging, compliance, and trust.
Data Quality: What Makes Data Good or Bad
Learn the six dimensions of data quality: completeness, accuracy, consistency, timeliness, validity, and uniqueness. Master detection, measurement, and remediation of data quality issues in Python.
