Best Udemy Data Analysis Courses for Scraped Data 2026
The best Udemy data analysis courses for scraped data in 2026 teach pandas, visualization, and SQL. Top picks: Data Analysis with Pandas and Python (Boris Paskhaver), Python for Data Science and Machine Learning Bootcamp (Jose Portilla, 4.6★), and The Ultimate Pandas Bootcamp. They bridge raw scraped output into cleaned, analyzed, and visual insights. Apify Datasets export to CSV/JSON—pandas ingests these directly.
Browse Data Analysis courses on Udemy
Top Udemy Data Analysis Courses for Scraped Data
| Course | Instructor | Rating | Focus |
|---|---|---|---|
| Data Analysis with Pandas and Python | Boris Paskhaver | 4.6★ | pandas, cleaning, aggregation |
| The Ultimate Pandas Bootcamp: Advanced Python Data Analysis | Andy Bek | — | Advanced pandas |
| Python for Data Science and Machine Learning Bootcamp | Jose Portilla | 4.6★ | NumPy, pandas, ML |
| 2024 Python Data Analysis & Visualization Masterclass | Colt Steele | — | Analysis + visualization |
| Learning Python for Data Analysis and Visualization | Jose Portilla | 4.4★ (20K) | pandas, visualization |
Data Analysis with Pandas and Python
Boris Paskhaver. Covers data loading, cleaning, aggregation, filtering, and basic visualization. Directly applicable to scraped CSV/JSON from Apify or custom scrapers.
Python for Data Science and Machine Learning Bootcamp
Jose Portilla. NumPy, pandas, Scikit-learn, visualization. Best when you want to go beyond descriptive stats into ML on scraped data.
The Ultimate Pandas Bootcamp
Advanced pandas: multi-indexing, groupby, merging, time series. For analysts who already scrape and need deeper transformation skills.
Scraped data → analysis workflow
- Collect: Scraper or Apify Actor → CSV/JSON
- Load:
pd.read_csv()orpd.read_json() - Clean: Drop duplicates, handle nulls, normalize types
- Transform: Filter, aggregate, merge
- Visualize: Matplotlib, Seaborn, Plotly
- Store: PostgreSQL, Excel, or downstream APIs
SQL for scraped data
Many scraped datasets benefit from SQL: deduplication, joins, aggregations. Add a SQL course (e.g., Jose Portilla's SQL for Data Science) when you push data to PostgreSQL or a data warehouse.
Free alternatives
pandas docs, Real Python's pandas tutorials, YouTube walkthroughs. Kaggle datasets mimic scraped structure for practice.
Start with Data Analysis with Pandas and Python. Add Python for Data Science if you want ML. Browse Udemy →
Load CSV/JSON into pandas, clean (nulls, duplicates, types), aggregate, and visualize. Add SQL for larger or multi-source datasets.
pandas (Python), Jupyter, and a visualization library (Matplotlib, Seaborn). SQL for structured storage.
Data Analysis with Pandas and Python (Boris Paskhaver) or Python for Data Science (Jose Portilla).