Doing Data Science with Python 2
About this course
This course walks through a complete data science project lifecycle: setting up a working environment (Anaconda, Jupyter, Git), extracting data from databases/APIs/web scraping, exploring and processing data (statistics, missing values, outliers, feature engineering), and building, evaluating, and deploying predictive models — closing with model persistence and exposing the trained model as a Flask API endpoint.
The honest take: at 309 reviews and 4.4 stars, this is a solidly reviewed, beginner-level course whose real differentiator is going all the way to deployment (a Flask API) rather than stopping at model training — useful for learners who want to see the full path from raw data to a usable endpoint, not just notebook-based model fitting.
What you'll learn
This course includes
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Instructor
Abhishek Kumar holds a Master's degree from UC Berkeley, is a Google Developers Expert in machine learning, and has authored 12 courses on Pluralsight.
Requirements
- Basic Python familiarity is helpful but the course covers setup from scratch
Who this course is for
- Beginners wanting a complete, end-to-end data science project walkthrough
- Learners who want to see deployment, not just model training