Python Functions for Data Science

О курсе

Data science in Python comes down to having the right functions ready at the right moment, not memorizing the entire language from the ground up. This course covers the tools that data analysts and data scientists rely on daily: Python's own built-in functions, NumPy, SciPy, pandas, matplotlib, and seaborn. Rather than treating each library as an abstract topic, you'll build a single, practical toolkit one skill at a time. You'll inspect, aggregate, sort, and filter data using nothing but Python's built-in functions, then move into NumPy to create and transform numeric arrays for fast, large-scale calculations. From there, you'll use SciPy to solve matrix-based problems and test whether a difference in your data is statistically meaningful. You'll bring that same data into pandas, where you'll build, clean, and reshape real tabular datasets, combining and grouping information exactly the way a real analysis project demands. Finally, you'll turn your findings into clear, presentation-ready charts with matplotlib and seaborn. By the end of this course, you'll be able to inspect, transform, analyze, and visualize real datasets using Python's core data science toolkit, ready to apply immediately to your own data.

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Madecraft

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