Internship← Data Science learning pathData Science · Module 2 of 10
Python for data science
Use Python and Jupyter efficiently.
📖 Key ideas
- Core Python: lists, dicts, functions, comprehensions.
- NumPy for fast numerical arrays.
- pandas for tables (DataFrames).
- Matplotlib/Seaborn for charts.
- Use virtual environments and requirements files.
💡 Real-life example: Python libraries are a well-equipped kitchen: NumPy is the knife, pandas the chopping board, Matplotlib the plating.
NumPyNumerical array library
pandasDataFrame library
MatplotlibPlotting library
List comprehensionCompact list creation