Python for Data Science
Python's simplicity and robust ecosystem make it a fundamental tool for data science. The chapter covers basic programming concepts, essential libraries for data manipulation and visualization, and the setup of a Python environment using Jupyter Notebook.
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Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Python is essential for data science due to its simplicity and power.
- Key data types include strings, numbers, lists, and booleans.
- Python structures like loops, functions, and libraries enhance data tasks.
- Core libraries: NumPy (math), Pandas (data), Matplotlib (visualization).
- Jupyter Notebooks are preferred for exploration and documentation.
Key Concepts
- -- Python
- A widely used programming language that is easy to learn and powerful for data science.
- -- NumPy
- A library used for numerical operations and handling arrays in Python.
- -- Pandas
- A library for data manipulation and analysis with DataFrames.
- -- Matplotlib
- A library used for data visualization, enabling the creation of graphs and charts.
- -- Jupyter Notebook
- An interactive coding environment that allows users to write and execute Python code in a document format.
Additional Learning Materials
Supplementary resources to enhance your learning experience.