If you’re diving into data science with Python, having the right tools in your toolkit is essential. Python’s ecosystem offers a wide range of libraries that simplify data manipulation, visualization, and machine learning. Here’s a quick rundown of five must-have Python libraries for any data scientist:

1. Pandas
   Pandas is the go-to library for data manipulation and analysis. It offers powerful data structures like DataFrames, making it easy to clean, process, and visualize datasets. When first learning how to use Python for data analysis, understanding how to use Pandas DataFrames and Series is essential.
2. NumPy
   NumPy provides support for large multi-dimensional arrays and matrices. It’s highly efficient for numerical computations, and many other data science libraries (like Pandas) are built on top of it. Many data science and machine learning tasks require the use of numpy arrays, so understanding how to convert and manipulate these arrays is vital.
3. Matplotlib
   For visualizing your data, Matplotlib is a versatile plotting library. It allows you to create static, animated, and interactive visualizations in Python. This library is easy to learn basic plotting with its built-in functions for graphs like line plots, scatter plots, bar charts and histograms. Once you have the basics down to create the plots, pairing it with libraries like seaborn can help customize your plots to help make them more visually appealing, as well as creating more advanced plots such as pair plots and heatmaps.
4. Seaborn
   Built on top of Matplotlib, Seaborn makes it easier to create attractive and informative statistical graphics. It’s perfect for heatmaps, pair plots, and other visually appealing charts.
5. Scikit-learn
   When learning python, you will eventually get to the point when you need to create advanced models. Scikit-learn is the leading library for machine learning in Python. It includes tools for classification, regression, clustering, and more, making it simple to implement and evaluate machine learning models.

With these five libraries, you can go from data collection to insights, covering almost the entire data science workflow.

I recommend picking a simple dataset from a site like Kaggle, or opening your favorite data analysis or statistics textbook and get to work.