Many new contacts in my LinkedIn network are asking me for some resources to begin their data science journey🤓. I don't want to share a structured series, but I'll be sharing from now on some exciting resources I've used during my personal upskills journey to reach the level I have today.

Python is the best programming language out there. So I'll start with these.

I've always had two favorite steps in data:
🧹🧤🧽𝗗𝗮𝘁𝗮 𝗰𝗹𝗲𝗮𝗻𝘀𝗶𝗻𝗴
📊📉📈𝗗𝗮𝘁𝗮 𝘃𝗶𝘇

What about you? Which steps do you prefer?

The journey from raw data to insightful, compelling visualizations is pivotal in data science, transforming complex datasets into narratives that captivate and educate. Python remains at the forefront of this transformation, offering a suite of libraries that cater to diverse visualization needs, whether for academic research, business intelligence, or interactive web applications. As we step into 2024, let's explore the top Python libraries that are defining the future of data visualization.

1. Taipy: Simplified Dynamic Visualizations

Taipy stands out for its user-centric approach to data visualization, making dynamic and interactive visualizations accessible to those with minimal web development experience. While offering a straightforward path to creating rich, engaging data stories, Taipy encourages data enthusiasts to contribute and support its growth by adding a star to their GitHub repository. This gesture not only acknowledges the developers' efforts but also helps foster a community around this innovative tool.

2. Plotly:

This library takes the lead in crafting interactive, publication-quality graphs online. Plotly's strength lies in its ability to produce a wide array of plot types, including intricate 3D visualizations, geographical maps, and interactive time series. The library’s seamless integration with web technologies allows for the embedding of visualizations in web pages, enhancing the interactivity and accessibility of data insights.
Plotly Github

3. Matplotlib:

As the grandfather of Python visualization tools, Matplotlib offers unparalleled control over every element of a plot, making it ideal for creating publication-ready charts and figures. It’s the foundation upon which many other visualization libraries are built, praised for its versatility and ability to plot anything with enough commands.
Matplotlib GitHub

4. Seaborn:

Built on top of Matplotlib, Seaborn extends its functionality, making it easier to generate complex visualizations. It’s particularly well-suited for statistical analysis, providing a high-level interface for drawing attractive and informative statistical graphics. Seaborn is the go-to for anyone looking to convey data insights through elegant visualizations that go beyond basic plotting.
Seaborn GitHub

5. Bokeh:

This library excels in creating interactive plots and dashboards directly in the web browser. Its powerful interface allows for the development of sophisticated visual applications, real-time dashboards, and complex data-driven interactive plots. Bokeh is designed to appeal to users who need to quickly and easily create interactive data applications without diving deep into web development.
Bokeh GitHub

6. Gradio:

Gradio makes it incredibly simple to create interactive UIs for Python scripts, lowering the barrier for sharing machine learning models and data analyses. It shines in scenarios where visualizing model outputs, comparing different models, or demonstrating model capabilities in real-time are crucial, making AI more accessible and understandable.
Gradio GitHub

7. Streamlit:

For Pilots, use Streamlit. It empowers users to build highly interactive applications for data exploration and visualization, emphasizing speed, simplicity, and the ability to iterate quickly.
Streamlit GitHub

In 2024, these Python libraries are not just tools but gateways to understanding and interacting with data in ways that were previously unimaginable. By choosing the right library for your project, you empower yourself to unlock new insights, tell compelling data stories, and engage with your audience on a deeper level.