1. what is the main purpose of our study (data science programming)?

The main purpose is to gain the ability to extract meaningful insights from raw, unstructured data by leveraging programming languages like Python or R. Instead of just looking at raw numbers, we use programming as a powerful tool to clean, analyze, and transform that data into a structured format. This process allows us to build models and provide data-driven conclusions or predictions that are essential for making strategic decisions.

2. ⁠why do we learn about it?

We learn Data Science Programming to handle large-scale datasets efficiently, automate repetitive data processing tasks, and build predictive models. In a world where data is everywhere, being able to interpret it gives us the power to solve complex problems and provide accurate forecasts for future trends.

3. ⁠what tools to have to expert about?

To become an expert, you need to master several essential tools:

• Programming Languages: Python (most common) or R.
• Data Libraries: Pandas (for data manipulation) and NumPy (for numerical computing).
• Visualization Tools: Matplotlib, Seaborn, or Plotly to create charts.
• Databases: SQL to retrieve and manage data from storage.
• Platforms: Jupyter Notebook or Google Colab for writing and testing code.

4. ⁠give your interest domain knowledge (data science)!

My primary interest lies in Data Visualization and Dashboard Design. I believe that data is only valuable if it can be understood well by its audience. By combining Data Science and Design principles, I aim to create interactive dashboards and infographics that transform complex data into something easy to read and aesthetically pleasing. Furthermore, I hope to create data-driven application designs that are not only functional but also aligned with my personal design taste and aesthetic standards.

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