What is the main purpose of our study Data Science Programming?

The main goal of learning data science programming is to understand how data is processed and analyzed using programming languages. In this course, we learn not only how to write code, but also how to read data, correct it if it’s messy, and process it to make it easier to understand.

Real-world data is usually in the form of numbers or raw information that isn’t immediately understandable. Therefore, we need specific techniques and tools to transform this data into information that helps identify patterns, understand problems, and support decision-making.

Why do we learn about it?

We learn data science programming because data is now very important in many areas, including business, technology, healthcare, and education. Nearly every action we take, particularly those involving digital activities, creates a large volume of data. However, this data cannot be used as is and needs to undergo processing and analysis. Therefore, the skill of using programming languages to handle and examine data is in high demand. Through this process, data that was initially just numbers or raw information can be changed into more understandable and meaningful information.

A clear example of this is the recommendation feature found in shopping applications or social media platforms. The content or products that show up are typically related to things we have looked up or seen before. This happens because the system uses and examines user data. It is evident that data science is not merely theoretical but is also applied in many areas of daily life.

What tools to have to expert about?

To become proficient in data science programming, we need to master various tools. The most important is a programming language, one of which is Python, as it is frequently used in data science. Furthermore, we must understand libraries like Pandas and NumPy, which are useful for processing data and performing numerical operations. Knowledge of databases and query languages like SQL is also crucial for organizing and retrieving data from various sources. Tools like Jupyter Notebook and Google Colab are popular choices for data exploration and analysis. Mastering these tools allows us to work with data more effectively and in a structured manner.

Give me your interest domain knowledge data science?

My interest in data science is in the field of business and digital platforms. I am interested in how data can be used to understand customer behavior and improve decision-making. For example, companies can analyze purchasing patterns to determine which products are popular and which strategies are effective.

I find it interesting how data can help businesses create better services and more personalized experiences for users. Through data analysis, companies can make decisions based on evidence rather than assumptions. That is why I am interested in applying data science in the business field.