Introduction

Programming is a fundamental foundation in the field of data science, as it enables data analysts to process, visualize, and extract meaningful information from various types of data. In this context, programming is not only a tool for writing code, but also a means to understand how analytical processes work from reading and cleaning data to building predictive models that generate valuable insights.

This skill is essential, especially as technological advancements require data practitioners to work efficiently with large datasets and apply programming logic throughout every analytical step. By understanding the basics of programming, students learn not only syntax, but also how to think systematically and solve data-related problems in a structured manner.

I would also like to express my sincere gratitude to Mister Bakti Siregar, M.Sc., CDS., the lecturer of Data Science Programming. His guidance, explanations, and dedication have helped me understand these fundamental programming concepts more clearly, coherently, and applicatively. I hope the knowledge he has provided will be beneficial for my academic journey and future development in the field of data science.

1. What is the main purpose of our study?

The main purpose of this study is to help us understand the essential programming foundations needed in data science. Through this material, we learn how a program works, how to process data using code, and how programming logic can help us solve data related problems. In short, the goal is not just to teach us how to write code, but to help us understand the entire analytical process from start to finish in a more structured way.

2. Why do we learn about it?

We learn programming because it plays a crucial role when working with data. Through programming, we can clean, process, and analyze data much faster and more efficiently. It also trains us to think logically and work in a more structured way, which is very important for solving data related problems. So overall, we study it to better prepare ourselves for various challenges in data science, whether in class assignments or real world projects.

3. What tools to have to expert about?

There are several tools we need to master if we really want to understand the world of data science. The most important ones are programming languages, especially R and Python, because they are used in almost every part of data processing from reading and cleaning data to creating visualizations and performing analyses.

Besides the languages themselves, we also need to get used to using supporting tools like R Studio or Jupyter Notebook. These tools help us write code more neatly and comfortably. We also need to be familiar with various libraries or packages that are commonly used, because these packages make our work much faster and easier.

In short, to be able to run data analysis from start to finish, we need to master the programming languages, the applications, and the packages that come with them.

4. Give me your interest domain knowledge?

I’m quite interested in domain knowledge related to the business and corporate landscape in Indonesia. Many important decisions in companies today heavily rely on data ranging from analyzing consumer behavior, monitoring sales performance, to determining the most effective marketing strategies. By understanding how businesses operate in Indonesia, I can better grasp the local context: such as shopping patterns, market trends, and the competitive challenges between companies.

This knowledge also makes it easier for me to connect data analysis with real world needs. For example, how data can help a retail company identify which products are selling well, or how an e-commerce company can predict demand. So, in my view, understanding the business world in Indonesia is crucial for making data analysis more relevant and valuable for decision-making in companies.

Reference

https://bookdown.org/dsciencelabs/data_science_programming/00-Introduction-to-Programming.html