Project by | ****Ulya Ganeswara Alamy

SIRCLO Solutions is an innovative Indonesian tech company offering e-commerce solutions. As a newly hired Data Analyst at SIRCLO Solutions, I was given my very first assignment: to classify a set of raw data into the correct types—numerical, categorical, and ordinal.

Introduction

Task Overview

<aside> <img src="/icons/briefcase_orange.svg" alt="/icons/briefcase_orange.svg" width="40px" />

As a new member of the team, it’s essential to establish a solid understanding of the data landscape at SIRCLO Indonesia. The core task is to examine different data sources and categorize them into numerical, categorical, or ordinal data—this will pave the way for effective analysis, insight generation, and business decision-making.

</aside>

<aside> <img src="/icons/binoculars_orange.svg" alt="/icons/binoculars_orange.svg" width="40px" />

In this project, my responsibilities included:

• Reviewing the dataset thoroughly.
• Identifying whether each data point was numerical, categorical, or ordinal.
• Explaining my classification rationale.
• Organizing results into clear tables and lists.

This methodical approach demonstrates how I tackle data challenges—breaking complex problems into manageable steps and creating reusable solutions for the team.

</aside>

Dataset Overview

Findings (Classifications)

<aside> <img src="/icons/description_orange.svg" alt="/icons/description_orange.svg" width="40px" />

Numerical Data

</aside>

• Quantitative values measured in units (age, height, distance, duration).

<aside> <img src="/icons/description_orange.svg" alt="/icons/description_orange.svg" width="40px" />

Categorical Data

</aside>

• Qualitative labels with no inherent order.

<aside> <img src="/icons/description_orange.svg" alt="/icons/description_orange.svg" width="40px" />

Ordinal Data

</aside>

• Values with a logical order or ranking (education levels, satisfaction, difficulty).

Discussion

Recommendations

<aside> <img src="/icons/compose_orange.svg" alt="/icons/compose_orange.svg" width="40px" />

Conclusion

This case study may look simple, but it reflects one of the most important lessons I learned as a new Data Analyst: every analysis starts with clean and well-defined data. By carefully identifying and classifying each data type, I built a solid foundation for deeper analytics work. This experience reinforced my belief that attention to detail, even at the smallest level, can make all the difference in achieving reliable insights.

</aside>