From Classroom Calm to Data Storm: The Unvarnished Journey to become a Data Analyst at i-spark

9 November 2023 | 2 minutes of reading time

As a Data Science and Society student at RijksUniversiteit Groningen (RUG) , complemented by years of steering my own entrepreneurial ventures and a solid grounding in business management studies, I envisioned a seamless integration into my role at i-spark as a data analyst. However, I soon recognized that the professional data landscape at i-spark was far more complex and unforgiving than the nurturing halls of academia. This reality was a stark reminder that the path to becoming a qualified data analyst at i-spark is indeed a long and demanding one, filled with real-world challenges that go beyond the classroom's calm to the industry's data storm.

Development v.s. Production mode

In the academic sphere, data science is often a sequence of well-structured exercises akin to those in a coding bootcamp, with each dataset curated and each objective clearly defined. This environment is crucial for instilling a solid base of theoretical knowledge, which acts as the scaffolding for scientific exploration. However, upon engaging with real projects at i-spark, I quickly learned that this theoretical knowledge is merely the starting point. It became evident that a cross-disciplinary skill set is also essential, blending technical acumen with business savvy, communication, and problem-solving abilities. In the professional field, the transition from the 'development' mode of academia to the 'production' mode of industry is striking. As a data analyst at i-spark, you're immersed in a dynamic environment where raw data and complex problems demand not only theoretical understanding but also the practical application of a diverse skill set to unearth insights that drive business value.

Criteria for data analysis

At school, a range of grades might indicate satisfactory progress, but at i-spark, the criteria for data analysis are straightforward: it's either a pass or a fail. Any work that doesn't directly contribute to resolving a client's query or aid in their strategic decision-making and operational enhancements is returned for further refinement. The output must undergo multiple quality checks, ensuring that each analysis is up to the most top standards of quality and relevance.

Weathering any data storm

While the path forward is challenging, it is the very essence of our transition 'From Classroom Calm to Data Storm.' For those of you on the same career path, let my experiences inspire rather than dishearten you. The demanding academic route and the strict criteria of the professional world are not just obstacles but affirmations of growth. As you navigate this journey, each milestone reached should be celebrated as a clear indication of your dedication and a reflection of the resilient analyst you are becoming, capable of weathering any storm in the data-driven landscape that lies ahead.   By Ghao, Data Analyst at i-spark and student at RUG