Hello, everyone! I’m Dea Novita, the PR representative from Bukit Vista, and I’m thrilled to share our recent data science insight from our guest lecture experience with the Data Science study program of Universitas Airlangga. Following a request from Mr. Muhammad Noor Fakhruzzaman, S.Kom., M.Sc., a distinguished lecturer in Data Science, we were invited to provide insights into the practical applications of data science within our operations at Bukit Vista. Our VP of Engineering was leading the session, showcasing how data science plays a pivotal role in enhancing operational efficiency and decision-making processes within our company.
The Profile of our Data Science Insights Speaker
On a day charged with anticipation, we introduced our VP of Engineering, Vidiskiu Fortino Kurniawan, to a room full of eager minds. Vidi, who has steered the helm of engineering at Bukit Vista since 2019, brought with him not just his accolades but a deep passion for the confluence of data science and real-world application. His academic journey, from the halls of Computer Science of Universitas Gadjah Mada to his research endeavors at the Digital Intelligence Lab of the Center for Digital Society (CFDS), not to mention his mastery of Counter-Strike, painted the picture of a multifaceted individual ready to share his world.
Throughout the lecture, Vidi focused on three main topics: the comparative value of data science and software development, the initiation of data science projects, and the relevance of academic learning to the industry. His insights provided a comprehensive overview for students at a crossroads in their career paths, emphasizing the practicality of theoretical knowledge and its application in the real world.
Data Science and Software Development: An Intersection of Paths
Vidi embarked on a discussion about the dynamic landscape of both data science and software development, highlighting the surge in interest post-COVID-19. He detailed the burgeoning opportunities in data science, despite the advancements in AI, noting the essential role of data scientists in interpreting and applying AI model outputs. Vidi’s approach to data-driven decision-making at Bukit Vista—ranging from pricing strategies to customer engagement—serves as a testament to the integration of data science in enhancing business operations.
He advocated for a balanced understanding of both fields, suggesting that the choice between data science and software development hinges on individual interests and market demands. By combining insights from both domains, Vidi illustrated how Bukit Vista leverages data science for strategic advantage, underscoring the importance of a dual skill set in today’s technology landscape.
Initiating a Data Science Project: A Structured Approach
Responding to queries about starting a data science project, Vidi outlined a methodical framework, emphasizing the importance of aligning with business objectives and understanding the problem at hand. From abstract concepts to the tangibility of initiating a project, Vidi walked us through the critical steps of aligning data science projects with business goals. His methodical approach underscored the importance of not just the ‘how’ but the ‘why’ behind data-driven decisions. His advice covered the entire project lifecycle, from data collection and analysis to model evaluation and practical implementation. This structured approach, Vidi argued, is crucial for translating data science initiatives into tangible business outcomes, sharing examples from Bukit Vista’s own practices to illustrate his points.
Moreover, Vidi provided insights based on his experience and laid out a framework for managing a data science project. Here are the key points he discussed:
- Project Management Flow in Data Science: Vidi talked about the importance of understanding project management flow in the context of data science. He indicated that it’s crucial to approach a project methodically, breaking it down into stages.
- Understanding the Business Aspect: He emphasized the need to comprehend what the business actually does. Knowledge of the business helps in framing the data science problem effectively.
- Identifying the Problems: Vidi suggested asking what kind of problems might arise from the business situation presented and how data science can address these problems. This includes understanding the business model, customer interactions, and the operational challenges that the business might face.
- Data Collection and Understanding: Before jumping into data analysis, he stressed the importance of data collection and truly understanding what the collected data represents. This would involve understanding the various data sources and how they can be used for the project.
- Data Analysis and Exploratory Data Analysis (EDA): He highlighted the significance of exploratory data analysis to get insights from the data and to understand correlations and patterns. This stage helps in forming a hypothesis about the data.
- Model Building and Evaluation: Vidi discussed building models and evaluating them based on their performance. He pointed out that one should expect to iterate on the model-building process, using the evaluation phase to refine and improve models.
- Practical Implementation: He mentioned that once the model is built, it’s essential to think about the practical implementation and how it will be deployed in the business context to solve the specific problem at hand.
- Learning from the Outcome: Finally, Vidi encouraged learning from the project’s outcomes, regardless of whether they meet the initial expectations. Understanding the reasons behind the success or failure of the project is part of the learning curve in data science.
The Relevance of Academic Learning in the Data Science Industry
: Perhaps the most poignant part of the discussion, where Vidi bridged the theoretical knowledge acquired in classrooms with its practical applications in the industry. Vidi shared his journey through data science education, touching upon his experiences both locally and abroad. He acknowledged the gap between academic rigor and industry practices but advocated for the deep theoretical understanding gained through formal education. This foundation, he argued, is invaluable for navigating the complexities of modern data science applications, including the use of advanced tools like GPT by OpenAI.
Conclusion
The guest lecture at Universitas Airlangga was a catalyst for change, inspiring a new generation of data scientists to look beyond the confines of their textbooks. Bukit Vista’s commitment to sharing knowledge and fostering partnerships with educational institutions is a beacon for those looking to make a tangible impact through technology.
If you’re a student, educator, or institution inspired by the potential of such collaborations, we invite you to reach out. Join us in our quest to not just navigate but shape the future of data science and property management.