My UvA Journey

Keywords: Business Communication, Information Dissemination, Data Science, Python, Business Analytics

Between 2021 and 2024, I completed my my Bachelor of Science in Communication Science and pursued a minor in Business Analytics and Data Science at Vrije Universiteit Amsterdam.

During my three years at university, I spent a year and a half working full-time. If you want to know more about my work experience, please click the “Education & Work” tag at the bottom or the link on the right to know more about my experience.

2021-2023

In the first two years of studying in Communication Science, I mainly focused on understanding how information is disseminated across different fields and how factors like color and packaging influence people’s subconscious, thereby affecting their decisions and perceptions. I learned to proficiently use SPSS for data cleaning and descriptive statistical analysis, and conducted hypothesis testing through t-tests, chi-square tests, and ANOVA to determine if relationships and differences between data were significant. Additionally, I mastered various research methods such as interviews (qualitative research), content analysis, and quantitative analysis.

Each course honed my ability to explain complex research findings clearly to those without related knowledge and helped them understand these results. Moreover, these courses developed my ability to view complex problems from different perspectives, allowing for thinking outside the box and coming up with alternative approaches.

Minor: Business Analytics and Data Science (Vrije Universiteit Amsterdam, September 2023 - February 2024)

I have always been deeply interested in data science. Through this minor, I gained practical experience in machine learning and data analysis. Before this, I had no formal training in Python and was studying while working full-time. Despite these challenges, I successfully completed the course, which had a pass rate of only 50%. I recall that at the start of the course, the professor asked about our backgrounds; 70% of the students majored in AI or Data Science, and I was the only one from social sciences without Python experience.

Through this minor, I learned the following:

  1. Introduction to Data Science: Mastered basic concepts of data science, including data collection, processing, analysis, and visualization. Learned to use Python for data analysis and model building, focusing on machine learning methods such as classification, logistic regression, decision trees, and K-NN models. Gained hands-on experience in using libraries like Pandas, NumPy, Matplotlib, and Seaborn.

  2. Strategic Management of Technology and Innovation: Understood types of innovation, external innovation environments, and their operationalization, including innovation trajectories, standards, platforms, and ecosystems. Learned about product development processes and organizational conditions that promote innovation. Gained in-depth knowledge of Agile and Scrum work models and the development of innovation strategies and their implementation in project selection, collaboration, and protection.

  3. Information Retrieval: Mastered basic concepts and techniques of information retrieval, including how search engines work. Through six coding assignments, I deeply studied key technologies of information retrieval such as indexing, searching, ranking, and evaluation. Explored the application of natural language processing (NLP) and machine learning in information retrieval.

  4. Data Structures and Algorithms in Computer Science: Mastered common data structures (such as arrays, linked lists, stacks, queues, trees, and graphs) and their operations. Learned basic algorithms (such as sorting, searching, and graph algorithms) and their complexity analysis. Developed the ability to design efficient algorithms to solve real-world problems and optimize code performance through weekly tests based on interview questions from companies like Google and Amazon.

  5. Data Cleaning: Gained the ability to find entry points in complex data, propose valuable questions, analyze them, and present research results in a way that the general public can understand. Learned skills and tools for data cleaning, integration, and transformation. Understood methods for handling structured and unstructured data, including dealing with missing values and data format conversion, to prepare and organize data for subsequent analysis and modeling.

Thesis

In June 2024, I completed my thesis titled “The Myth of Color: How Low-Saturated Packaging Can Entice You to Make a Purchase?” Please click on “Education & Work” at the bottom or the link on the right to read the article titled “Thesis (Marketing): The Impact of Pale Packaging on Soda Choices”