Renee Hsiao | Junior (Year 3) | BSc in International Business
Position: Alibaba - Data Analytics
Renee:
In this training internship, I participated in three projects related to data analysis and data science, gaining practical experience with data cleaning, modeling, interpretation, and business application. This journey helped confirm my academic and career direction and strengthened my technical skills.
I encountered challenges during training since I do not have related background. Therefore, the chance to learn within a large corporate organization and collaborate with professional mentors was especially valuable and meaningful.
First project: Focused on user behavior and value analysis using the RFM model. By analyzing transaction and behavioral data, I calculated recency, frequency, and monetary value to segment users into high-value, growth, and low-activity groups, transforming raw data into actionable business insights.
Second project: Centered on customer segmentation and behavioral pattern exploration. Through feature engineering and clustering techniques, I identified differences among user groups and learned how to effectively communicate analytical results to ensure their practical application.
Third project: Concentrated on market basket analysis and product association rules. By constructing a shopping basket matrix, I identified frequently co-purchased products and evaluated their support and association strength, demonstrating how data science can inform marketing strategies and product bundling.
Completing these projects provided a strong sense of achievement and clarified my suitability for graduate study. With guidance from my corporate mentor, I learned how to define business problems and generate actionable insights. This training strengthened my foundation and motivated me to pursue advanced studies, aiming to become a data-driven decision-making professional.
Eason Chen | Grade 11
Position: Alibaba - Data Analytics
Eason:
Hi, I'm Eason, and I participated in Alibaba's data analysis training internship. Before the training started, I was actually a bit nervous because I'm only 17 and not very familiar with the real workplace.
This data analysis training was a very important experience for me. Although I was not familiar with the field of data analysis, under my instructor's guidance, I learned how to organize data, analyze numbers, create charts, and turn complex numbers into clear results.
In the real world, the data is often messy and it taught me how to clean data, categorize it, find problems, and better understand the complete data analysis process. Along the way, I also learned how to research, how to report progress, and how to organize messy data into conclusions that people can understand.
This training made me more certain that I like doing data- analysis related work and gave me a clearer sense of my future direction. I'm very grateful to everyone who guided me.
James Lin | UCL | MSc in Infancy and Early Childhood Development
Position: CAS - Psychology Research Assistant
James:
Looking back on my training internship journey, it felt like an evolution from “confused thoughts” to “clear logic.” The pre-employment training played a crucial role in this transformation. It clarified application procedures, responsibilities, and required materials, allowing me to prepare with confidence rather than uncertainty.
Previously, interviews were my greatest concern. I often spoke in a loop, struggling to present my suitability for a position in a structured way. Through mock interview training, I realized that effective expression is a skill requiring deliberate organization of ideas. On the actual interview day, this preparation proved invaluable. I was able to articulate my experiences systematically, engage in meaningful dialogue with interviewers, and recognize the importance of further refining concise communication.
The internship also fostered profound professional reflection. In the early stage, I translated the work of psychotherapy scholar Harlene Anderson, whose concept of “collaborative conversation” emphasizes a non-pathologizing and “non-knowing” stance. While this approach initially shaped my idealized view of therapy, subsequent experiences provided deeper insight. While organizing a senior expert’s interview about the “family consultant” model, I felt the emotional weight therapists face: the expert serves high-stress school principals who, despite appearing successful, suffer deep despair and suicidal thoughts. At the same time, the expert’s comments on clients suddenly discontinuing therapy helped me understand the necessity of respecting their autonomy “let the flower become a flower, let the tree become a tree”, and learning to carry regret while staying professionally stable.
A pivotal moment occurred while observing a teacher handle a client with obsessive-compulsive disorder (OCD). Instead of attempting to eliminate the client’s fear of the number “4,” the therapist guided the client to coexist with anxiety and continue functioning. This experience reshaped my understanding of therapy—from offering quick solutions to fostering resilience.
Overall, the internship transformed me from a translator focused on textual accuracy into a learner with deep respect for human experiences. It affirmed my commitment to psychology as both a science of people and an art of lived experience.
Candy Lee | Junior (Year 3) | BSc in Industrial Engineering and Management
Position: Alibaba - Data Analytics
Candy:
I learned a lot. At the beginning, GIC advisors helped me contact my mentor, and afterward they also checked on and tracked my training progress. Whether it was document handling, scheduling, or confirming training content, the advisors assisted and reminded me so I didn’t feel lost. During the training period, they also regularly checked on and tracked my learning progress to ensure the project proceeded on schedule, so I didn’t have to worry about being unable to reach my mentor or the training plan failed to move forward.
In terms of content, my mentor gave me a complete data analysis project framework: from problem definition, data collection and preprocessing, exploratory data analysis, to model building, algorithm application, and result evaluation. Each step clearly explained the goals and methods, letting me complete a full data analysis report step by step. I learned many practical applications of modeling, algorithms, and statistical methods, and also learned that data visualization can present data results more clearly and easily understood in the report. During the process, any questions I didn’t understand, I could ask my mentor. The mentor could clearly answer which method to use here, resolve doubts, or guide me to complete the task. I could ask freely whenever I am in doubt. If I had questions, I could report them and the mentor would flexibly adjust. Overall, over these six weeks I learned more than expected.
Personally, I think the biggest strength is the mentor’s ability to clearly resolve problems, explain, and guide task content. This clear and directional guidance method lets me truly understand what I’m doing and why it is done this way. When doing research with professors at school, they don’t necessarily give concrete, clear explanations for students. Sometimes, the answers are more general or inconsistent, leading us to feel uncertain about the research direction, which in turn affects progress. When problems aren’t truly clarified, we can only repeatedly try and guess, which not only has low efficiency but also makes it hard to build solid understanding. Therefore, this training amplified the strength of being able to clearly answer questions.
Nikki Peng | BU BSBA
Position: JP Morgan - Business Analytics
Nikki:
Hi, my name is Nikki. Today, I’m presenting the results of my analysis on the Chinese NEV market. Through this process, I have developed a workflow: from sourcing high-quality industry data, extracting critical technical variables, and transforming raw numbers into visual evidence. My first goal was to move beyond surface-level information to find the technical truth of the industry. I learned to navigate three types of data sources.
The first one is regulatory frameworks. I analyzed the "dual-credit policy" to understand how the government shifted from direct subsidies to market-based constraints.
The second is industry benchmarks. I sourced specific battery performance data, such as energy density and charging C-rate, to track technical progress.
The third one is market statistics. I utilized panel data from 2011 to 2025 to map the historical growth trajectory of the industry.
The core of my learning involved data extraction—identifying the "signal" within the "noise." I focused on three structural shifts:
1. Technological Segmentation: I categorized the market into BEV, PHEV, and REEV, identifying the specific trade-offs between system complexity and energy efficiency.
2. The 2021 Pivot: By analyzing installed capacity data, I identified 2021 as the year LFP batteries officially overtook ternary batteries due to innovations like CTP and blade battery technology.
3. Customer Logic: I extracted survey data showing a fundamental shift in buyer priorities, where safety and charging speed have become more critical than brand name.
Finally, I applied my design background to create visual evidence of market transformation. Here are the key findings I visualized: The first one is the 50% Milestone. I mapped the penetration rate from near zero in 2011 to 55% in the first half of 2025, signaling the official decline of the internal combustion engine.
The project taught me that effective data analysis is about more than just numbers; it’s about synthesis. By combining economic theory with technical data and visual design, I can now tell a complete story of how an industry matures—from policy-driven beginnings to a market-driven future. Thank you.