Machine Learning Adventures After MLDS

Recent graduate Xueqing (Shirley) Wang shares what appealed to her about MLDS and how it prepared her for life after graduation.

Xueqing (Shirley) Wang had one goal when she began her first class in Northwestern Engineering's Master of Science in Machine Learning and Data Science (MLDS) program (formerly the MSiA program): she wanted to develop the knowledge and skills necessary for a career in data science. 

With her classes completed and a new job as a machine learning engineer at TikTok scheduled to start in February, Wang (MLDS '23) accomplished her mission.  

Wang will be developing machine learning models to help mitigate risk and detect fraud on TikTok's e-commerce platform. Before moving to Seattle for her new job, Wang looked back on her experience in MLDS and how it prepared her for where she is today.  

What excites you about machine learning and data science?   

The widespread adoption of machine learning and data science across various industries, coupled with their rapid evolution, is truly exciting. I am motivated by the opportunity to contribute to the cutting-edge advancements that are reshaping our society. Being an integral part of such innovative changes across different sectors is what truly ignites my passion.  

How did MLDS prepare you for your new role at TikTok?  

The MLDS program's contemporary and industry-aligned curriculum thoroughly prepared me for this role. It equipped me with the necessary skills required for the job, such as proficiency in big data tools and deep learning techniques. Additionally, the hands-on experience from the industry practicum and capstone project were particularly valuable in providing me the opportunity to work with large-scale data and tackle real-world challenges, which went beyond the scope of typical classroom projects. 

What was it about the MLDS program that initially appealed to you? 

The MLDS curriculum is well-designed to prepare students for a data science career. Besides its data science-oriented curriculum, the MLDS program is reputable for its successful career outcomes and a robust alumni network. The small cohort size particularly resonated with me since it promised personalized educational experience and the opportunity to form close relationships with other students.  

As I was browsing through the student and alumni profile section on the program's website, I was impressed by their career placements and diversified experiences. This further indicated the program's competitiveness and reinforced my decision to apply and join a community of outstanding individuals. 

When you started in MLDS, what were your post-graduation goals?  

Coming from an undergraduate background concentrated on computer programming, I recognized a gap in my ability to effectively work with data and understand machine learning models in-depth. Through the program, I hoped to gain practical experience in managing the entire data science pipeline, from data extraction to informed decision-making. This well-rounded approach was crucial to ensure that I would be fully prepared for a full-time professional role after graduation. 

Which MLDS class impacted you the most?  

Cloud Engineering. The course emphasized the significance of containerization and scalability, which are pivotal in the field of data science. The course allowed us to implement these concepts by building and deploying end-to-end machine learning models in a cloud environment. This practical experience was extremely beneficial in understanding the real-world applications and challenges in data science. 

What did you learn from the program’s team projects?  

The most challenging part of each project was always the initial phase where we had to comprehend the broader problem and narrow it down to a specific and manageable scope. Everyone could have a different perspective, so it was important to achieve a consensus on a common goal and communicate often during the early stages. Additionally, collaborating with different classmates was an enriching experience that allowed me to gain valuable insights into working effectively in a team environment. 

Is there anything else you'd like to add? 

I met some of my best friends in this program!

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