MY PATH

My interest in statistics and data analysis is twofold: first, is my resolute desire to pragmatically apply the analytical skills I have accumulated over my career in project management and supply chain management to helping find answers and solutions to some of the world’s most pressing existential questions. I would argue that the skills that I developed while working within the world of project management has provided me with a strong fungible foundation of analytical abilities to be successful in doing graduate level work in Statistics. Second, to set a strong example for both of my young children that anything is indeed possible with hard work and dedication. Going back to school as a single mother was one of the most difficult decisions that I’ve ever had to make, but also has been one of the most rewarding. In the end, I want to be able to provide the best kind of life for both of my children, in ways that are not merely financial and pursue a master’s degree in Statistics is the first step in achieving these goals.

My undergraduate degree in Data Science prepared me to efficiently collect, organize, and analyze large swaths of data for applications such as modeling climate change or understanding business trends. At its heart Data Science combines many disciplines such as computer science, ethics, mathematics and statistics to analyze and solve complex questions. In its most basic form, Data Science makes use of detailed mathematical analysis to create quantified models, representations and synopses for a given set of data. I have found the statistical component of Data Science to be one of the most interesting aspects of my education, and it’s the reason why I’m looking to continue working my education in Statistics.

From a young age, I felt myself drawn towards data driven solutions. I can vividly remember working in my parents shop and stocking the same spot on the shelves after school every day. This spot on the shelf belonged to a very popular brand of instant noodles, so popular in fact that the small space on the shelf we had for the noodles was practically empty every day. In comparison, the other kinds of noodles that surrounded these popular noodles remained relatively full for long periods of time and required little to no daily restocking.Customers couldn’t buy products that weren't on the shelf and might entirely forego buying anything from our store as a result; this was a real problem that required a real solution. Recognizing this situation, I told my parents about the noodle stocking problem they had on their hands. I brought forward to them all the information that I had about the situation, from the amount of this particular brand of noodles being sold compared to the rest of the noodles we carried, to the potential lost revenue not stocking more of this noodle could represent to their business. The data that I presented to my parents made them think deeply about what I had said, and ultimately took my recommendation about expanding the shelf space for this noodle. As a result, I no longer had to completely restock the noodle shelves every day and my parents saw a modest increase in their overall business.

As I got older I became more enthralled with understanding what information, specifically statistics, could tell me about the way the world really works. Understanding statistics better made it so I could begin to see the important relationships between two disparate data sets. I remember IMB running television commercials about this subject, they said something to the effect of “What can Scottish sheep markets tell us about oil production in the Middle East?”. This commercial advertising the effects Big Data and Analytics gave me a better framework about how to understand the types of questions I was interested in answering. Statistics, as a field, initially appealed to me because of its ability to make sense of the seemingly random noise that large data sets appear to be on the surface. The ability to be able to understand both the empirical and theoretical undergirding of large parts of our world using models and equations to both describe a situation while also being capable of offering up meaningful suggestions for prescriptive action is awe inducing.

Being aware of the potential power that data could have, especially in my previous career in Supply Chain Management (SCM), I started to apply some of these lessons to my own work. I started to focus on how to apply data to the business decisions I was responsible for at my job. I wanted to be able to make better informed business decisions, and data represented this possibility for improvement. In SCM the demand forecast is the heart and soul of the entire operation, it serves as the barometer from which all other metrics are constructed. Sensitivity to this forecast is the difference between being able to accurately assess and implement effective inventory strategies, production arrangements with manufacturers, and even the order delivery date for customers; in short being able to accurately predict and model the demand forecast in SCM is the difference between being successful or not. Fortunately, I was able to use some of the rudimentary heuristics and analytics I knew to engineer a successful career in SCM in China.

I worked on one such project that required manufacturing in China, while supplying an overseas end user. Historically my company at the time had experienced difficulties with shipping our products and having them arrive in a timely manner to the customer. In order to meet the on-time delivery target I used the knowledge I had acquired about day to day operations of the factory to successfully solve issues concerning continuous production and helped to implement a new process which helped to shorten delivery times by nearly 30%. Not being able to see the connections between the various steps in a products’ manufacturing life cycle, makes it difficult to be able to effectively, accurately and successfully predict potential problems. Having a better understanding of the data, I would argue, would make forecasting some of these potential problems a more feasible undertaking than it presently is. In the end, understanding what the data says allows you to be able to solve larger and more complex issues.

While I have an interest in being able to apply the methodology and approach to fields like SPC, I recognize that the problems that Statistics can help us understand are much greater and often more complex than understanding some of the mundane maladies of business; but the underlying process and techniques for understanding is much the same. I am also drawn to studying Statistics for its interdisciplinary nature; being able to work across multiple disciplines to not only solve problems but to also understand how their contributions can be used in new and innovative manners is an exciting prospect for me. Understanding what the data has to say is only one important part to understanding the problem, we must also know how to listen to what the data is telling us. Part of my undergraduate studies in Data Science we learned what it meant to ‘listen’ to what the data was telling us. I believe that the skill set that I have acquired through both my professional and academic career has uniquely prepared me to succeed in graduate level work in statistic. I am ready to take the next step in my career and this step will begin by applying what I have learned in the past to practice