When I initially delved into data analysis, I felt like I was wandering in a huge maze and gathering pieces of information along the way to find the exit. As I gained more experience in working with data, I discovered that it was a thrill to analyze data to find patterns and even more fulfilling to transform them into useful insights for decision makers. However, the more I learned, the humbler I became. I realized that in order to proceed further on this path that I am interested in, statistical knowledge and coding skills are quite necessary.
I entered the data analysis field because I have a strong curiosity to find out the relationships and hidden patterns between behaviors and outcomes. I was curious about how media companies made decisions based on the market and the factors that led to a better financial success. Therefore, I chose the topic “The Impact of Multiplatform Competition on U.S. Television Stations’ Financial Commitment and Financial Performance” as my graduate thesis under the guidance of Dr. Ann Hollifield. I conducted academic research using public data sources, such as the Market Competition Index from the National Association of Broadcasters and the Net-Revenue of Broadcasting Companies from Nielsen. I analyzed the correlations between variables using SPSS and built up visualization graphs in Excel. After analyzing the results, I found that in the past 10 years the multi-platform competition had increased and more local broadcast companies displayed various formats of media content, which increased profits. As my first time approaching a big data analysis project, this experience gave me the sensation of finding treasure in a mine and thus confirming my interest in data analytics.
The experience with my graduate thesis and analyzing public data sources landed me a job as a digital marketing analyst at U-Haul after graduation. I quickly discovered that I loved being an analyst. I completed a couple projects for the Truck and Trailer program that increased the number of reservations for the subsequent quarter. I felt very encouraged and fulfilled seeing my hard work turn into valuable assets for business units to make decisions. People started to recognize my analytics skills, even though most of my work had only involved Google Analytics and Excel. A year later, I was promoted to a business analyst role. In the new position, I worked on projects that required knowledge of Google Data Studio, MS SQL server, Tableau and Python. Initially not very familiar with these tools, I had to search online for assistance and take several online courses. However, I was able to quickly absorb the material and apply my new skills to my work. I want to continue to further my knowledge and engage myself in this new blooming field, which has many applications.
Right now is an ideal time for me to enroll in your MIDS program. My company recently shifted its focus to big data and purchased the license for Kafka, Data Brick and Tableau. The program’s curriculum includes machine learning and statistics, which will solidify my foundation required for these tools. Through attending my company’s data science and analytics meetings bi-weekly, I saw how other coworkers built up dashboards from datasets and used machine learning to study customer purchasing patterns. I am very eager to learn more about this growing field and make good use of the new tools available. Due to the large amount of traffic visiting our websites and numerous locations across the country, the data we process are greater than a hundred thousand lines. It will be very useful for me to learn how to handle a large amount of data properly and therefore be more efficient at running analysis across the board. With the courses from the data science program, I can contribute more to my company.
The data science field has an incredible amount of practical applications that I have only touched the surface of, and knowing this has made my passion for pursuing this path even stronger. Coming from the marketing and communications background, I desire to improve my skills in data modeling, machine learning, statistics, and coding so that I can provide better guidance to my coworkers and help drive business decisions.