Had someone asked me what I would do differently if I can go back by one year, one thing for sure I would not change is applying for the MSBA program at UC Davis. During this 9-month period, I received a comprehensive training of knowledge and skillsets that most analytics jobs are looking for and eventually found the analytics career that I am passionate about.
One of the most shinning components of my MSBA journey is the practicum project. Via working with the analytics team of a real organization and interacting with real data generated from daily operations, I am able to apply concepts and theories I learned in class to solve real business problems and brainstorm with organization leaders to drive business impacts. During my practicum project, I practiced coding skills using Python extensively to retrieve, clean, and query the operation data. I strengthened the understanding of various machine learning algorithms and their pros and cons in solving different types of problems. For example, the primary problem of my practicum project is to reduce new patient wait time. To translate it into an analytics problem, the question was reframed as what we should do to reduce new patient wait times by x%. By definition, this question falls into the scope of prescriptive analytics. by identifying the causal relationship between the patient wait times and what hospitals can control, such as increasing overbooking and supply of provider slots, it’s reasonable to say we can expect to reduce wait times by x% by altering factor A by y%.
How does my learning in the practicum project benefit me in the aspect of career development? It equipped me with the most sought-after skills for analytics jobs. Analysts distinguish themselves from Machine learning engineers and statisticians by fast and effective problem detection. Analysts know what types of problems are worth exploring and solving in a specific business context by mining the massive data. They understand the business better and have a good mastery of data analytics skills, which are the two overarching capabilities that companies seek when evaluating applicants. Via the practicum, I initiated meetings with clinic managers to learn more about the existing scheduling system, their reasoning for long wait times, and how they see the fit between the strategic position of the organization and the goal of this analytics project. With a better comprehension of the business context, I saw the connection between analytics-driven improvements and business goals. It helped me transform the operational problem into an analytics problem when choosing models and algorithms, and meanwhile empowered me to deliver the results, recommendations, and potential benefits from an operational perspective to management. Through this process, my business acumen and data analysis skills were both enhanced. It also strengthened my career aspiration of engaging with prescriptive analysis on a regular basis.
Besides the practicum project, I also had plenty of takeaways from lectures and the class assignments during the MSBA program. I learned a variety of statistic tests used to aid decision-making in marketing, sales, and product using R and Python. I learned how to query structured and unstructured data for small and big dataset using MySQL, MongoDB and Spark. Those skills qualify me for database management and analytics decision-making, which are top skills of data analyst roles that I am looking for. Meanwhile, I learned how to reframe a business problem in the analytics context, how to avoid pitfalls when designing experiments like A/B testing, and how to be aware of limitations and potential biases of what data says. Whenever I approach a problem, those takeaways would alarm and challenge me to think critically about the data collection process, my understanding of the data and the problem, the methods I choose, and the implication of the likely results before I throw a bucket of codes. Those exercises consolidated my understanding of abstract concepts and enabled me to demonstrate analytics and coding skills via multiple projects on the GitHub profile. During interviews with data analyst positions where experience and knowledge in causal analysis, experimental design and database management are demanded, I have a bunch of stories ready to tell.
With the learning of analytics diving deeper, I saw the application of analytics in different industries and domains. I saw the trend of leveraging automated machine learning services to shift more efforts to framing the problem and improving models. I also witnessed the increased usage of cloud services and the demand for handling and leveraging Big Data. Looking at the analytics career, despite the different titles such as data analyst, business analyst, data scientist, and business intelligence engineer, the common ground lies in the seamless integration of the appropriate analytics and business understanding. The demand for more advanced, more interpretable and user-friendly analytics tools is essentially the demand for faster insights and more effective improvements supported by data. The future analytics career will flourish, binding closely with higher-level business decisions, and promoting in-depth transformation within organizations.
Facing the bright future of analytics, I believe following along with the top-notch innovations in analytics, deepening domain knowledge, being critical about the methods and models used by understanding their mechanisms, and working actively with cross-functional teams to find problems worth tackling will help us sharpen the competitive edge and get fully prepared when opportunities knock on the door.
