Class of 2021

Photo of Lanqi Fei

Lanqi graduated from University of Maryland, College Park in 2020 with double majors in Mathematics and Agriculture and Resource Economics. During her time in college, Lanqi has built solid theoretical data analytics backgrounds through her mathematics courses. She also gained hands-on analytical skills through her computer science and econometrics projects.

Lanqi became interested in data analytics and data science through a Kaggle sales forecasting data competition. In this competition, Lanqi presented an overall analysis and solution to the underlying machine learning problem based on time series data. She and her teammates used Python to visualize the dataset and plotted the trends of transactions in the Favorita grocery store to compare the number of items in different categories. Noticing how sales have seasonal patterns and that the demand for different grocery items varies, Lanqi explored models based on time-series like log moving average to capture its seasonal trend and broke the datasets into various categories to train them separately. This experience improved Lanqi’s coding skills and confirmed her interest in data analytics and data science. Later, Lanqi worked on an econometrics research paper collaborating with classmates in her junior year. For this research paper, Lanqi explored the relationship between a country’s level of development and the corresponding greenhouse gas emissions. Through conducting data mining and cleaning, generating visualization charts in Python to evaluate historical patterns, running correlations, and clustering analysis, Lanqi filtered out over 10,000 suitable data entries from CAIT Country GHG Emissions provided by the World Resources Institute, and GDP and Population data from the World Bank of over 186 countries. Then Lanqi applied machine learning models such as linear regression, cross-validation, and PCA on the training dataset, and finally built an excellent model with an approximate accuracy of 89% and discovered 25 potential factors. Among these factors, Lanqi found that countries that produce higher GDP tend to have a higher level of total GHG emissions. Among the different factors, it was also found that for developed countries, the energy sector is the primary GHG-emitting sector, whereas, for developing countries, agriculture still accounts for 20% of emissions. This research experience has given a boost to Lanqi’s confidence in data analytics and modeling skills, as well as strengthened her enthusiasm in working in data analytics industry.

As a MSiA candidate, Lanqi is looking forward to deepening her knowledge in data analytics through her coursework and industry practicum to prepare for her future career in data analytics. She is eager to apply her skills to deal with real world complex problems and believes that she will make an impact in the industry.