Reply To: Letter of Recommendation for Economics

December 25, 2019 at 12:48 am

Score: 45


  1. Restrict each paragraph to 90 words;
  2. Shorten your RL to 350- words;
  3. More than 50% of sentences exceed 20 words. Shorten/split them;
  4. Convert passive sentences into their active counterparts;
  5. Numerous grammatical/syntactic/factual errors;
  6. lots of redundant sentences.

This is XX from XX University XX School. I am writing this letter to recommend my best student – Yewei Qiu. I made acquaintance with Yewei in 2018, when she enrolled in one of my courses, Big Data Economics, and gave me a profound impression by performing exceptionally well with a final grade of 97/100. Generally speaking, she is one of the brightest and hardest-working students with superb analytical ability. For that, I would like to offer my fullest support and strongest recommendation for this promising young lady.

Yewei is a quick learner. My lectures are somewhat challenging for undergraduates who have never been exposed to programming, as they cover bullet points from the primary statements in Python, text processing, Pandas modules, DataFrame, and Web crawlers, to the applications of Python in machine learning, quantitative finance, statistics, and econometrics. But I noticed Yewei was able to catch up quickly in class and submit weekly programming assignments on time with high quality, which demonstrated not only her quick learning ability and intelligence but also her strong initiative for new knowledge. Besides, it takes a deep foundation in data analytics and a strong quantitative ability to comprehend all the contents in my class proficiently, and I believe that Yewei is already equipped with these abilities.

I am also very impressed by Yewei’s superb analytical ability, along with her steady mastery of economic principles and statistical methodologies. By the end of the semester, all students needed to complete a group project to use what they have learned about Python. Yewei’s group chose to discover the effects of GDP and PM2.5 on average expectancy, and Yewei was in charge of data analysis and visualization, the most challenging task with the heaviest workload among all. After analyzing the character of the data, she chose to use the log-log model for its fitting degree. She used regression lines and kernel density plots to objectively demonstrate correlations between variables, displaying her strong data analytic skills, a strong logical mind, as well as a consolidated knowledge base in statistics.

More importantly, I was also astonished by Yewei’s innovative thinking when it comes to applying theories in cases. For the data visualization of the project, she utilized course codes from ECharts, adjusted collected data into a specific format to embed the source codes and produce images, and eventually displayed correlations between data with a dynamic bubble chart. Unlike scatter diagrams, dynamic charts are more interactive and easier to check different data from different countries, which was an interesting approach that none of the other groups have thought. Their presentation was intriguing, comprehensive, and overall satisfying. Besides, in the final exam, Yewei handled the last and hardest question with flying colors – I required them to design a new indicator to solve a current issue in movie ratings. To my surprise, Yewei used the Bayesian statistical algorithm that IMDb uses in movie rating, created some new variables instead of using the existing ones, and ran a t-test on the old and new scores to test the disparities between them. Her creativity earned her a 97 out of 100 in the final exam, ranking top 3 in class.

I see Yewei as an individual with strong motivation towards her goals. And she can steadily and rationally push herself every step of the way. She is outgoing, resilient, reliable, and communicative, and have huge potential to achieve success in any advanced program or data-driven position in the future. Therefore, I would like to offer my strong recommendation for Yewei Qiu, hoping that you can take my evaluation into serious considerations for her candidacy into your esteemed program.