Welcome to Pat's Page!

Hi! This is Qingmengting Wang, you can also call me Pat :)

About me

I am now a Master's student in Data Science at University of San Francisco, with past experiences in Pocket Gems, Baidu and Accenture. I am passionate about applying Data Science techniques to solve real-world challenges.

To know more about me, you can find some info below:

View my resume View my Linkedin


Analysis Techniques

Machine Learning, Deep Learning, NLP, SQL, A/B testing

Programming Languages

Python, C++, Matlab, Spss, Eviews


Spark, BigQuery, PostgreSQL, Flask, ggplot, plotly, AWS, Bootstrap, PyTorch, Git, Keras, TensorFlow

Work Experience

Data Science Intern

Used several deep learning techniques to work on two natural language processing related projects.

Data Analysis Intern

Used exploratory data analysis to detect anomaly for Baidu's advertisting Business.

Consulting Intern

Conducted oversea-investing strategy research for PetroChina, in which I used several machine learning techniques for some side projects.



A tool that can help you convert your math equation directly to code. This purpose and usage of this project is clearly described in our main page. And you can find our code here.

Will your dog get lost?

Key words: exploratory data analysis, ggplot
This project is aimed to compute the missing rate of different dog’s breeds with DataWorld Animal Center Intake with several exploratory data analysis techniques. We tried to summarize the common features of breeds that are easy to get lost.
This project is a lot fun! After studying a bunch of reasons such as size, age, running speed, we found that the key feature that significantly increases the missing rate of dog is: sterilization, i.e. whether the owner castrates or spays it. For different breeds, unsterilized male or female dogs also have huge difference of missing rate. For example, male huskies have much higher missing rate while female corgis are easier to get lost, from our data.

Patients' heart rate and arterial pressure prediction

Key words: kaggle competition, feature engineering
This project is a kaggle competition that aimed to predict patients' heart rate and arterial pressure. It's a fun competition that relys heavily on feature engineering. To find more, you can see our code here.

Stay Connected!

Feel free to contact me at anytime!

Email: qwang83@usfca.edu
Phone: (628)602-5952
3 Bayside Village,
San Francisco, 94107