This document is a resume for Grace Han Yang that summarizes her education and experience in business analytics. She received a Master's degree in Business Analytics from University College London, and a Bachelor's degree in Business Intelligence and Information Management from Southwestern University of Finance and Economics in China. Her experience includes data analysis internships at the City Council of Croydon and EDF Energy, where she conducted analytics, visualization, and machine learning projects on large datasets.

1. GRACE (HAN) YANG
han.yang.15@ucl.ac.uk (+44) 07719924933 49 Southbridge Road, Croydon, CR0 1AG
EDUCATION
UNIVERSITY COLLEGE LONDON, London, UK Sep 2015–Sep 2016
Master of Science in Business Analytics (with specialization in COMPUTER SCIENCE) GPA: 78.75/100 top 10%
CORE MODULES: Business Analytics, Data Analytics, Information Retrieval and Data Mining, Web Economics,
Natural Language Processing, Graphical Models, Applied Machine Learning, Supervised Learning
SOUTHWESTERN UNIVERSITY OF FINANCE AND ECONOMICS, Chengdu, China Sep 2011-Jul 2015
Bachelor of Science in Business Intelligence and Information Management GPA: 86.3/100 top 15%
CORE MODULES: Mathematical Analysis, Advanced Algebra, Probability, Statistics, Time Series Analysis,
Econometrics, Information System Management, Data Analysis and Business Intelligence, Database Management,
Data Mining, Microeconomics, Macroeconomics, Accounting, Intermediate Financial Accounting, Finance
INCLUDING: UNIVERSITY OF JYVÄSKYLÄ, Finland Sep 2014-Jun 2015
Exchange Student in Information Systems (sponsored by CHINA SCHOLARSHIP COUNCIL) top 10%
EXPERIENCE
CITY COUNCIL OF CROYDON, London, UK Oct 2016-present
Data Analyst Intern
Investigate data from various sources (e.g. welfare, legal system) and conduct advanced data visualization individually
using MS Power BI; generate well-written report to support the decision-making process of the ongoing projects.
EDF ENERGY, London, UK Jan 2016-Apr 2016
Data Analyst Intern
Worked in a group of 4 and developed ad-hoc machine-learning algorithms to predict the probability of a customer
renewing with EDF in Python on a 70077-records dataset; reported to non-technical EDF manager in weekly meeting.
l Visualized each variable and got exploratory insight into the customer dataset; recognized outliers and extracted their
common characteristics with SVM; proposed several renewal product recommendations based on the data insight.
l Identified customer segmentations using a method integrating K-means and K-modes to cluster mixed categorical
and numerical data, finally obtained 5 customer clusters and evaluated them on their customer values.
l Achieved final prediction accuracy of 72% within set deadline; generated a final report summarizing the project
implementation and presented to senior manager of our predictive models as well as the modeling results.
CHINESE HOUSEHOLD FINANCE RESEARCH CENTER, Chengdu, China Jul 2013-Aug 2013
Surveyor & Data Analyst Intern
Conducted research on the financial overview of Chinese households; attended data collection techniques training.
l Interviewed sample families those are randomly chosen by computer in Guizhou Province and collected their
financial data; wrote advanced queries to analyze the major payment behavior of 8000 households by MySQL.
l Identified a significant increasing trend of online payment; analyzed the ongoing payment behavior transformation.
PROJECTS
DETERMINE PROTEIN DOMAIN STRUCTURE USING MACHINE LEARNING, London, UK Jun 2016-Sep 2016
Data Analyst
Built machine-learning models to predict the protein domain structure directly from amino acid sequences; reported
weekly progress and discussed further work with supervisor; wrote up a final dissertation based on the modeling results.
l Integrated unstructured protein sequence data of various formats into one unified data file; created a novel chunk
prediction method for domain boundary and predicted domain number solely by global protein sequence features.
l Ensemble classifiers and logistic regression model (LR) were trained with the consolidated data to predict domain
boundary in Python; obtaining domain boundary and number prediction accuracy of 87% and 72% respectively.
CLICK-THROUGH RATE PREDICTION FOR ADVERTISEMENT REAL-TIME BIDDING, UK Feb 2016-Apr 2016
Group Leader & Data Analyst
Cooperated with 2 group members from math and engineering backgrounds to predict the click-through rate of each
auctioned ad impression in real-time bidding display advertising given a noisy dataset of 3M users’ clicking records.
l Cleaned and resampled the imbalanced dataset to train machine learning algorithms (e.g. boosted tree models, LR)
with various feature-engineering methods (e.g. log-odds), optimizing the trained models by parameter tuning.
l Obtained a converged AUC score 0.780 and finished ranking the FIRST place in the KAGGLE competition.
TECHNICAL SKILLS
Languages: Python, R, MATLAB, Java, SQL Tools: SPSS, SAS, Tableau, MS Power BI, MS Office, MS SQL server