Data analyst
Resume:
WONE EUI JUNG
714-***-**** · ********@***.*********.*** · San Jose, CA 95192
PROFESSIONAL SUMMARY
MS Information Systems (decision science concentration) student with 8.5 years’ experience in finance. Machine Learning, NLP, Deep Learning, Statistics, data visualization, data driven analysis. Seeking to use my expertise in data science application to proceed insightful and efficient solutions to solve real-world problems and to achieve company goals.
STRENGTHS AND EXPERTISE
Strengths: ETL, Data Visualization, Data Analysis, machine learning, NLP(natural language Processing), deep learning, Regression forecast, Time series decomposition, ARIMA model.
Software/Apps: SQL, Python (TensorFlow, Keras, Scikit-Learn, NLTK, Numpy, Pandas, SciPy, Scikit-learn, seaborn), R (dplyr, Caret, ggplot2, StringR), Spark(Spark SQL, MLlib, Spark Streaming), Scala, Tableau, Alteryx, AWS, Bloomberg, ForecastPro, R Shiny,, Excel, VBA.
Github : https://github.com/woneuy01
Linkedin : https://www.linkedin.com/in/wone-eui-jung-899abb1b/
Medium: https://medium.com/@woneuy01_5877
Project
February 2020-Current
Covid-19 Social media Analysis (YouTube, Reddit analysis): Python, AWS, API
Area of interest: NLP, Sentiment analysis, Deep Learning, Machine Learning, Transfer Learning
Convert YouTube to audio file and change audio file to text file with AWS transcribe
Collect YouTube analytics retrieve view, like, comment counts using YouTube API
Scrape YouTube and Reddit comments sentiment analysis
Text data cleaning (Lemmatization, Stemming, TFIDF), topic extraction using N-gram
Visualization with word cloud, Word2Vec, GloVe, sentiWordNet, BERT, Sentiment analysis
Sentiment classification using SVM, CNN, GRU, LSTM
September 2019-December 2019
Analyze Los Angeles Traffic Collision data sets (large data sets 488K rows) from Kaggle: R
Area of interest: Random Forest, XGBoost, K-means clustering, Logistic regression, Time Series
Data cleaning, data manipulation, detect outliers, handling missing data, combining categories, and check collinearity.
Exponential smoothing and holt’s methods showed decreasing trends for last years.
Under-sampling, up-sampling, SMOTE used for unbalanced data classification.
Classification (logistic regression, KNN, decision trees) found that time and location is an important factor for a car accident.
Random forest, XGBoost predicted that time is the most significant impact to predict DUI(Driving under influence) and hit and run.
K-means clustering group people by gender, age, race. Cluster 2 group showed highest parking lot accidents compare to other groups. LA city can provide this group for extra parking education
October 2019 – December 2019
Analyze Bank Marketing Data Set from UCI machine learning repository: R
Area of interest: Random Forest, XGBoost, K-means clustering, Logistic regression
ggplot for data visualization and dplyr for data exploration and data cleaning.
Collinearity showed employment is highly related to interest.
Under-sampling, SMOTE, over-sampling for unbalanced data classification.
Experiment for 0.5 and 0.3 cutoff for classification.
Classification using KNN, decision trees, logistic regression to predict telephone marketing result.
Random forest and XGBoost showed call duration, and interest rate were the most important factors to predict the success of customer subscription of term deposit.
XGBoost achieved the highest sensitivity (93%) among all the models showing the call duration is the most important factor at 0.5 cutoff.
April 2019-May 2019
Web scraping Yelp restaurants: Python
Area of interest: Web scraping, data extraction
Pandas and Beautiful soup to scrape data from the Yelp website.
Collect Irvine area restaurants data and export to csv/xlsx.
NumPy and seaborn to explore restaurant data and plot distribution of price and rating.
May 2020
Interactive dashboard Amazing Mart data analysis: Tableau
Profit analysis by category, country, individual on dashboard interact with filter by year and region.
PROFESSIONAL EXPERIENCE
KYOBO AXA Investment Managers
Portfolio Manager (Global market fund) – Seoul, Korea December 2013-December 2016
Managed MSCI World Index tracking fund with ETFs and futures achieved stable market performance.
Managed global market fund with hedging various currencies.
Built Long/Short model composed of value and growth factors on MSCI developed market Index.
Established Shanghai-Hong Kong stock connected delegation fund and RQFII trading, closely worked with China and Hong Kong offices while co-operating with compliance and risk teams.
Reported closely with institutional clients for monthly performance analysis and ad-hoc request.
Communicated with the marketing, sales, and compliance teams regards managing and product development to ensure alignment of legal and risk requirement objectives.
KYOBO AXA Investment Managers
Portfolio Manager (Domestic fund) – Seoul, Korea February 2011-November 2013
Listed ETFs on Korea exchange market and managed ETF tracking KOSPI100 and KOSPI200 index funds.
Researched earnings revisions of consumer discretionary sector of KOSPI 200 Index.
Communicated with institutional investors providing market research and technical fund performance analysis report using excel and Bloomberg.
KYOBO AXA Investment Managers
Research Assistance – Seoul, Korea April 2009-January 2011
Assisted In-house economist market research.
Researched global market indicators and report global market news to clients during market crash period.
KYOBO AXA Investment Managers
Trader (Global fund) – Seoul, Korea June 2008-March 2009
Traded global market stocks and hedging foreign currencies with forward and futures.
Carried out transactions of global market trades and researched global market trends.
Education
California State University, Fullerton May 2020
Master of Science in Information Systems, Decision Science (GPA 3.75)
Soldesk Academy April 2017-August 2017
Java Web developer course
University College London, UK
Bachelor of Engineering in Electrical Electronic Engineering
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