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Di zhang
data analyst Di Zhang, Data Entry & Admin
skills: Python Sql Sas Tableau Matlab Qualification and highlight: • 3-year of work experience using SAS, SQL, Python in telecom, and retail doing reporting to give insight for business stakeholder and ETL for data that used for building report and dashboard. • 2-year of work experience in data visualization and reporting in insurance, consumer product and manufacturing industries using Tableau for dashboard and story presentation, SAS Gplot & SGplot for dashboard and linked report with dashboard, PowerPoint, Advanced Excel, and Excel VBA. • 2-year of work experience with loss ratio analysis, and churn rate analysis using and data mining using machine learning models (Mixture of Gaussians, knn, NN, linear regression); also using SAS EM for logistic regression model. insurance industries to produce business plan and analysis of current market utilized SAS EG. • 1-year of work experience with data mining in insurance industry for data visualization and reporting; and manufacturing industry for image clarification in quality assurance; utilized machine learning model: Mixture of Gaussians, knn, NN. Applying Python library: pandas Numpy, Sklearn, Keras, Matplotlib) • 2-year of work experience in medical research with developing new ethel detection device while using SAS for ETL, reporting using Tableau SAS Gplot, data validation; MATLAB for simulation of the experiment. • Master’s degree in engineering. • Have experience in leadership in small group project and ability to work independently and part of a team with good problem-solving skills. Experience: Data analyst contract, MacroData Consulting Inc., Toronto Sept 2018 – April 2020 • Performed ETL by utilizing SAS data; and perform data validation on key KPIs; and derive necessary variable from the data Combine the data from different data source. • Produced graphs dashboard and reports using SAS SGplogt & Gplot & Tableau, also building predictive model using SAS EM and python. • Project 1: pulled on customer data of a home equity loan company from Oracle server (using SAS libname reference), build a report on the segmentation of the customer and analyze the risk factor using SAS EG. • Project 2: accessed customer data from SQL server to build a business report which show the trend in sale and prediction in the future load utilized SAS EG, also utilize SAS macro automation in the report to show weekly or monthly progress. • Project 3: extracted data on customer transaction data of an insurance industry from DB2 (using SAS proc import) to build a report on the post campaign analysis and some ad hoc analysis during the campaign process and develop KPI to monitor the campaign. • Project 4: Loaded transaction data and credit score data from DB2 server and preprocessed the data using pandas. Combined relational database using SQL and creating new variables or drop unwanted variables. employed SAS Gplot to produce a dashboard about loss ratio analysis, segmentation analysis, and churn rate analysis. • Project 5: extracted data on customer transaction data of a retailer from Oracle server (using SAS libname reference) applied utilized RFM analysis on the transaction data to segment customer for better CRM to cross-sale and up-sale, utilized SAS EG. Reporting analyst, Nielsen, China May 2018 – July 2018 • Extracted yogurt sales data in china from csv to provide insights off the market. • Produced report on the seasonality of the market, projection of the sale in future, comparison on the sales of different channels and different market segments based on location and investigated the market share of different brands. • Analyzed the market trend and customer preference on different product and brand, • Utilized tableau to create a story to present the insight into the yogurt market. Researcher, CADIPT May 2016 - Jan 2018 • designed a simulation for in-vitro ethanol detection in blood, as well to preprocess data and calculate desired statistical parameters and coauthored two paper on this system. • Created weekly report on simulation data points to find optimal parameters for the ethanol detection system to supervisor, also produced monthly report to compare between different system parameter also between simulation and experiment to client. • Built model in ethanol monitoring and drunken level detection. • Project 1: Applied linear regression (proc glm) to produce a continuous line for glucose level monitoring of a patient utilizes the data collected in experiments. • Project 2: Loaded the data from the experiment result in text format using SAS data step and check for outlier in the data using proc mean and remove the irregularity in the data. Utilized logistic regression (proc logistic) try to determine whether the ethanol level is below legal limit of 0.08mg /ml within the accuracy required (0.0003% systematic error) by DADSS (Driver Alcohol Detection System for Safety). Data mining Project​​​​ ​​ 2016 • Developed an efficient model that can give accurate classification on given image (128 pixels x 128 pixels) for quality inspection of manufacturing. • Project 1: Utilized python to load in image data and utilized NN (neural network model using keras), knn( k nearest neighbor using sklearn), SVM(support vector machine using sklearn). Adopted PCA (principal component analysis) to reduce the input data size also tried feature extraction method to reduce the size of the data. Employed ensembled methods to combine algorithm to improve accuracy. Education: University of Toronto – M.Eng. (Master of Engineering) 2016 – 2018 Relevant course: machine learning; numerical method; stochastic process; project management.
Data Entry & Admin
A/B Testing
Data Analytics
Data Processing
Data Vizualization
Machine Learning
20 $
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