Data Analyst, located at ShangHai. Both Science of Society and Engineer Background. On the way to the Data Scientist journey.
1. Regression Analysis
2. Time Series Analysis
OLS, Logit Regression, Propersity Score Matching.
Vector autoregression, Autoregressive integrated moving average.
3. Classfication Problem
Decision Tree, Random Forest, K-Nearest Neighbors, SVM, K-Means, Gradient Boosting algorithms.
4. Deep learning
CNN, RNN, GAN.
5. Build up analysis loops
Text mining, Web crawling, Data cleansing, Automation Reports, DashBoard, etc.
(magrittr, knitr, flexdashboard, highcharter, tm, pdftools, RMariaDB, ROracle, shiny, etc.)
Learning Deep Learning, inclouding CNN, RNN, GAN, etc.
(keras, tensorflow, lsuv_init, django, etc. )
Oracle database, MariaDB, SQL server.
Excel VBA, C#, STATA, SAS, SQL
1. Develop programs to automate daily reports.
2. Based on the daily report, co-work with front-end engineer to deploy the interaction report system.
3. Planning the data science projects to achieve the goal of intelligence factory.
4. Learning the data science fields knowledge and application, include AI, IOT, algorithms, etc.
Highlight the low performance machine by R and hclust method and response to the test engineer.
Review the machine behavior daily and the maintenance results weekly.
Significantly reduce the test cost about 15%~20% at this stage.
Collecting MORE data such as the maintenance diary and other useful data.
Make the maintenance suggestion to the test engineer automatically.
Combine the Dashborad project.
Design real time data cleanse process, data storage structure and dashboard interface.
Fix issues from users.
Training a Regression Tree by R and CART package.
Pick out the high purphase potential customers for the department of catalogue.
Reduce the mail cost in half and keep the same sales revenue.
GPA: 4.34 / 4.5