Data Analyst

Luying Lou

240-***-**** ●**** Cherokee St, College Park, MD 20740 ● ******.***@*************.***.*** https://www.linkedin.com/in/luying-lou-257493197/

Data Analyst

EDUCATION

University of Maryland, Robert H. Smith School of Business College Park, MD, USA Master of Science in Business Analytics, GPA: 3.8/4.0 December 2020 SKILLS

• Programming – Python, R, Scala, SQL, C++, HTML, JavaScript

• Analytics & Tools – Tableau, RStudio, Apache Spark, Google Analytics, Snowflake, Excel

• Databases – MySQL, Microsoft SQL. • Systems: Windows, Linux PROJECT EXPERIENCE

Web-based System for House Rental Recommendation (SQL, Python, Tableau)

● Coded Python web crawler to capture information of apartments from website; Integrated our own SQL Server database with those data; Generated SQL structured query to compare apartments features such as prices, facilities and transportation, and visualized results by creating interactive dashboards with Tableau. Finally built an informative rental website interface.

Analysis of Features of Top Trending Videos in YouTube using Python

● Conducted a lot of data cleaning work such as dealing with invalid values and outliers, text processing and data type converting; Analyzed the relationship between number of views and factors like publish time, categories and countries, using Python packages such as matplotlib, seaborn and plotly; Applied text analysis on the comments and tags of trending videos, extracting some key words that show people’s tastes and preference. Generated actionable strategies for YouTube video content creators. Sales Forecasting of Products Sold by Walmart (Python)

● Compared average sales by weeks, months or years to identify seasonality or periodicity; Conducted analysis of sales for stores in different states, comparing average sales revenue and different prices distributions of similar products; Forecasted the sales of each item for the next 30 days based on predictive models, helping sellers better manage cash flow and inventory.

Analysis of Reasons Contributing to Hotel Booking Cancelling (AWS, Python, PySpark)

● Applied machine learning algorithms, such as random forest, logistic regression, neural network and XGboosting, to train models and get important features causing booking cancelling; Leveraged AWS and Apache Spark for storing big datasets as well as increasing data processing efficiency; Forecasted cancelling risk of bookings to better manage customers’ orders and also better predict revenue. WORK EXPERIENCE

Udhyam Learning Foundation Maryland, United States Business Analyst Intern November 2019 – March 2020

● Coordinated with a consulting team to analyze sales across different geographic areas to reach out to potential clients for a solar product, as part of a non-profit solar project targeted at micro-entrepreneurs.

● Constructed models using Python to predict clients default probability based on their income and credit history. Increased the accuracy and efficiency of identifying customers with high potential default risk.

● Conducted supply chain analysis for the company to find optimized network, reorder point and order quantity, taking into account factors such as demands, capacity of warehouse, costs of shipments, storage costs and so on. Baicizhan App Technology Company Chengdu, China

Product Analyst April 2019 – August 2019

● Analyzed users’ reviews of our app product by using natural language processing and sentiment analysis. Extracted some key words they talked about so that Product team could gain insights of users’ preference and provide new services to users. Increased enrollment rate of the English learning App by five percent.

● Leveraged web crawler technique to analyze factors that influenced the ranking of our app in search engines; Tracked channels that drive users into website and their browsing behaviors on our website; Generated reports to give content creator team some advice on designing product web page, which helped increase conversion rate.

● Proposed evaluation indicators for educational technology sector, benchmarking products of 10 local competitors.

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