My name is Ivy Wu. I am a data-savvy person and also a postgraduate student who is currently studying MSc Business Analytics at the University of Bath School of Management. I was a project management professional and started my journey as a data analyst since 2021!
I am interested in storytelling with data and actively look for ways to understand what data is. I have always believed that data analytics is important for every business by uncovering hidden patterns and insights - and probably get every answers from it 💬.
👉 Click to see my skill sets
- R (forecast, tidymodels, ggplot2, Tidyverse, dplyr data analysis package)
- Python (Data analysing with Pandas module)
- VBA/User Forms/Pivot Table
- SPSS (Descriptive Model, Regression, ANOVA, Decision Tree Modelling)
- SAS E minor (Regression, Decision Tree, Neural Network Modelling)
- Arena (Simulation Modelling)
- Tableau (Visualisation with interactive dashboards)
- IBM Cognos Analytics (Visualisation and forecast with interactive dashboards)
- Microsoft SQL Server/ PostgreSQL (Subqueries, Aggregations, Conditional Statements, Data Manipulation)
- Microsoft Access (Data manipulation with SQL, Report)
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2013 NYC Flight Departure Delay Analysis Utilised R and Tableau dashboard to visualize the New York city flight dataset in 2013 and gain insights regarding how to improve departure delay, which is one of the most addressed problems faced by all stakeholders within the supply chain of air travel. |
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Exploratory Data Analysis With Visualizations (ggplot2) Created data visualizations with ggplot2 in R to reveal the initial patterns and characteristics of a dataset. The process of exploration involves identifying the presence of outliers, the distribution of data values, the structure of the dataset and the relationships between different data variables. |
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K-means Clustering to understand naturally occurring clusters among smartphone users Built K-means clustering models (unsupervised machine learning) in R to uncover subpopulation structures in the data. This analysis is powerful in extracting insights from data and aims to facilitate a deeper understanding of smartphone users. |
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Random Forest Classification to predict smartphone users' gender Utilised tidymodels in R to build Random Forest Classification (supervised machine learning) models to understand and predict the future customer behavior trends. |
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Batch time-series forecasting with cross validation Utilised cross-validation, automatic ETS and ARIMA modelling in R to batch forecast 130 time-series with error evaluations and model selection strategy. |
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Manual time-series forecasting Utilised R and ggplot2 to generate time series forecasting with regression, ETS and ARIMA models on food glass containers shipments data from January 1981 to June 1991. |
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Ford Fulkerson Algorithm for Maximum Flow Problem Utilised VBA and user forms in Excel to create a decision making tool to solve the maximum flow problem in a railway network system. |