Position: Junior Data Analyst
Category: Data Analyst
English level: upper
Years of experience: 0

Work History

• English/Spanish Teacher at Private School (2 years of experience)

• Customer Service Agent (Promoted to Team Leader of Customer Support Department)
Online-store of gold bars, Belize City (5 years of experience)

• ESL Kindergarten Teacher in China, Beijing (1,5 year of experience)

• English to Spanish Interpreter (Self-Employed)
Projects
• Data Preprocessing Building Permit Project
Goal: preprocessing for further analysis and modeling by handling missing values, data type conversions, and other necessary adjustments. Techniques: imputing missing descriptions (Mode imputation), Unavoidable Missing Data (datetime was changed to NaT), Forward filling, Mean imputation, Non-informative Value imputation.

• Bike Path Usage Analysis and Prediction Project (Poisson Regression)
Goal:  predict the number of riders on one path based on the number on another path using Poisson Regression. Techniques: EDA, Correlation Matrix and Feature Selection, Handling outliers (Outlier Capping), Data splitting into training and testing sets, Model Building and Evaluation, Model Visualisation, RMSE.

• Predicting #cakeweek Project (Logistic Regression) 
Goal: predict whether a recipe was part of #cakeweek based on other recipe features. Techniques: Filtering data to find correlations, EDA, Finding Correlated Features, Data splitting into training and testing sets, Model Building and Evaluation, Modification of predictions to improve model accuracy.

• Water Salinity Prediction Project (Linear Regression)
Goal: predict the water salinity based on its temperature. Techniques: Feature Selection, Sampling, Correlation Plotting, Simple Linear Regression Model, Model Visualisation, Model Evaluation with R^2 score.

• Adidas US Sales EDA Project
Goal: analyse the sales data to identify trends and patterns. Techniques: Data Preparation, Correlation Matrix, EDA, Trend Analysis, Growth Rate, Visualisation (Pie charts, Line Charts, Bar Charts), Simple Linear Regression, Reshaping time series to 2D array, Model Building and Prediction, Plotting Predictions vs actual data and fitting a line, Data Export, Dashboard in Tableau.

• Covid-19 (SQL)
Goal: extract valuable insights from a COVID-19 dataset by employing SQL queries and leveraging Microsoft Azure's data management capabilities. Techniques: Retrieving data from table, Filtering, Using aggregate functions and Grouping, Joining the covid_deaths and covid_vaccinations tables to analyze vaccination statistics, Creating temporary tables and views.

• Web Scraping Multiple Pages on Amazon
Goal: scrape data from the Amazon Best Sellers page for Kindle eBooks. Techniques: HTTP requests, Parsing the HTML content and extracting relevant information, Reading and writing data to CSV, read the data from the CSV file and create a DataFrame, Writing test cases.