Wendel Burlat
Data Analyst
Backgound and Data Journey
Aspiring Data Analyst with a strong academic background in physics and a passion for extracting meaningful insights from data. Proven experience as a Math Content Editor for 4 years demonstrates meticulous attention to detail and analytical thinking, making for a seamless transition into the data industry.
Proficient in Python and Tableau, equipped with the ability to perform data analysis, create data visualizations, and solve complex problems. Committed to leveraging my skills and knowledge to drive data-driven decision-making and contribute to the success of dynamic data teams.
Food Delivery Time Prediction
Our primary aim is to develop a robust model for predicting delivery times, leveraging features such as delivery person age, ratings, vehicle type, and the distance between the restaurant and delivery location. We seek to optimize delivery routes, improve operational efficiency, and enhance customer satisfaction by tailoring delivery time estimates to different customer segments.
E-Vehicle Population Data Analysis Project
The analysis aims to provide a comprehensive understanding of electric vehicle adoption and characteristics, contributing to informed decision-making in the context of electric vehicle policies, infrastructure development, and environmental considerations.
NEO Visualization Data Project
The project aims to visualize and analyze data related to Near-Earth Objects (NEOs). NEOs are celestial objects, such as asteroids and comets, that have orbits that come close to Earth. Understanding NEOs is crucial for planetary defense and scientific research. This project seeks to provide informative visualizations and insights into NEO characteristics, including their orbits, sizes, and close approaches.
Movie Analysis Project
This project aims to analyze and gain insights from a dataset containing information about movies. It utilizes Python, various data analysis libraries, and data visualization techniques to explore the dataset and answer questions related to movie revenue, budgets, ratings, and more.
Loan Analysis Project
This project is dedicated to exploring various aspects of loans, including applicant profiles, credit scores, interest rates, and income levels. By harnessing data analysis and visualization, the project seeks to provide a detailed understanding of factors influencing loan approval decisions.
Sales Analysis Project
This project is dedicated to gaining insights into various aspects of sales, including revenue, costs, profits, and key performance indicators (KPIs). By leveraging data analysis and visualization, the project aims to provide a detailed understanding of sales trends, identify growth opportunities, and optimize business strategies.
Sentiment and Keyword Analysis Project
The Sentiment and Keyword Analysis Project is a data analysis endeavor that focuses on understanding sentiments and identifying keywords in textual data, such as articles or blog titles. Using Python, NLTK (Natural Language Toolkit), and VADER (Valence Aware Dictionary and sEntiment Reasoner), this project delves into sentiment analysis and keyword identification to extract valuable insights from text data.
Analyzing Titanic Passenger Data
The sinking of the Titanic is one of the most well-known maritime disasters in history, and the passenger data from that tragic event has provided valuable insights into survival factors. In this data analyst project, we will dive into the Titanic passenger dataset to analyze and uncover patterns, correlations, and insights that shed light on the factors that influenced passenger survival.
Global Happiness 2019 Project
This data analysis project aims to explore and understand the factors influencing global happiness. Using the World Happiness 2019 Report dataset, we will investigate the relationships between various socio-economic, environmental, and cultural factors and the happiness levels of different countries or regions. The goal is to gain insights into what contributes to happiness on a global scale and provide meaningful recommendations based on our findings.