Emmanuel
Chude
Email Address
LinkedIn Profile
Data Analysis Portfolio link: Github & Tableau
Entry-level Data
Analyst
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Results-driven Junior Data Analyst with a strong foundation in
Summary
data analysis and visualization. Proficient in leveraging statistical
techniques, data manipulation, and programming skills to extract
actionable insights from complex datasets. Adept at using R programming language, SQL, Excel, Google Sheets, and Tableau
tools to analyze trends, identify patterns, and present findings in
clear and concise reports. Ability to collaborate effectively in
cross-functional teams, communicate technical concepts to
non-technical stakeholders, and contribute to data-driven
decision-making. Seeking to apply analytical expertise and
contribute to data-driven strategies in a dynamic and
growth-oriented environment.
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Experience
Project: COVID-19 Data Analysis and Visualization
using Excel, SQL and Tableau
June, 2023
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As part of an independent initiative, I undertook a project
focused on analyzing and visualizing COVID-19 data
sourced from the CDC website. By leveraging my skills in
data analysis, statistical techniques, and data
visualization, I aimed to uncover insights related to the
pandemic's impact.
COVID-19 Impact Analysis: Analyzed global COVID-19
data, calculating death percentages, and assessing
prevalence in the United States population.
Country-Specific Analysis: Identified countries with high
infection rates and severe death impacts, considering
population proportions.
Continental Impact Insights: Explored COVID-19 death
rates across continents, highlighting regions most
affected.
Global Trends Overview: Visualized global new cases
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trends, revealing the pandemic's evolution over time.
Comprehensive Global Overview: Summarized global
COVID-19 statistics, showcasing total cases, deaths, and
death percentages.
Project: Analysis on the famous Titanic Dataset
using Excel, SQL and Tableau
July, 2023
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Soft Skills
Survival Demographics and Rates: Explored passenger
demographics and survival rates using the train_data
table, Separated data for passengers who survived
(Survived = 1) and those who didn't (Survived = 0).
Gender-Specific Analysis: Analyzed gender-specific
patterns in survival and fatality using the train_data
table, Calculated percentages and averages for male and
female passengers.
Age and Survival Correlation: Explored the correlation
between age and survival outcomes for both men and
women, Calculated average ages of surviving and
deceased passengers.
General Survival Trends: Examined overall survival trends
and fatality rates across the entire dataset, Calculated the
general percentage of survivors and deceased individuals.
Comparative Analysis - Sex and Survival: Compared
survival percentages between male and female
passengers, Calculated gender-specific survival and
fatality rates.
Continent-Specific Analysis: Investigated the dataset
from a continent-specific perspective, Analyzed survival
and fatality rates based on passengers' continental
origins.
Creativity, Adaptability, Excellent Work Ethics, Communication,
Time-management, Problem Solving, Attention to Details,
Interpersonal Skills, Good relationship with Co-workers and
stakeholders.
Hard Skills
Statistical Analysis, Data Visualization (Tableau and
Spreadsheet), Reporting tools (R Markdown, Google Data Studio,
Looker), Data Analysis, Spreadsheets (Excel Sheets, Google
Sheets), SQL Queries, R-programming language.
Education
Certificate in Google Data Analytics
Coursera, [Oct, 2022 - May, 2023]
