I am a Data Analyst skilled in Excel, SQL, Power Bi, and Python. With these tools, I make sense of large and complex data and communicate accurate findings from the data to make effective decisions. Connect with me on LinkedIn and visit my profile on kaggle.
Using Python, I performed a comprehensive Exploratory Data Analysis on Prosper loan data with 81 variables to discover the features that affect borrowers' interest rates and loan status outcomes by creating univariate, bivariate and multivariate explorations. I also used Python to design a slide presentation with explanatory plots that convey my findings.
In addition to the given WeRateDog (@dog_rates) archive data, I queried Twitter API using Python's Tweepy library to gather additional data. I wrangled the WeRateDogs data to create my analyses and visualizations.
I analyzed a fictional US Regional Sales Data that contains six (6) tables; customer, location, product, region, sales order, and sales team. I used SQL, MySQL workbench, Joins, subqueries and stored procedures to answer thirteen (13) business questions on the best-performing sales teams, the profitable products and the most valuable customers.
An "Investigate a dataset" project from Udacity Data Analysis Nanodegree. Using Python, I performed extensive Data Wrangling and Exploratory Data Analysis on 10,866 movies in the data to understand the different features and the features that drive revenue.
I analyzed twelve (12) months of trip data set, with over five (5) million rows in total, to better understand how casual riders and members differ and gave recommendations based on my findings. I used the tidyverse packages and functions like rbind, filter, mutate, select, summarize, arrange, and group by to perform the analysis.
I cleaned Nashville Housing Data with over 50,000 rows. I performed the following cleanings: filled in missing data, fixed incorrect data types, split columns and deleted invalid data using SQL and Microsoft SQL Server Management Studio (SSMS) to increase the quality of the data for analysis.
Using SQL, I recreated a project done by a popular YouTube channel with Excel. I explored the game data by answering 15 business questions using SQL and MySQL workbench.
This course helped me develop my ability to use Python to work with messy, complex datasets. I learnt how to manipulate and prepare data for analysis and to create visualizations for data exploration. I also learnt how to use my data skills to tell a story with data.
I completed extensive three months of job-ready Google Professional Data Analytics certificate training. I worked with spreadsheets, SQL, and R to organize and transform data.
Using Power BI and Excel, I distilled large and complex data into simple and concise dashboards/ visualizations. See some of my visualization projects below:
Visualization of individuals and organizations awarded Noble prizes for making outstanding contributions in six fields.
Visualization of private companies with a valuation over One billion dollars as of March 2022.
Participated in NovyPro July 2023 Data Visualization challenge on LinkedIn.
Completed the KPMG AU Data Analytics Consulting Virtual Internship on Forage.