Quinton Goodman

Data Analyst

Excel | SQL | Python | Tableau

Welcome to my website! I'm a data analyst with a passion for using data to drive positive change. Here, you can explore my portfolio of work, learn about my journey, and connect with me to unlock valuable insights together. Let's turn numbers into meaningful stories and make a difference!

CERTS & COURSES

Course	Skills Learned	Tools Used	Time Commitment	Status
Google Data Analytics Certificate \|\| Coursera & Google	Data analytics, spreadsheets, SQL, Tableau, R, data aggregation, data cleaning, data calculations, data ethics, data visualization, presentations, problem solving, structured thinking	Google Sheets, SQL, Tableau, Excel, R, Big Query	6 Months	Completed
Data 6300 \|\| MTSU Data Science, Graduate Certificate Program	Data wrangling, exploratory data analysis (EDA), data visualization, descriptive and inferential statistics, machine learning concepts, and database management.	Python, Excel, Jupiter Notebook	7 Weeks	Completed
Data 6310 \|\| MTSU Data Science, Graduate Certificate Program	Data profiling, data cleaning, data visualization, dimensionality reduction, clustering techniques, and association rule mining.	Python, Excel, Jupiter Notebook	7 Weeks	Completed

Portfolio

In my portfolio, you'll find a collection of data analysis projects that showcase my skills, creativity, and problem-solving abilities.

Excel | Dynamic Dashboard Displaying Road Accident Data
Python & Tableau | Dashboard Displaying London Bike Rides Data
SQL | Walmart Sales Analysis

About Me

Hey there! I'm a data professional who's all about uncovering insights, making smart decisions, and driving real change. With a background in education and a newfound love for data analytics, I bring a fresh perspective to the field.My journey into data analytics started with a burning curiosity to unravel the stories hidden within the numbers. I jumped into the Google Data Analytics certificate course and got hands-on with data analysis, visualization, and interpretation. It was an eye-opening experience that set me on the path to a career in data.What really gets me pumped about data analytics is its power to turn raw data into actionable insights. I thrive on diving deep into datasets, spotting patterns, and transforming complex information into visuals that tell a compelling story.When I'm not busy crunching numbers, you can find me indulging in my passion for reading and movies. They're my escape and a great way to broaden my horizons. I'm always up for learning new things and staying on top of the latest developments in data analytics.Looking ahead, my goal is to bring together my diverse background, data skills, and passion for making things better. I want to be part of a collaborative team where we can turn data into actionable insights that drive real impact.Let's connect and explore how we can harness the power of data to make a difference. I'm excited to team up with fellow data enthusiasts and create a world where decisions are driven by solid insights.

Excel | Dynamic Dashboard

Project Overview

In this project, I developed an Excel Road Accident Dashboard to analyze and visualize traffic accident data for the years 2021 and 2022. The aim was to gain valuable insights that can shape road safety policies and reduce casualties resulting from accidents. Through this project, I showcased my expertise in data analysis and visualization using Excel.

Key Features

Interactive dashboard presenting key insights on road accidents and casualties
Data filtering options by accident dates (month and year) and rural/urban setting
Visual representations of data using various charts, including bar charts, donut charts, tree maps, and line charts
Custom number formatting to highlight the magnitude of total casualties

Methodology

Utilized a dataset in XLSX format with 21 fields and 32,000 rows obtained from Kaggle
Converted the data into a table format for efficient filtering and sorting
Performed data cleaning by checking for null values and correcting typos
Extracted month and year information from accident dates using Excel functions
Generated pivot tables to analyze primary and secondary Key Performance Indicators (KPIs)

Identified trends and patterns in accident severity and vehicle types involved
Examined casualties by different vehicle types and their contributions to total casualties
Analyzed monthly comparisons of casualties between the current and previous years
Determined maximum casualties by road type and distribution of casualties by road surface
Explored the relationship between casualties by area/location and day/night periods

Impact

The Excel Road Accident Dashboard provides essential information to stakeholders, including the Ministry of Transport, Road Transport Department, Police Force, Emergency Services Department, Road Safety Corps, Transport Operators, Traffic Management Agencies, Public, and Media. These insights can assist in shaping road safety policies, allocating resources effectively, and implementing targeted interventions to reduce casualties and enhance road safety.

Insights

I am excited to share this project and discuss its implications with professionals in the field. Feel free to explore the interactive dashboard and reach out to me for further information, collaboration, or any insights you may have. Let's work together towards safer roads and a brighter future

Python & Tableau | Dashboard London Bike Ridest

Project Overview

This project is an end-to-end data analyst portfolio project that demonstrates the process of gathering, exploring, manipulating, and visualizing data. The data used in this project is the London bike sharing data set, which is publicly available on Kaggle

Key Features

Data gathering: The project uses Python libraries to programmatically download the data set from Kaggle.
Data exploration and assessment: The pandas library is used to explore and assess the data, including checking for missing values, identifying data types, and calculating summary statistics.
Data manipulation: The data is manipulated using pandas to clean and prepare it for visualization. This includes renaming columns, handling missing values, and creating new calculated fields.
Data visualization: Tableau is used to create five visualizations of the data:
Total number of bike rides
Moving average of bike rides
Heatmap of temperature vs. wind speed
Number of bike rides split by weather and hour (in tooltip)
Number of bike rides split by hour (in tooltip)
User-defined parameters: The dashboard includes user-defined parameters that allow users to control the moving average period.
Set actions: Set actions are used to link the moving average chart to the other visualizations on the dashboard so that selecting a different time period on the moving average chart will update the other visualizations accordingly.

Methodology

Data gathering: The data is downloaded from Kaggle using the kaggle API.
Data exploration and assessment: The data is explored and assessed using pandas.
Data manipulation: The data is manipulated using pandas to clean and prepare it for visualization.
Data visualization: Tableau is used to create the visualizations.
Dashboard design: The visualizations are arranged into a dashboard and interactive features are added using set actions and user-defined parameters.

Insights

The project provides insights into the patterns and trends of bike sharing in London. For example, the moving average chart can be used to see how the number of bike rides changes over time, and the heatmap can be used to see how the number of bike rides is related to temperature and wind speed.

Impact

This project can be used as a portfolio project for data analysts to showcase their skills in data gathering, exploration, manipulation, and visualization. It can also be used as a learning resource for people who are interested in learning how to use Python and Tableau for data analysis.

View DashBoard

SQL | Walmart Sales Analysis

Project Overview

lThis project explores Walmart sales data using SQL queries to gain insights into top-performing branches, products, sales trends, and customer behavior. The data was obtained from the Kaggle Walmart Sales Forecasting Competition. It aims to identify areas for improvement and optimization of sales strategies.

Methodology

This project leverages SQL queries to analyze the Walmart sales data stored in a relational database.

Data Wrangling and Cleaning:

The Walmart sales dataset is imported into a relational database management system MySQL.
The data is organized into appropriate tables with relevant columns.
The data is inspected for missing or null values.

Feature Engineering:

New features are generated from existing columns to enhance analysis:

timeofday:Categorizes transactions into morning, afternoon, or evening.
dayname: Extracts the day of the week from the transaction date.
month_name: Extracts the month from the transaction date.

SQL Queries:

SQL queries are written to answer the key questions listed above.
Queries may involve aggregation functions, joins, filtering, and grouping to extract meaningful insights from the data.

Insights

The results of the SQL queries are analyzed to identify patterns and trends in the data.
Reports are generated to summarize the findings and present them in a clear and concise manner..