As a Data Analyst in Harwood Acoustics, I employ my skills, experiences, and technical expertise to achieve outcomes for the company. I am deeply passionate about the diverse data analysis work I am currently engaged in because it allows me to uncover meaningful insights, identify patterns, and contribute to informed decision-making, fostering my enthusiasm for transforming raw data into valuable knowledge. I am confident that I can add significant value as a Data and Business Analyst.
Postgraduation (Data Modelling & Research), University of Wollongong, 2022
Google Data Analytics Professional Certificate, Google, 2024
Microsoft Power BI Desktop for Business Intelligence, Udemy, 2024
Tableau 2024 A-Z: Hands-On Tableau Training for Data Science, Udemy, 2024
The previous and current projects by employing the data analysis and visulizing tools e.g., SQL, Power BI, Tableau, Python, and excel, are listed below
I was tasked with analysing age pensioner data, including family situation, disability, special needs, hospital admissions, social housing, and crime numbers, in Auburn and Bankstown within the larger Sydney area. The main challenge was to accurately separate the Auburn and Bankstown pensioner data from the broader Sydney council area, clean it, and analyse it using advanced data processing techniques. I needed to provide actionable recommendations to improve living standards for these pensioners. Using Python and SQL, I cleaned and processed large datasets, ensuring accurate segmentation of Auburn and Bankstown data. I developed efficient queries and algorithms to isolate pensioner data from the greater Sydney dataset. I then visualized key insights to identify patterns, trends, and outliers, addressing critical issues such as physical disabilities, psychological conditions, and living conditions. Through advanced data handling techniques, I built comprehensive profiles of the pensioners and identified areas for improvement in their living standards.
My analysis led to several important recommendations, including enhancing disability support (for 13.9% with physical disabilities), addressing psychological and cancer-related healthcare (for 3.4% and 0.7%, respectively), focusing on preventive care for the majority (80.4% with no special needs), improving respiratory healthcare (for 17% of emergency presentations) see the link (Age Pensioner Clients Living In Auburn & Bankstown LGA) and developing targeted housing support for single pensioners with children (83.3%)(Age Pensioner Clients Living In Auburn & Bankstown LGA). These recommendations were well-received and formed the basis for actionable plans to improve pensioner living conditions in these areas. This project demonstrates my advanced skills in using Python and SQL to handle complex data processing, analysis, and visualization tasks. The outcomes are:
I led a Power BI project for a global cycling equipment manufacturing company, aiming to develop a dynamic and interactive dashboard to track the company KPIs(sales, revenue, and profit), geospatial analysis, and product level analysis. The client sought to gain actionable insights with time into their sales performance across diverse markets and product lines. The objective was to develop a comprehensive Power BI dashboard that could aggregate, visualize, and analyse sales data to identify key trends, patterns, and performance indicators with real-time.
I began by gathering data from various sources, including sales databases, CRM systems, and financial records. The sample of uncleaned raw data is shown in the following link Sample of Sales in 2020-2022. The tables pen in the RHS of the Sample of Sales in 2020-2022 dashboard, are indicating various product information, return information, customers information, and region information. These raw data is then cleaned, transformed, and modelled the data to ensure accuracy and relevance, see the link Samples of Sales Data Cleaning. The data set has been modelled using the STAR Schema, see the data modelling ground work link
Data modelling employing the STAR Schema
Using Power BI’s advanced analytics and visualization features, I created interactive dashboards and reports, incorporating slicers, filters, and dynamic visuals to provide a user-friendly experience. I then implemented measures to track sales, revenue, and profit metrics, enabling real-time monitoring and benchmarking against targets. The Power BI solution empowered the client to make data-driven decisions, optimize sales strategies, and enhance overall performance. With clear insights into KPIs, the company achieved improved profitability, streamlined operations, and gained a competitive edge in the global market. Using DAX with M code, the sample of few analysis are given as below.
-DAX formula in M code for calculating the Total Quantity Sales
-Drilling down for understanding the sales information
-Sample of Data Hierarchy and M code for the calculation of Product Return Rate
-Dynamic and interactive dashboard:Orders,Profit,Revenue,Return Rate of the product
-Dynamic and interactive dashboard: KPI based on the geographical area
-Business performance dashboard with time
-Dashboard of coustomers details
For data security and integrity purposes, some crucial results from the project have not been shared in this document. This precaution ensures the protection of sensitive information and maintains the integrity of the data throughout the project’s lifecycle.
Project Overview: This project showcases a sales analysis dashboard built using Tableau. The analysis focuses on the total sales performance of various sales representatives across different regions. The purpose of this project is to provide insights into the sales distribution by region and by representative, helping stakeholders identify top performers and areas that may need improvement.
Objective: The primary objective of this project is to analyze the total sales figures across different regions and sales representatives. This analysis aims to: -Visualize the total sales by region and representative. -Identify top-performing representatives in each region. -Highlight regions or representatives with lower sales figures for potential performance improvement. -Provide a comprehensive view of sales distribution across the company.
Data: The data used in this project includes: -Item: The product being sold. -Order Date: The date when the order was placed. -Region: The geographical area where the sales were made (Central, East, West). -Rep: The sales representative responsible for the sales. -Total Sales: The total sales amount for each transaction. -Unit Price: The price per unit of the product sold. -Units: The number of units sold.
Dashboard Features: -The Tableau dashboard consists of the following key features: -Bar Chart Visualization: A bar chart representing total sales by sales representative, segmented by region. This visualization helps in identifying the top sales reps in each region. -Color Coding: Different colors are used to represent different sales representatives for easy identification. -Interactive Filters: The dashboard allows users to filter data by specific regions or representatives, making it easier to focus on particular segments.
Insights From the Analysis: -The East region shows the highest sales performance, with Susan being the top sales representative. -The Central region also shows strong performance, particularly by Matthew. -Some regions have sales representatives with lower sales figures, indicating areas where performance improvement strategies may be needed.
Conclusion: This Tableau dashboard effectively visualizes the distribution of sales across regions and representatives, providing valuable insights for decision-makers. The interactive nature of the dashboard allows users to explore the data in various ways, leading to more informed business strategies.
Project Description:
This Tableau project (Dashboard of USA Unemployment Trends by Age Group) visualizes the trends in unemployment across different age groups in the USA, highlighting how various demographic segments were impacted over time. The visualization, presented as a stacked area chart, breaks down the total number of unemployed individuals into age groups such as “20 to 24 years”, “25 to 34 years”, “35 to 44 years”, “45 to 54 years”, “55 to 64 years”, and “65 years and over”. The data reveals significant fluctuations in unemployment, particularly around major economic events, with younger age groups experiencing more volatility. The chart effectively shows the cumulative impact of unemployment across age demographics, allowing for easy comparison and historical analysis. This project provides valuable insights for policymakers, economists, and researchers, offering a clear visual representation of how unemployment has evolved across different age categories in the USA. The visualization can be used to inform decisions regarding economic policies and workforce development strategies.
I have been given a set of uncleaned movies data (Movie_data) to clean and filter. The data has been then cleaned and filtered acording to the type of movie (filtered action movie) release date, and revenue (filtered Comedy movies which sequered revenue above 300000000). The cleaned data was then used to visulize based on the genere ,i.e.,drama, action, and comedy (Sample of Movie Bar Chart Based on Genre and Gender).
