Pandemic Study

In response to the escalating global impact of Covid-19 in recent years, this project has been initiated to extract pertinent data from publicly available sources. Subsequently, through meticulous data normalization and preparation, the aim is to establish a robust foundation for visualizing potential pandemic scenarios. Employing tools such as Excel and SQL, the processed data will be harnessed to craft insightful and informative visual representations that can aid in understanding, forecasting, and devising effective strategies for addressing future pandemic situations.

GitHub Work link

Pandemic Dashboard

Recognizing that insight forms the bedrock of valuable data, this project serves as a guiding beacon for users, enabling them to navigate the intricate journey of data transformation and visualization. By harnessing the combined capabilities of Excel, SQL, and Tableau, this undertaking facilitates a meticulous step-by-step process that culminates in the creation of an all-encompassing dashboard encapsulating the multifaceted Covid-19 landscape. The result is an enlightening and aesthetically enriching dashboard that empowers users with comprehensive insights into the dynamic Covid-19 status.

GitHub Work link Tableau Visualization

Data Cleaning

The crux of impactful data analysis rests upon the foundation of pristine and meticulously prepared data, and this project stands as a compelling testament to the art of cultivating trustworthy data through the meticulous application of extraction, comprehensive cleansing, intricate filtering, and meticulous normalization techniques. Employing the dynamic prowess of Excel for adept data collection and harnessing the precision of SQL for astute data refinement, this endeavor culminates in the creation of an exceptionally resilient dataset that serves as the backbone for informed decision-making and insightful analytics.

Input file GitHub Work link

Data Science with Scrapping Live Data from Real Website

This Python project involves scraping data from Wikipedia, specifically focusing on the world's largest companies by revenue. It employs the BeautifulSoup component from the bs4 library to extract and process data from the web page. The data is then transformed and organized using the powerful pandas library. The end result is a CSV file named 'companies.csv,' which provides a structured dataset of the largest global companies' revenue figures. This dataset can be easily utilized for further analysis and insights.

Data Scrapper: Github Code Link

Machine Learning with Gene-Gene Interaction

Within the captivating domain of gene-gene interaction lies an intellectually stimulating arena that beckons researchers to unravel the intricate threads linking genotypes with their corresponding phenotypes, and this project delves into this intricate realm with the combined power of Excel's data manipulation prowess and Python's versatile scripting capabilities, skillfully leveraging an array of machine learning and deep learning models, including neural networks, to meticulously craft a dynamic framework that illuminates the multifaceted intricacies of these genetic associations, ultimately contributing to a richer understanding of biological systems.

UCLA Presentation (under KOELLING, LIU, PHAM names) GitHub Work link

Machine Learning with Linear Regression - Predicting Stock Price

In this machine learning application, an exploration into stock price prediction was conducted using two distinct training models: standard averaging and exponential moving average. These models were implemented with the assistance of the sklearn and yfinance libraries to evaluate their effectiveness in forecasting stock prices. The project served as an opportunity to assess the correctness level of diverse training and prediction methodologies, offering valuable insights into the realm of financial data analysis and machine learning techniques, particularly in the context of stock market forecasting.

Github Colab Link

Sale Dashboard

In this Excel project, we're creating a sales dashboard by cleaning, normalizing, and analyzing customer data. With a focus on factors like age, commute distance, and income, we're transforming raw information into actionable insights. The interactive dashboard allows users to explore customer behaviors using slicer options for region, marital status, and education levels, facilitating informed decision-making for bike purchase strategies and marketing initiatives.

GitHub Work link

Auto-Classify Files

This Python project simplifies file organization by automatically categorizing and storing files into separate folders, making use of the 'os' and 'shutil' libraries. By placing the 'main.py' file in your target directory and following the embedded instructions, you can efficiently manage and sort a wide range of file types. Imagine having a cluttered folder with a mix of file formats, and now picture this project swiftly and accurately sorting them for you. It's a time-saving solution for tidying up your digital workspace.

GitHub Work link

E and T (of ETL) for indexing, filtering, and ordering big-data

This Python project delves into the heart of data science by focusing on the pivotal Extracting and Transforming (ET) stages of the ETL (Extract, Transform, Load) process. It all begins with reading a data file, but it doesn't stop there. This project offers the flexibility to index data by selecting specific fields, providing the basis for intricate data exploration. Once the data is indexed, the project's robust filtering capabilities come into play, allowing users to refine datasets based on their unique criteria. The power to sift through and select only the most pertinent records is invaluable for meaningful analysis. Whether you're working on a data science project or simply seeking to streamline data handling, this Python project is a reliable ally in your quest for actionable insights.

GitHub Work link

Sale Dashboard

In this project, I leveraged Microsoft SQL Server as the core platform to efficiently manage data. I set up SQL Server Reporting Services (SSRS) and utilized Azure Data Studio to normalize before importing external data sources. My focus was on data recovery, ensuring data integrity throughout the process. Leveraging SSRS, I executed a robust Extract, Transform, and Load (ETL) process with Azure to transform raw data into a comprehensive Revenue Report. The final step involved deploying this sub-report onto the local report server, integrating it with the real-time database from SQL Server, thus enabling real-time insights and reporting capabilities.

Working link

Real-time Hospital's Dashboard

Engineered within the versatile framework of Google Sheets, the Volunteer Activities Dashboard project orchestrates a sophisticated symphony of data extraction, transformation, and loading, seamlessly harmonizing intricate volunteer activity data streams that encompass a plethora of attributes spanning shifts, departments, time slots, and volunteer types, and culminating in the creation of a dynamic and interactive dashboard that not only delivers real-time insights through meticulously designed scorecards but also imparts deeper comprehension through an ensemble of visually captivating charts and graphs, thus laying the foundation for enhanced volunteer program monitoring and optimization by streamlining the complex processes of data management, transformation, and visualization within organizational contexts.

Google Sheet Working link

Full-Stack Software Development

Employing SQLite as the backend with Django and Python loading and fetching data to the front end as a complete website, our Hotel Management System effectively stores and manages comprehensive hotel rental customers and rooms data. The project's foundation takes shape on draw.io, meticulously sketching the database model to ensure optimal structure. This is a full project scome from requirement collecting and analyzing to designing interface, database, implementing and testing with Agile methodologies. The user interface design then comes to life through Balsamiq, offering a user-centric experience that complements the database's prowess. Seamlessly integrating these tools, the project delivers a holistic solution that streamlines data storage, management, and user interaction.

Database model Mockup interface Test Specification Description Usecase vs Testcase ft. Tracing Matrix Github Working link

Data Analytics: Professional Insights

In our data analytics project, we utilized the power of Microsoft Power BI to extract, transform, and load data from diverse sources, enabling us to gain valuable insights into professional careers based on an array of demographic and economic factors. This project exemplified the full scope of a data analytics endeavor, encompassing rigorous ETL procedures to cleanse and prepare data for analysis. The culmination of our efforts is a sophisticated and dynamic dashboard presented in the Power BI format, offering stakeholders an intuitive platform to explore and understand the intricate dynamics of professional careers in a visually compelling manner.

Professional Breakdown Github Work Link

Data Visualization @ Automobile Sale During Recession and Non-recession

This Python project focuses on the extraction, transformation, and visualization of historical automobile sales data, with a specific emphasis on differentiating between recessionary and non-recessionary periods. Leveraging powerful libraries such as Seaborn, Dash, and Matplotlib, the project offers an insightful perspective into the patterns and trends within the automotive industry during economic downturns. By meticulously processing and loading the data, it generates a variety of visualization views that provide a comprehensive understanding of how car sales are affected by economic cycles. This project not only demonstrates the capabilities of data analysis in Python but also offers valuable insights for stakeholders in the automobile sector.

Github Work Link