Data Acquisition, Exploration, Visualization & Analytical Ranking | UMBC Graduate Studies | 2023
This repository contains Python-based data-analysis work developed during my graduate studies at the University of Maryland, Baltimore County (UMBC) in 2023.
The notebooks collectively demonstrate several stages of a practical analytical workflow, including data acquisition, foundational analysis, exploratory data analysis, visualization, and ranked or Top-N analysis.
Historical project note: The notebooks represent original graduate-level work from 2023. This documentation was added later to improve clarity, reproducibility, and technical presentation while preserving the original project history.
The repository can be understood through a general analytics lifecycle:
External / Raw Data
↓
Data Acquisition
↓
Data Preparation
↓
Exploratory Analysis
↓
Analytical Transformation
↓
Visualization
↓
Ranking / Top-N Analysis
↓
Interpretable Insights
| Notebook | Analytical Area |
|---|---|
Web Scraping.ipynb |
Programmatic acquisition of information from web sources |
Basic Analysis.ipynb |
Foundational analytical operations |
Data Exploration.ipynb |
Exploratory data analysis |
Plotting.ipynb |
Data visualization |
Top N.ipynb |
Ranking and Top-N analytical analysis |
Data analysis begins with access to usable information. This notebook explores programmatic web-data acquisition and the process of converting information from external sources into structured data for subsequent analysis.
Conceptually:
Web Source
↓
Retrieval
↓
Content Parsing
↓
Structured Representation
↓
Analytical Dataset
Web-data acquisition introduces practical considerations such as source structure, changing page formats, missing information, parsing logic, and responsible access practices.
This notebook covers foundational analytical operations that support more advanced exploratory work.
Basic analysis helps establish characteristics such as:
- Dataset structure
- Available attributes
- Data types
- Summary information
- Data-quality characteristics
- Initial patterns
Exploratory Data Analysis (EDA) is used to understand data before making stronger analytical or predictive assumptions.
EDA can help identify:
- Distributions
- Missing values
- Unusual observations
- Relationships between variables
- Potential analytical questions
- Data-cleaning requirements
The notebook demonstrates the role of exploration as a bridge between raw data and more structured analysis.
Visualization converts analytical results into forms that are easier to interpret.
Plots can help reveal:
- Trends
- Comparisons
- Distributions
- Outliers
- Relationships
- Concentration patterns
Visualization is therefore both a communication technique and an analytical tool.
Ranking is a common analytical requirement in business and operational environments.
Top-N analysis can help identify:
- Highest-ranked categories or observations
- Most frequent items
- Largest contributors
- Priority areas
- Outliers
- Candidates for deeper investigation
This notebook represents the progression from detailed observations toward prioritized analytical outputs.
Taken together, the notebooks illustrate a broader analytical pattern:
Acquire
↓
Understand
↓
Prepare
↓
Analyze
↓
Visualize
↓
Prioritize
↓
Communicate
Although analytical tools evolve, this underlying process remains relevant to modern data and decision-support workflows.
The repository provides historical evidence of foundational experience across areas including:
- Python
- Jupyter Notebook
- Data acquisition
- Web scraping
- Data exploration
- Exploratory Data Analysis
- Data transformation
- Analytical ranking
- Top-N analysis
- Data visualization
- Structured analytical workflows
An important progression in analytics is moving beyond producing data toward identifying information that is relevant to a decision.
Conceptually:
Raw Records
↓
Analysis
↓
Patterns
↓
Prioritized Information
↓
Decision Support
Exploration, visualization, and ranking can help reduce large volumes of information into more interpretable outputs.
These foundations are relevant to later work involving enterprise analytics, automation, predictive methods, and AI-assisted decision support.
Programmatic web-data acquisition should account for applicable:
- Website terms and conditions
- Access policies
- Copyright
- Privacy requirements
- Rate limits
- Data-protection requirements
- Appropriate use of collected information
The web-scraping notebook should be understood as a technical learning exercise rather than authorization to collect information from arbitrary external systems.
This repository represents graduate academic exercises rather than a deployed production analytics platform.
Accordingly:
- Individual notebooks may use different datasets or analytical scenarios.
- The repository does not claim enterprise deployment.
- The repository does not claim commercial adoption.
- The repository does not claim external impact or recognition.
- Results should be interpreted in the context of the original academic work.
The notebooks preserve the original analytical implementations.
Because the work originated in 2023, external web pages, datasets, APIs, libraries, or package versions used by the notebooks may have changed.
Exact dependency and data-source instructions should be added only after they are verified from the original notebooks.
Developed during graduate studies at UMBC in 2023, this repository documents foundational work in data acquisition and analytical problem solving.
It forms part of a broader technical progression:
Data Acquisition
↓
Data Analysis
↓
Visualization
↓
Distributed Analytics
↓
Machine Learning
↓
Enterprise Automation
↓
AI-Assisted Decision Support
This repository is maintained for educational, portfolio, and technical-documentation purposes. It does not represent a production data-acquisition service or commercial analytics product.