https://github.com/willie-conway/meta-data-analyst-portfolio
A comprehensive 📚portfolio showcasing projects and skills developed during the Meta Data Analyst Professional Certificate 🎓course, featuring 📈data analysis, 📊visualization, and 👨🏿💻management using various ⚙️tools.
https://github.com/willie-conway/meta-data-analyst-portfolio
big-data business-intelligence data-analysis data-cleaning data-driven-decisions data-management data-mining data-visualization exploratory-data-analysis jupyter-notebook machine-learning pandas porfolio predictive-modeling python spreadsheet-analysis sql statistics tableau visualization-tools
Last synced: 3 months ago
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A comprehensive 📚portfolio showcasing projects and skills developed during the Meta Data Analyst Professional Certificate 🎓course, featuring 📈data analysis, 📊visualization, and 👨🏿💻management using various ⚙️tools.
- Host: GitHub
- URL: https://github.com/willie-conway/meta-data-analyst-portfolio
- Owner: Willie-Conway
- License: mit
- Created: 2024-10-25T06:07:55.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-06-23T00:52:39.000Z (4 months ago)
- Last Synced: 2025-06-23T01:32:31.882Z (4 months ago)
- Topics: big-data, business-intelligence, data-analysis, data-cleaning, data-driven-decisions, data-management, data-mining, data-visualization, exploratory-data-analysis, jupyter-notebook, machine-learning, pandas, porfolio, predictive-modeling, python, spreadsheet-analysis, sql, statistics, tableau, visualization-tools
- Language: Jupyter Notebook
- Homepage: https://github.com/Willie-Conway/Meta-Data-Analyst-Portfolio/blob/main/Statistics%20Foundations/Jupyter%20Nootebook/Facebook%20Ad%20Conversions%20Analysis.ipynb
- Size: 49.3 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
# Meta Data Analyst Professional Certificate Portfolio
## Overview
Welcome to my portfolio! I have completed the **Meta Data Analyst Professional Certificate**, where I gained valuable skills and knowledge in data analysis, data management, and data visualization. This portfolio showcases the projects and assignments I completed during the course, highlighting my proficiency in key concepts and tools.
## 📖Table of Contents
- [Course Summary](#course-summary)
- [Skills Acquired](#skills-acquired)
- [Projects](#projects)
- [Project 1: Data Cleaning and Preparation](#project-1-data-cleaning-and-preparation)
- [Project 2: Data Visualization](#project-2-data-visualization)
- [Project 3: Exploratory Data Analysis](#project-3-exploratory-data-analysis)
- [Project 4: Data Storytelling](#project-4-data-storytelling)
- [Tools and Technologies](#tools-and-technologies)
- [Conclusion](#conclusion)
- [Contact Information](#contact-information)
## 📚Portfolio Structure
```markdown
/MyPortfolio
│
├── /Data_Analysis_with_Spreadsheets_and_SQL
│ ├── Commonly_Used_Spreadsheet_Tools.py
│ ├── Data_Analysis_with_Spreadsheets.py
│ ├── Explore_Data_Visually.py
│ ├── Most_Profitable_Stores.twb
│ ├── Overview_Of_Common_Chart_Types.py
│ └── README.md # Project description and usage instructions
│
├── /Data_Analytics
│ ├── Case_Study.py
│ ├── Data_Analysis_vs_Data_Science.py
│ ├── Data_Exploration_Checklist.py
│ ├── Data_Scrubbing_Checklist.py
│ ├── Datasources.py
│ ├── Different_Types_Of_Models.py
│ ├── Experience_the_Power_of_GenAI.py
│ ├── Exploring_and_Modeling_Data.py
│ ├── Feature_Engineering.py
│ ├── Generative_AI_Overview.py
│ ├── Generative_AI_Response.py
│ ├── Key_Points_on_GenAI_in_Data_Analytics.py
│ ├── OSEMN_Framework.py
│ ├── OSEMN_Framework_for_Cat_and_Dog_Products.py
│ ├── Obtaining_Data.py
│ ├── Obtaining_and_Scrubbing_Data.py
│ ├── Validity_Of_Data_Checklist.py
│ ├── iNterpreting_Data.py
│ ├── iNterpreting_Data_and_Storytelling.py
│ └── README.md # Project description and usage instructions
│
├── /Data_Management
│ ├── Big_Data_Management_Systems_Roundup.py
│ ├── Compliance_Best_Practices.py
│ ├── Data_Collection_Tool_Roundup.py
│ ├── Data_Profiling_and_Validation_Tools_Roundup.py
│ ├── Data_Storage_Formats.py
│ ├── Data_Visualization_Tools_Roundup.py
│ ├── Data_security_Fundamentals.py
│ ├── Machine_Learning_Tools_Roundup.py
│ ├── Storage_Solutions_Roundup.py
│ ├── Storage_System_Roundup.py
│ ├── Storage_Tools_Roundup.py
│ └── Using_Data.py
│
├── /Python_Data_Analytics
│ ├── /Jupyter_Notebooks
│ │ ├── .ipynb_checkpoints
│ │ ├── Aggregations.ipynb
│ │ ├── Basic_Exploration.ipynb
│ │ ├── Booleans_in_Python.ipynb
│ │ ├── Conditional_Statements.ipynb
│ │ ├── Creating_Explanatory_Visualizations.ipynb
│ │ ├── Creating_Visualizations.ipynb
│ │ ├── Dictionaries.ipynb
│ │ ├── Exploration_-_Basic_Statistics.ipynb
│ │ ├── Exploration_-_Filtering_Data.ipynb
│ │ ├── Exploring_With_Visualizations.ipynb
│ │ ├── Full_OSEMN.ipynb
│ │ ├── Introduction_to_Libraries.ipynb
│ │ ├── Lists_and_Tuples.ipynb
│ │ ├── Modeling_with_Python.ipynb
│ │ ├── Modifying_Values.ipynb
│ │ ├── Removing_Data.ipynb
│ │ ├── Selective_Subsets.ipynb
│ │ ├── Subsets_with_Pandas.ipynb
│ │ ├── Using_Pandas_and_Matplotlib_to_Create_Visualizations.ipynb
│ │ └── Variables_in_Python.ipynb
│ └── README.md # Overview of Python data analytics projects
│
├── /Sample_Data
│ ├── Activity_Dataset_Cleaned.xlsx
│ ├── Activity_Dataset_Cleaning.xlsx
│ ├── Home_Selling_Prices.xlsx
│ ├── Website_Sales.xlsx
│ └── README.md # Description of the datasets
│
├── /Statistics_Foundations
│ ├── /Capstones_Modules
│ │ ├── 1_Getting_to_Know_the_Data
│ │ │ ├── Datasets
│ │ │ ├── Screenshots
│ │ ├── 2_Understanding_Your_Data_Samples
│ │ │ ├── Datasets
│ │ │ ├── Screenshots
│ │ ├── 3_Testing_Your_Hypothesis
│ │ │ ├── Datasets
│ │ │ ├── Screenshots
│ │ └── 4_Data_Modeling
│ │ ├── Datasets
│ │ ├── Screenshots
│ └── README.md # Overview of statistics foundations projects
│
├── /Tableau
│ ├── Age_and_Income_-_Cluster_Analysis.twb
│ ├── Time_Series.twb
│ └── README.md # Overview of Tableau projects
│
├── /Excel
│ ├── AB_Testing.ipynb
│ ├── Capstone_Week_4_-_Show_Me_the_Model.ipynb
│ └── README.md # Overview of Excel projects
│
├── .gitignore
├── CHANGELOG.md
├── CONTRIBUTING.md
├── LICENSE
├── README.md # Main overview of the entire portfolio
└── requirements.txt
```
## 📝Course Summary
The Meta Data Analyst Professional Certificate program provided me with comprehensive training in various aspects of data analysis. I learned about 📅data collection, 🧹cleaning, 📊visualization, and the importance of metadata in managing and analyzing data effectively.
## ⚙️Skills Acquired
- Data cleaning and preprocessing
- Data visualization techniques
- Exploratory data analysis (EDA)
- Statistical analysis and interpretation
- Data storytelling and presentation
- Proficiency in tools such as `Excel`, `Python`, and `SQL`
## 🛠️Projects
### Project 1: Data Cleaning and Preparation
- **Objective**: Clean and prepare a raw dataset for analysis.
- **Description**: I worked with a messy dataset containing missing values, duplicates, and inconsistencies. I applied techniques to clean the data, including:
- Removing duplicates
- Imputing missing values
- Normalizing data formats
- **Technologies Used**: `Python` (`Pandas`), `Excel`, `SQL`
- **Link**: [Getting to Know the Data](link-to-your-project)
### Project 2: Data Visualization
- **Objective**: Create compelling visualizations to convey insights from data.
- **Description**: I utilized visualization libraries to create informative charts and graphs that highlight key trends and patterns in the data.
- **Key Visualizations**:
- Bar charts
- Line graphs
- Heatmaps
- **Technologies Used**: `Python` (`Matplotlib`, `Seaborn`), `Tableau`
- **Link**: [Understanding Your Data Samples](link-to-your-project)
### Project 3: Exploratory Data Analysis
- **Objective**: Conduct a thorough exploratory data analysis on a given dataset.
- **Description**: I analyzed a dataset to uncover insights and relationships between variables. This involved:
- Descriptive statistics
- Correlation analysis
- Identifying outliers
- **Technologies Used**: `Python` (`Pandas`, `NumPy`), `Excel`, `SQL`
- **Link**: [Testing Your Hypothesis](link-to-your-project)
### Project 4: Data Storytelling
- **Objective**: Develop a narrative around data findings to present to stakeholders.
- **Description**: I created a presentation that tells a story using data visualizations and analyses, focusing on making insights accessible and actionable.
- **Key Components**:
- Storyboarding the presentation
- Creating engaging visuals
- Highlighting actionable insights
- **Technologies Used**: `PowerPoint`, `Tableau`
- **Link**: [Data Modeling](link-to-your-project)
## ⚙️Tools and Technologies
- **Programming Languages**: `Python`, `SQL`
- **Data Analysis Tools**: `Excel`, `Pandas`, `NumPy`
- **Data Visualization Tools**: `Matplotlib`, `Seaborn`, `Tableau`
- **Other Tools**: `PowerPoint`, `Jupyter Notebooks`
## Conclusion
Completing the Meta Data Analyst Professional Certificate has equipped me with the essential skills and knowledge to pursue a career in data analysis. I am excited to apply what I’ve learned in real-world scenarios and look forward to contributing to data-driven projects.
Feel free to reach out if you have any questions or would like to discuss my work further!
## Contact Information
- **Email**: [hire.willie.conway@gmail.com](mailto:hire.willie.conway@gmail.com)
- **GitHub**: [Willie-Conway](https://github.com/Willie-Conway)
- **LinkedIn**: [Willie Conway](https://www.linkedin.com/in/willieconway/)