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
• Skills Acquired
• Projects
  • Project 1: Data Cleaning and Preparation
  • Project 2: Data Visualization
  • Project 3: Exploratory Data Analysis
  • Project 4: Data Storytelling
• Tools and Technologies
• Conclusion
• Contact Information

📚Portfolio Structure

/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

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

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

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

⚙️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
• GitHub: Willie-Conway
• LinkedIn: Willie Conway