I'm a recent graduate of ATU with a strong passion for data analytics. I love turning raw data into valuable insights, uncovering patterns, and using data to drive decisions. I have a solid foundation in Python, SQL, and statistical analysis, with hands-on experience in data visualization and machine learning.

Feel free to check out my projects below and get in touch via https://www.linkedin.com/in/keith-p-mcnamara/

• Languages: Python, SQL, R, go, c
• Tools & Libraries: Pandas, Matplotlib, Seaborn, Scikit-learn, Jupyter, Excel, scipy, flask django
• • Machine Learning: Supervised and Unsupervised learning, Model Evaluation, Feature Engineering, Regression & Classification, Time Series Analysis

• AWS Certified Solutions Architect - Associate

Here are some of my key data analytics projects:

• Data Projects: A collection of various data analysis projects I've worked on, demonstrating a range of skills including data cleaning, analysis, and visualization.

  How to Clone:

  git clone https://github.com/keithmmc/dataprojects.git
  

• Data Projects: A collection of various data analysis projects I've worked on, demonstrating a range of skills including data cleaning, analysis, and visualization.

• How to Clone:

  git clone https://github.com/keithmmc/dataprojects.git
  
  

• Applied Statistics: This repository showcases my knowledge in applied statistics, where I use statistical methods to analyze and interpret real-world data.

• • How to Clone:

  git clone https://github.com/keithmmc/appliedstats.git
  

• Data Fundamentals: A repository dedicated to foundational data analysis techniques, including data wrangling and exploratory data analysis (EDA).

• • How to Clone:

  git clone https://github.com/keithmmc/data_fundamentals.git
  

• PDA2 (Porgramming for Data Analysis 2): This project focuses on practical data analysis techniques, including the application of machine learning algorithms and more advanced data analysis methodologies.

• How to Clone:

  git clone https://github.com/pda2.git

• Exploratory Data Analysis (EDA): I’ve used EDA techniques to uncover insights from raw datasets, visualize distributions, and highlight key trends.

• Machine Learning Models: Built and evaluated machine learning models such as decision trees, random forests, and linear regression for predictive analysis.

• Data Visualization: Created insightful visualizations using Matplotlib, Seaborn, and Tableau to help interpret and communicate findings clearly.

• Web Development: Designed simple data-driven web applications using Flask and Django.

• Statistical Analysis: Applied statistical methods, including hypothesis testing, regression analysis, and statistical inference, to interpret data and provide recommendations.

I’m always looking for new ways to improve my skills and grow as a data analyst. Right now, I am focused on:

• Advanced machine learning algorithms (e.g., deep learning, neural networks)
• Time series analysis for forecasting and trend analysis
• Cloud platforms for big data (AZURE< Google Cloud)
• Exploring other visualization tools like Power BI and Plotly

Feel free to explore these repositories to learn more about the work I've done. If you have any questions or would like to collaborate on a project, don't hesitate to reach out!