I'm Brandon Harrelson, a burgeoning data scientist diving deep into the analytics arena through an intensive bootcamp program. My voyage in data science is fueled by my enthusiasm for uncovering insights from data and applying machine learning to tackle real-world challenges. This repository serves as a chronicle of my progress and a testament to the hands-on skills I've developed in areas ranging from exploratory data analysis to advanced machine learning techniques.
This repository is a tapestry of my growth and learning, showcasing projects from various sprints of my data science bootcamp. Each project is an exploration into different domains and tools of data science:
- Sprint 1: Python Fundamentals - Introduction to the basics of Python, focusing on variables, data types, control structures, and essential data structures like lists and dictionaries.
- Sprint 2: Data Wrangling and Visualization - Techniques in Exploratory Data Analysis (EDA), data cleaning, and visualization to uncover patterns, insights, and anomalies in data.
- Sprint 3: Statistical Data Analysis - Application of statistical concepts and tests to interpret data findings accurately and to understand distributions, correlations, and regression analysis.
- Sprint 4: Software Development Tools - Exploring tools and methodologies in software development including version control with Git, collaborative platforms like GitHub, and best practices in coding.
- Sprint 5: Integrated Project 1 - A comprehensive project that combines data cleaning, analysis, and visualization to solve a real-world problem using Python.
- Sprint 6: Data Collection and Storage - Introduction to SQL, databases, and data collection techniques, focusing on querying, aggregating, and managing data efficiently.
- Sprint 7: Introduction to Machine Learning - Covering foundational machine learning concepts, algorithms, and how to apply them to real datasets.
- Sprint 8: Supervised Learning - Delving deeper into machine learning with a focus on supervised learning techniques including classification and regression.
- Sprint 9: Machine Learning in Business - Understanding how to apply machine learning models to solve business problems, evaluate model performance, and deploy solutions.
- Sprint 10: Integrated Project 2 - An advanced project where complex data analysis and machine learning techniques are applied to tackle business or social issues.
- Sprint 11: Linear Algebra - Exploring the role of linear algebra in data science and machine learning, understanding vectors, matrices, and their operations.
- Sprint 12: Numerical Methods - Applying numerical methods in Python for solving mathematical problems relevant to data science, such as optimization problems.
- Sprint 13: Time Series Analysis - Learning about time series data, its specific challenges, and techniques for analysis, forecasting, and seasonal adjustment.
- Sprint 14: Machine Learning for Texts - Introduction to Natural Language Processing (NLP), text preprocessing, and making predictions from text data.
- Sprint 15: Computer Vision - Exploring image processing techniques and applying machine learning models to recognize patterns and objects in images.
- Sprint 16: Unsupervised Learning - Investigating unsupervised learning techniques such as clustering, dimensionality reduction, and association rules. Note: No project for this sprint.
- Sprint 17: Final Project - Capstone project that encapsulates the knowledge and skills acquired throughout the bootcamp, focusing on a comprehensive data science challenge.
In my data science toolkit, I leverage various languages, libraries, and frameworks to analyze data, build models, and craft visualizations. Some of the key tools in my arsenal include:
Feel free to delve into my repositories to explore the various data science projects I've undertaken. If you have any queries, insights, or collaboration ideas, I'm all ears! Connect with me through the LinkedIn and Facebook badges above. Let's make data science a tool for positive change together!