GWU: Data Mining (Decision Sciences 6279)
Course materials for GWU's Data Mining and Machine Learning classes covering preprocessing, modeling, and practical Kaggle applications.
What is GWU: Data Mining (Decision Sciences 6279)?
GWU_data_mining is a collection of educational materials for George Washington University's Data Mining and Machine Learning courses. It provides structured learning resources covering data preprocessing, statistical modeling, machine learning algorithms, and practical applications through Kaggle competitions. The repository serves as a complete curriculum for students and professionals looking to build data science skills.
Graduate students in data science or analytics programs, particularly those at GWU, and self-learners seeking structured, university-level materials on data mining and machine learning.
It offers a comprehensive, applied curriculum with real-world project integration (Kaggle), support for multiple software tools, and a pre-configured Docker environment for easy setup, making it a practical resource for hands-on learning.
Overview
Materials for GWU DNSC 6279 and DNSC 6290.
Use Cases
Best For
- University students taking data mining or machine learning courses
- Self-learners seeking structured curriculum with practical exercises
- Preparing for Kaggle competitions with guided materials
- Learning data preprocessing and feature engineering techniques
- Understanding model interpretability and validation methods
- Setting up a consistent data science environment with Docker
Not Ideal For
- Developers seeking ready-to-use ML libraries or APIs for immediate integration
- Learners preferring short, video-based tutorials over text-heavy academic materials
- Professionals needing up-to-date content with latest software versions and frameworks
Pros & Cons
Pros
Covers 11 sections from basic data prep to model interpretability, providing a full university-level education in data mining and machine learning.
Includes hands-on workshops for ongoing Kaggle competitions like Advanced Regression and Digit Recognizer, emphasizing real-world application.
Designed to work with Python, R, SAS, H2O, PySpark, TensorFlow, Keras, and XGBoost, offering flexibility across different tools.
Provides a pre-configured Docker image with Anaconda Python 3.5, H2O, XGBoost, and GraphViz for easy and consistent environment replication.
Cons
Relies on Anaconda Python 3.5 and may not be updated for newer library releases, potentially causing compatibility issues.
Some materials are restricted to Blackboard for GWU students, limiting full availability for external learners.
Includes grading schemes, exams, and university-specific policies that are irrelevant for self-learners focused solely on skills.
Frequently Asked Questions
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Coursera: How to Win a Data Science Competition: Learn from Top KagglersAdvanced Machine Learning Specialization
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