University of Idaho
CS 5622 Applied Data Science with Python is a graduate-level course that introduces students to the complete lifecycle of a data science project, from data collection to model deployment. The course emphasizes the practical application of Python programming and modern data science libraries to solve real-world problems.
This course is joint-listed with CS 4622 (undergraduate equivalent).
The curriculum is organized into four main themes:
- Review of Python programming basics
- Functions, modules, and best practices
- Object-oriented programming concepts relevant to data science
- Data collection and preprocessing
- Data exploration and visualization
- Libraries: NumPy, Pandas, Matplotlib, Seaborn
- Predictive model design, selection, and evaluation
- Key application domains:
- Image Processing
- Natural Language Processing (NLP)
- Time Series Analysis
- Frameworks: Scikit-Learn, Keras, TensorFlow, PyTorch
- Deploying data science projects into production
- Model serving and scaling
- Performance monitoring and diagnosis
- Ensuring reproducibility and version control
This section documents weekly tasks, projects, and deliverables. This will be updated regularly.
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Week 1:
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Week 2:
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Week 3:
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(Continue for additional weeks...)
- Strong Python programming skills
- Familiarity with statistics, probability, and linear algebra
- Prior experience with data analysis is helpful but not required
- Python 3.x
- NumPy, Pandas, Matplotlib, Seaborn
- Scikit-Learn
- TensorFlow, Keras, PyTorch
- Jupyter Notebook / JupyterLab
- Git & GitHub for version control
- Assignments: 40%
- Midterm Project: 20%
- Final Project: 30%
- Participation & Discussion: 10%
- Instructor: [Add Name]
- Email: [Add Email]
- Office Hours: [Add Schedule]
This repository is for educational purposes only. All course materials belong to the University of Idaho and respective authors.