SCI 255 - Intro. to Scientific Computing

1. Demonstrate proficiency in basic computer architecture
   1. Navigate the file system structure, directories, and files
   2. Recognize common file types (e.g., .txt, .csv, .xlsx, .py, .ipynb) and their uses
   3. Create, move, copy, and delete files and directories
   4. Handle file permissions and access
   5. Save data to various file formats
   6. Employ data serialization techniques (e.g., JSON, Pickle)
   7. Install Python and its dependencies
   8. Install packages for Python
   9. Navigate Jupyter notebooks
2. Manipulate data in Python
   1. Load data from files into Python
   2. Save data to various file formats
   3. Apply basic Python syntax rules
   4. Define variables and data types
   5. Use basic operators and expressions
   6. Handle exceptions and errors
   7. Work with numeric data types (floats, integers)
   8. Handle dates, times, and scientific units
3. Compare and contrast data types, conditions, and control statements
   1. Describe the functionality of data types and when to employ each type
   2. Construct strings and lists from called data
   3. Index data from data types
   4. Employ arithmetic and logical operators
   5. Implement conditional statements (if, else, elif)
   6. Employ loops (for and while) for iterative tasks
4. Construct data representations and visualizations
   1. Identify and handle missing data
   2. Remove duplicates and outliers
   3. Normalize and standardize data
   4. Create meaningful plots and charts
   5. Customize visualizations for different data types
   6. Demonstrate use of Pandas for data manipulation
   7. Apply data aggregation and summarization techniques
5. Translate scientific problems into computational solutions
   1. Analyze basic statistical metrics on datasets
   2. Build hypotheses or questions based on preliminary analysis
   3. Decompose problems into smaller tasks
   4. Diagram computational pipelines to understand steps of analysis
6. Build basic statistical models for scientific data
   1. Analyze overall trends in quantitative data
   2. Build linear and nonlinear models of data
   3. Perform hypothesis-driven significance tests
   4. State biological, chemical, or physical explanations of computational models
7. Explore more complex models in scientific data analysis
   1. Identify appropriate model types for different data
   2. Consider ways to overcome shortcomings of simple linear models
   3. Build models of biological, chemical, or physical processes
   4. Diagnose scientific systems using logistic regression
   5. Solve simple differential equations in Python and interpret results
   6. Build simple machine learning algorithms to predict data outcomes
8. Communicate results of data analysis to a scientific audience
   1. Present findings and analyses to both technical and non-technical audiences
   2. Document code and analysis methods effectively
   3. Describe assumptions and limitations to data and analyses
   4. Write clear and concise reports
   5. Use figures, charts, and graphs in accessible and communicative ways