Common Course Outline

Course discipline/number/title DSCI 2253: Python for Data Science

Catalog Description

Credits 3
Hours/Week 3
Prerequisites (Course discipline/number) COMP 1150
Other requirements None
MnTC Goals (if any) NA

Course Description

This course develops strong foundations in Python programming for data science applications. Students learn to work with essential data science libraries including NumPy for numerical computing and Pandas for data manipulation. Through hands-on programming exercises using real-world datasets, students develop proficiency in data cleaning, transformation, analysis, and visualization. The course emphasizes writing efficient, maintainable code while building fundamental understanding of data science concepts and their practical applications in business and research contexts. Students are recommended to have a basic understanding of statistics.

Date Last Revised (Month, year)

April, 2025

Outline of Major Content Areas

  1. Python Programming Fundamentals
  2. Development environment setup and basic syntax
  3. Variables, data types, and control structures
  4. Functions and modular programming
  5. Error handling and debugging strategies
  6. Python Libraries for Data Science
  7. Array operations and numerical computing
  8. Data manipulation and analysis frameworks
  9. Visualization libraries and plotting basics
  10. Data Acquisition and Cleaning
  11. Working with common file formats
  12. Database connectivity fundamentals
  13. Web data collection techniques
  14. Data cleaning methodologies
  15. Advanced Data Manipulation
  16. DataFrame operations and transformations
  17. Aggregation and grouping techniques
  18. Handling missing and inconsistent data
  19. Feature engineering principles
  20. Exploratory Data Analysis
  21. Descriptive statistics
  22. Distribution analysis
  23. Correlation studies
  24. Pattern identification
  25. Data Ethics and Bias
  26. Sources of bias in data collection
  27. Impact analysis on diverse populations
  28. Ethical considerations in analysis
  29. Bias mitigation strategies
  30. Statistical Analysis
  31. Hypothesis testing implementation
  32. Regression techniques
  33. Model evaluation methods
  34. Statistical inference
  35. Data Visualization
  36. Basic plotting techniques
  37. Advanced visualization methods
  38. Interactive visualizations
  39. Best practices in visual communication
  40. Project Documentation and Communication
  41. Notebook organization and documentation
  42. Code documentation standards
  43. Technical writing for data science
  44. Results presentation strategies

Learning Outcomes (General)

The student will be able to:

  1. Write efficient Python code implementing appropriate data structures and control flow for data science applications
  2. Use NumPy and Pandas to perform data manipulation, numerical computing, and statistical analysis
  3. Create reproducible analysis workflows with clear documentation and error handling
  4. Implement data cleaning and preprocessing pipelines for various data formats
  5. Apply statistical methods to analyze relationships and patterns in data
  6. Write modular, reusable code following Python best practices
  7. Generate effective data visualizations that clearly communicate analytical findings
  8. Evaluate data quality and identify potential sources of bias
  9. Design and execute complete data analysis projects from raw data to final recommendations
  10. Produce clear technical documentation and presentations for various audiences

Learning Outcomes (MnTC)

NA

Methods for Evaluation of Student Learning

Methods may include but are not limited to:

RCTC Core Outcome(s)

This course contributes to meeting the following RCTC Core Outcome(s):

Critical Thinking
Students will think systematically and explore information thoroughly before accepting or formulating a position or conclusion.

Special Information (if any)

None