RDS 100 Introduction to Data Science introduces the foundations of data science through programming and exploratory data analysis. Students will develop essential coding skills, use version control, apply basic data cleaning and wrangling techniques to prepare datasets for analysis, and create effective visualizations. Core topics include an introduction to statistical inference, regression, classification, and clustering. Emphasis is placed on reproducible workflows in notebooks, enabling students to build confidence in coding while analyzing real-world datasets.
Prerequisites: Minimum grade of 60% in Pre-Calculus 12 or equivalent
Accessibility Services Notice
Students who would like an academic accommodation and who have a documented disability should contact Accessibility Services, if they have not already done so.Course Details| Total number of weeks | 15 |
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| Total Credits | |
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| Total Hours | 75 |
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Typical hours per week breakdown| Lecture | 3 |
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| Lab (lab, field, computer) | 2 |
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Learning Outcomes
Upon successful completion of this course, the learner will be able to:
- Acquire data from files, spreadsheets, databases, and approved online sources using computational tools
- Wrangle data into analysis-ready formats using standard cleaning and transformation techniques
- Select appropriate analytical approaches for common research and statistical question types
- Construct tables and visualizations that summarize data clearly and accurately
- Evaluate regression and classification models using appropriate performance measures and validation methods
- Apply clustering to identify structure in data including resulting groupings
- Estimate population quantities using sample data
Grading Table
Standard Academic and Career Programs Grading Table
| Percentage | Letter Grade | GPA |
| 90-100 | A+ | 4.33 |
| 85-89 | A | 4.00 |
| 80-84 | A- | 3.67 |
| 76-79 | B+ | 3.33 |
| 72-75 | B | 3.00 |
| 68-71 | B- | 2.67 |
| 64-67 | C+ | 2.33 |
| 60-63 | C | 2.00 |
| 55-59 | C- | 1.67 |
| 50-54 | P | 1.00 |
| 0-49 | F | 0.00 |
| | DNW | 0.00 |
See the Academic Calendar for General Information including how to withdraw from course(s) and other regulations.
Disclaimer
Information contained in this course outline is correct at the time of publication. Content of the course is revised on an ongoing basis to ensure relevance to changing educational, employment and market needs. The instructor will endeavor to provide notice of changes to students as soon as possible. The instructor reserves the right to add or delete material from courses.