Course Description
Principles of Data Science provides an introduction to the core concepts and methods used to analyze and interpret data in various domains. Students will explore the process of collecting, cleaning, and organizing data, as well as techniques for identifying patterns, trends, and relationships within datasets. The course covers key topics such as statistical analysis, data visualization, and basic machine learning concepts. By the end of the course, students will be equipped with the foundational knowledge to solve real-world problems using data-driven insights and effectively communicate their findings to diverse audiences. (3 credits)
Prerequisite
- None
Student Learning Outcomes (SLOs)
Upon completion of the course, the students will be able to:
- Identify fundamental data science concepts, including types of data, common statistical measures, and key techniques used for data analysis across various domains.
- Collect and clean raw data, preparing it for analysis by addressing missing values, inconsistencies, and errors in order to improve data quality and reliability.
- Analyze datasets using statistical methods to uncover patterns, trends, and relationships, effectively summarizing insights for data-driven decision-making.
- Visualize data through charts, graphs, and dashboards using tools like Excel, Tableau, or Python libraries, interpreting visual results to convey findings to diverse audiences.
- Describe basic machine learning concepts, such as supervised and unsupervised learning, and recognize when machine learning techniques may be applicable to solve data-driven problems.
- Communicate data insights in a clear and concise manner, tailoring presentations and reports to a non-technical audience to support decision-making processes in various contexts.
Course Activities and Grading
| Assignments | Weight |
|---|---|
Discussion (Weeks 1-7) | 10% |
Homework Assignments (Weeks 1-3, & 5-7) | 40% |
Midterm Project: Code Review (Week 4) | 4% |
Midterm Project: Final Submission (Week 4) | 13% |
Final Project: Presentation (Week 8) | 13% |
Final Project: Presentation Peer Review (Week 8) | 7% |
Final Project: Coding Final Submission (Week 8) | 13% |
Total | 100% |
Required Textbooks
- This course uses Open Educational Resources (OER). OER are openly licensed, educational resources that can be used for teaching, learning and research. OER may consist of a variety of resources such as textbooks, videos and software that are no cost for students.
Course Schedule
Week | SLOs | Readings and Exercises | Assignments |
1 | 1 | Topic: Introduction to Data Science
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2 | 1,2 | Topic: Data Types, Tables, and Data Cleaning
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3 | 3,4 | Topic: Data Visualization Techniques & Choosing and Customizing Plot Types
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4 | 1-4 | Topic: Midterm Project
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5 | 5 | Topic: Probability Review and Exploratory Data Analysis
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6 | 5 | Topic: Prediction, Machine Learning Techniques and Model Evaluation
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7 | 5,6 | Topic: Experimentation, Causal Inference, and Presenting to Non-Technical Audiences
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8 | 1-6 | Topic: Final Project
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COSC Accessibility Statement
Charter Oak State College encourages students with disabilities, including non-visible disabilities such as chronic diseases, learning disabilities, head injury, attention deficit/hyperactive disorder, or psychiatric disabilities, to discuss appropriate accommodations with the Office of Accessibility Services at OAS@charteroak.edu.
COSC Policies, Course Policies, Academic Support Services and Resources
Students are responsible for knowing all Charter Oak State College (COSC) institutional policies, course-specific policies, procedures, and available academic support services and resources. Please see COSC Policies for COSC institutional policies, and see also specific policies related to this course. See COSC Resources for information regarding available academic support services and resources.