Data Exploration
CMPSC 105 - Fall 2026 - Allegheny College
This course offers a modern, computational approach to understanding the world through data. Throughout the semester, we will build a practical toolkit for rigorous, analytical problem-solving. At its core, data exploration combines computational thinking, inferential reasoning, and real-world applications. When confronted with data from a complex phenomenon, our goal is to gain a better understanding of what’s happening and communicate our findings effectively. To this end, we will
- Formulate precise hypotheses based on real-world observations.
- Compare various techniques and modes of data visualization.
- Write code in Python using libraries such as Pandas and Seaborn to manipulate, summarize, and visualize datasets.
- Effectively communicate findings through clear visualizations and structured reports.
By the end of this course, students will have a solid foundation in wrangling, analyzing, and visualizing data. You will be prepared to tackle future data-intensive coursework and equipped to apply these tools across a wide variety of disciplines and professional domains.
Table of contents
- Course Description
- Learning Objectives
- When and Where
- Topics
- Textbooks
- Coursework
- Grading
- Assessment Policies
- Seeking Assistance
- Using GitHub and Discord
- Using Email
- Honor Code
- Effective Development Practices
- Disability Services
- Syllabus Changes
Course Description
An introduction to the methods of collecting, exploring, transforming and visualizing data for storytelling. Often participating in team-based and hands-on activities, students learn how to use web platforms and dashboards to acquire, explore and investigate data to generate summarized key data insights using visual techniques. Students also apply open-source programming language to discover patterns in the data, test hypotheses, and check assumptions using graphical representations. During a weekly laboratory session, students employ cutting-edge software tools to complete projects, reporting on their results through both written documents and oral presentations. Students are invited to use their own departmentally approved laptop in this course; a limited number of laptops are available for use during class and lab sessions.
- Prerequisite: None
- Distribution Requirements: Quantitative Reasoning (QR), Scientific Process and Knowledge (SP)
Quantitative Reasoning: Quantitative Reasoning is the ability to understand, investigate, communicate, and contextualize numerical, symbolic, and graphical information towards the exploration of natural, physical, behavioral, or social phenomena.
Scientific Process and Knowledge: Courses involving Scientific Process and Knowledge aim to convey an understanding of what is known or can be known about the natural world; apply scientific reasoning towards the analysis and synthesis of scientific information; and create scientifically literate citizens who can engage productively in problem solving.
Learning Objectives
Allegheny College’s educational program is designed so that its graduates are able to:
- AC-1: Think critically and creatively.
- AC-2: Communicate clearly and persuasively as speakers and writers.
- AC-3: Invoke multiple ways of understanding to organize and evaluate evidence, and to interpret and make sense of their experiences and the experiences of others.
- AC-4: Apply their knowledge and learning to engage in informed debate, and to analyze and solve problems.
Computer Science 105 at Allegheny College is a foundational course in the Data Science program. Graduates with the Data Science major and minor — who all take the Computer Science 105 course — must demonstrate their attainment of these learning objectives:
- DS-1: Effectively collects, organizes, analyzes and interprets both structured and unstructured datasets from diverse sources.
- DS-2: Can effectively and ethically use statistical data analysis techniques, modern machine learning algorithms, and state-of-the-art software tools and programming environments to design, build, evaluate, and deploy new predictive models.
- DS-3: Demonstrates and articulates the value of subject matter expertise in domains that apply data science techniques.
- DS-4: Can clearly and persuasively communicate the results of data analysis including critical examination and reflection on the ethical implications of such analysis.
All four of the Computer Science major’s learning objectives support the QR and SP distribution requirements and the College’s learning objectives.
The specific learning objectives for Computer Science 105 are as follows:
- CS-105-1: Develop hypotheses based on motivating problems and/or observations and identify appropriate data to address hypotheses.
- Enables the attainment of DS-1 and DS-2.
- CS-105-2: Identify and describe key elements in different types of data visualizations.
- Enables the attainment of DS-4.
- CS-105-3: Use web-based platforms to accurately present data sets through multiple visualizations.
- Enables the attainment of DS-3 and DS-4.
- CS-105-4: Use an open-source programming language to compute summary statistics and visualize key patterns in the data.
- Enables the attainment of DS-2.
- CS-105-5: Contribute to and present structured, web-based documentation that describes data exploration steps and visualization-based conclusions.
- Enables the attainment of DS-4.
The learning objectives for Computer Science 105 enable the attainment of the DS program learning objectives that in turn support the attainment of the College’s learning objectives.
When and Where
- Lecture
- M/W/F 1:30pm - 2:20pm, Alden 101
- Lab
- W 2:30pm - 4:00pm, Alden 101
Topics
- Study Design and Inferential Thinking [ways of asking the right questions]
- Formulating precise hypotheses
- Causation vs. correlation
- Observational studies vs. randomized control trials
- Data Manipulation and Programming [ways of shaping the data]
- Data structure and wrangling (Python and Pandas)
- Cleaning, filtering, and aggregating real-world datasets
- Data Visualization and Communication [ways to visually represent and interpret data]
- Principles of effective visualization
- Python libraries for data visualization (e.g., Matplotlib, Seaborn)
- Interactive dashboards
- Documentation and reporting findings
- Statistical Tools and Computational Inference [ways of reasoning about data]
- Descriptive statistics
- Simulation and resampling techniques (e.g., bootstrapping)
- Quantifying and communicating uncertainty
- Privacy-preserving techniques
Textbooks
We will cover selections from the open access books Computational and Inferential Thinking: The Foundations of Data Science and Data Science: A First Introduction with Python, though we will use supplementary material from other sources as well.
Coursework
Students will complete:
- Readings
- Lab Exercises (11 total)
- Course Project
- Initial proposal
- Final Report
- Final Presentation
These assessment categories have the following definitions:
-
Class Participation: Students are expected to regularly attend and actively participate in all class and lab sessions, as outlined in the course schedule. See the attendance policy below. Additionally, a student’s class participation grade may be reduced if they are frequently observed, during either class or laboratory sessions, undertaking non-course-related activities like viewing email, social media, or other content not about algorithm analysis.
-
Lab Exercises: There are 11 labs during the semester in which students work in teams on various data analysis tasks. The deliverables for labs include documentation in a GitHub repo addressing specific questions or demonstrating requested calculations, analyses, visualizations, and code snippets. Lab Exercises are due by 11:59pm on the day of the lab. Labs will be graded based on effort and completion. For late assignments, please refer to the tokens policy.
-
Course Project: Students will work in teams and employ techniques and skills learned throughout the course on a final project. Students will submit an initial proposal, a check-in, and a final report and the project will culminate in a final presentation during the last week of classes. The proposal and check-in will be graded on effort, completion, and progress. The final report and presentation will be graded based on correctness and quality.
Grading
Assessment categories will contribute to the final grade according to the percentages listed in the table below.
| Category | Percentage |
|---|---|
| Participation | 15% |
| Lab Exercises | 50% |
| Course Project (Proposal/Check-in/Report/Presentation) | 35% (10%/5%/10%/10%) |
Final grades will be assigned based on the weighted sum of the assessment categories listed above.
| Letter Grade | Percentage Range |
|---|---|
| A | 95-100% |
| A- | 90-94.9% |
| B+ | 87-89.9% |
| B | 83-86.9% |
| B- | 80-82.9% |
| C+ | 77-79.9% |
| C | 73-76.9% |
| C- | 70-72.9% |
| D | 60-69.9% |
| F | 0-59.9% |
Assessment Policies
Unless exempted by the instructor, students must abide by the following assessment policies:
Assignment Submission
All assignments will have a stated due date shared through GitHub, the course website, and/or the course Discord. No credit will be awarded for any coursework that you submit to the incorrect GitHub repository or web site. Unless special advance arrangements are made with the instructor to address extenuating circumstances, no work will be accepted after the deadline.
Course Tokens
Students may “spend” up to four Course “tokens” that they may use to secure seven days of additional time to complete a deliverable from the course project or a lab exercise (excluding the final presentation). To spend a token, a student must fill out the token request Google form within 48 hours of the original deadline. Students may use these tokens at their discretion within the constraints described above. Outside of using these three tokens or severe, extenuating, and unexpected circumstances that are well documented, the instructor will not grant any requests for extensions or reassessment.
Course Attendance
It is mandatory for all students to attend each of the lecture and lab sessions. We will take attendance using a Google form. Excluding the first week of the semester, students can have eight absences without any impact to their grade. These accommodations are meant to cover illness and emergency, so you should always come to class if you are able to do so.
As a general guideline, students cannot miss more than two weeks of class in total throughout the academic semester without receiving a letter grade reduction. For this course, excluding the eight excused absences, the overall course grade will decrease by 1/3 of a letter grade for each additional absence or absence equivalence regardless of base grade.
Class Preparation
In order to minimize confusion and maximize learning, students must invest time to prepare for the class sessions. Although the course instructor and the student technical leaders will always be available to serve as guide for individual students, teams of students, and the entire class, it is expected that students will volunteer to lead and actively contribute to all class sessions. Only those students who have prepared for class by reading and running the assigned material will be able to effectively participate in these class discussions. To help students remain organized and to effectively prepare for classes, the instructor will maintain a list of course slides, a lecture schedule with reading assignments, and other important information about the course on the schedule page of the course website.
Seeking Assistance
Students who are struggling to understand the knowledge and skills developed in this course are encouraged to seek assistance from the course instructor and/or the student technical leaders. Students should, within the bounds of the Honor Code, ask and answer questions on the course Discord Server; please request assistance from the instructor and student technical leaders first through public Discord channels before sending an email or a direct message. Students who need more assistance are invited to schedule a meeting through the instructor’s appointment scheduler and come to the meeting with details about their question. Students can see the office hour schedule for student technical leaders by viewing the list of student technical leaders and by monitoring announcements in the Allegheny College Computer Science Discord Server.
Using GitHub and Discord
This course will primarily use GitHub and Discord for all course communication. We will use GitHub for the sharing of both source code and documentation for course projects/assessments and for reporting issues in those materials. We will use course and department Discord servers for all course discussions. The course Discord Server will be the main forum for discussing the professional and technical content in the field of algorithm analysis. The Allegheny College Computer Science Discord Server will be the main forum for Department of Computer Science announcements. Finally, any content that a student wants the instructor to assess (e.g., the work for a course project) must be in a GitHub repository.
Using Email
Although we will primarily use the course Discord Server for class communication, the instructor will sometimes use email to send announcements about important matters such as changes in the schedule. It is your responsibility to check your email at least once a day and to ensure that you can reliably send and receive emails. This class policy is based on the statement about the use of email that appears in The Compass, the College’s student handbook; please see the course instructor if you do not have this handbook.
Honor Code
The Allegheny College Academic bulletin describes The Academic Honor Program that governs the entire academic program at Allegheny College. The Honor Program applies to all work that is submitted for academic credit or to meet non-credit requirements for graduation at Allegheny College. This includes all work assigned for this class (e.g., executable examinations and course assignments). All students who have enrolled in the College will work under the Honor Program. Each student who matriculates at the College acknowledges this Honor Code pledge:
I hereby recognize and pledge to fulfill my responsibilities, as defined in the Honor Code, and to maintain the integrity of both myself and the College community as a whole.
Effective Development Practices
Students who create the source code and documentation for their projects and assignments should ensure the implementation of a high-quality final product. While students are permitted to use a wide variety of tools, such as integrated development environments, testing frameworks, automated debuggers, and code generators (e.g., systems that leverage large language models like GitHub Copilot) and documentation sites such as StackOverflow, they must take responsibility for all of the source code and documentation that they submit for this course, including artifacts that are generated by a software tool.
This means that every student must work as an effective developer by documenting the sources for their work and verifying the correctness, maintainability, and long-term reliability of all source code and documentation that they submit. As such, students who use software tools to create content are responsible for citing their sources and demonstrating their understanding of it as a part of any follow-on assessment. Moreover, all students in the class are responsible for all of the source code and documentation submitted to the GitHub repository that hosts the course projects, including any tool-generated software artifacts. This means that every student should be able to answer questions, during either an in-person or online discussion, about any content produced for this course, including that which was generated by a software tool.
Disability Services
Students with disabilities who believe they may need accommodations in this class are encouraged to contact Student Accessibility and Support Services (SASS) at 814-332-2898 or studentaccessibility@allegheny.edu. SASS is located in the Center for Student Success in Pelletier Library. Please contact SASS as soon as possible to ensure that approved accommodations are implemented in a timely fashion.
Syllabus Changes
The instructor may make updates or changes to this document at any time as needed until term grades are due. Changes will be announced to the class.