CSC 112 Fall 2026: programming, data science, Excel, and Python for business computing.
Course Website: Lecture notes on this site; assignments, grades, and announcements on D2L
This is an approved Science General Education distributive course.
Introduction to the fundamentals of business computing. Topics include surveys of the following sub-areas of computer science: hardware/operating systems, programming languages/software, ethics/social issues, problem solving, and advanced MS Excel and programming for business use. The course includes laboratory projects in MS Excel and Python programming.
This offering treats Excel as the primary analytical environment and Python as an automation layer. No prior programming experience is assumed. We will spend most of the semester learning to ask questions of data in Microsoft 365 (especially Excel), then use Copilot as a checked assistant, and finally use Python to repeat work that should not be done by hand.
This syllabus is subject to change. Please refer to D2L for the latest version.
By the end of this course, students will be able to:
| CSLOs | General Education Outcomes | Assessment |
|---|---|---|
| SLO1, SLO2, SLO4 | GE-GOAL #1d, #2d, #3a, #3b | Programming / Python projects |
| SLO1, SLO2 | GE-GOAL #1a, #1c, #1d | Capstone project / exam |
| SLO3, SLO4, SLO5 | GE-GOAL #2d, #3a, #3b | Excel projects |
There is no prerequisite for this course.
| Assessment | % of Final Grade |
|---|---|
| Excel Projects (15) | 70% |
| Excel Exams (2) | 20% |
| Python Programming Projects (2) | 10% |
| Grade | Quality Points | Numeric | Interpretation |
|---|
Refer to the Grading Information section of the Undergraduate Catalog for description of NG (No Grade), W, Z, and other grades.
Late assignments will be accepted; however, a 2.5% deduction per day, including weekends, will be applied to the assignment grade until submission.
CSC112 MWF 11AM: Question about Excel Project 3). Official class email will be sent through D2L; you are responsible for receiving it.| Week | Lecture | Topic and Readings | Lab / Office activity | Assessments |
|---|---|---|---|---|
| 1 | 1 | How computers think and store information Excel Ch. 1 (workbook, cells, files) | Create a workbook; inspect Number / Text / Date formats | Excel Project 1 |
| 2 | 2 | Data literacy before Excel Excel Ch. 1–2 (tables as data, not decoration) | Open a messy CSV; clean headers and types | Excel Project 2 |
| 3 | 3 | Formulas as reusable thinking Excel Ch. 2 (formulas, references) | Relative vs absolute references; weighted total | Excel Project 3 |
| 4 | 4 | Functions and logical expressions Excel Ch. 2 (functions, IF) | SUM, AVERAGE, IF / IFS; test the boundary | Excel Project 4 |
| 5 | 5 | Charts as arguments Excel Ch. 3 | Chart one claim; paste a linked chart into Word/PowerPoint | Excel Project 5 |
| 6 | 6 | Datasets and Tables Excel Ch. 4 | Convert a range to a Table; sort, filter, conditional formatting | Excel Project 6 |
| 7–8 | 7 | Subtotals, PivotTables, PivotCharts Excel Ch. 5 | Pivot a campus or sales dataset; one-sentence claim | Excel Projects 7–8 Excel Exam 1 |
| 9–10 | 8 | What-if analysis and specialized functions Excel Ch. 6–7 | Goal Seek / Scenario Manager; XLOOKUP | Excel Projects 9–11 |
| 11 | 9 | Statistical functions + Copilot as a checked assistant Excel Ch. 8 | Mean vs median; Copilot drafts a formula you audit | Excel Projects 12–13 |
| 12–14 | 10 | Python as an automation layer | Recreate one Excel summary in Python; compare outputs | Python Projects 1–2 Excel Projects 14–15 |
| Final | — | Final exam period | Excel Exam 2 | Excel Exam 2 |
This list outlines the topics we will study and may be modified as the semester progresses.
It is the responsibility of each student to adhere to the university’s standards for academic integrity. Violations of academic integrity include any act that violates the rights of another student in academic work, that involves misrepresentation of your own work, or that disrupts the instruction of the course. Other violations include (but are not limited to): cheating on assignments or examinations; plagiarizing, which means copying any part of another’s work and/or using ideas of another and presenting them as one’s own without giving proper credit to the source; selling, purchasing, or exchanging of term papers; falsifying of information; and using your own work from one class to fulfill the assignment for another class without significant modification. Proof of academic misconduct can result in the automatic failure and removal from this course. For questions regarding Academic Integrity, the No-Grade Policy, Sexual Harassment, or the Student Code of Conduct, students are encouraged to refer to the Department Undergraduate Handbook, the Undergraduate Course Catalog, the Ram’s Eye View, or the University Website.
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