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Syllabus
CSC 112 Fall 2026: programming, data science, Excel, and Python for business computing.
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How Computers Think and Store Information
Computers follow instructions and store typed values. Excel cells are memory you can see.
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Data Literacy Before Excel
Ask a question, name the rows and columns, and clean types before you SUM anything.
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Formulas as Reusable Thinking
A formula is an algorithm. Relative and absolute references say what should move when you copy.
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Functions and Logical Expressions
Functions are named algorithms. IF is a decision you can test at the boundary.
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Charts as Arguments
A chart makes one claim. Choose a type, title the claim, and keep it linked to the data.
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Datasets and Tables
A Table is a living rectangle: structured references, sort, filter, and formatting that follows the data.
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Subtotals, PivotTables, and PivotCharts
Pivots are grouped questions. If the pivot surprises you, check the source Table.
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What-If Analysis and Specialized Functions
Treat weights and rates as parameters. Look up descriptions; Goal Seek and scenarios record experiments.
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Statistical Functions and Copilot as a Checked Assistant
Describe a distribution honestly, then let Copilot draft formulas you can still verify.
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Python as an Automation Layer
Repeat an Excel analysis in Python and check that both answers match.
CSC 112: Programming & Data Science
Course Description
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. As a Science Distributive course, it emphasizes observation, data, quantitative methods, and checking whether a result is actually correct.