| Course | BU 451 Programming for Business Analytics |
|---|---|
| Unit | Unit 1 |
| Paper type | Environment and setup exercise |
| Length | About 850 words, 6 pages |
| Format | APA 7 student paper |
| School | Herzing University |
| Program | BS in Business Analytics |
| Updated | September 2026 |
Free sample paper for BU 451 Unit 1
A Folder That Runs on Someone Else's Machine: An Environment and Setup Exercise for Analyzing a Composite Chattanooga Paddle Rental Company's 2025 Season in Python
Student Name
Business, Herzing University
BU 451: Programming for Business Analytics
Instructor Name
January 14, 2026
A Folder That Runs on Someone Else's Machine: An Environment and Setup Exercise for Analyzing a Composite Chattanooga Paddle Rental Company's 2025 Season in Python
Purpose of the Setup
This term's analysis uses data from Riverbend Paddle Co., a composite kayak and paddleboard rental company created for this course, which rents craft from three launch sites on the Tennessee River around Chattanooga between April and October. The data are four files: every rental in the 2025 season, a list of launch sites, the fleet of craft and a daily weather record. Before any of those files is opened, this exercise builds the environment the analysis will run in and makes it part of the submission. The standard it must meet is simple to state and easy to fail: an instructor who downloads the folder, runs one setup command and one analysis command on a different computer should get the same results the author got.
Sandve et al. (2013) list rules for reproducible computational work, among them recording how every result was produced, avoiding manual steps and keeping exact versions of the software used. Wilson et al. (2017) add practical habits for small projects: a clear folder layout, raw data kept untouched, and a short explanation of where a reader should begin. This setup applies both.
The Project Folder
The folder has one fixed layout, created by the setup script rather than by hand, so that every later unit can rely on the same paths.
| Path | Contents | Rule |
|---|---|---|
| README.md | What the project is, how to set it up, where to begin | Read first |
| requirements.txt | Python packages with exact versions | Changed only deliberately, with a note |
| setup_env.py | Creates the environment and the empty folders | Runs from nothing |
| data/raw/ | rentals_2025.csv, sites.csv, fleet.csv, weather_2025.csv | Never edited; code reads only |
| data/processed/ | Files written by the cleaning code | Code writes only; safe to delete |
| scripts/ | Numbered scripts, 01_ to 08_, one per unit | Run in number order |
| output/ | Tables and figures written by code | Code writes only; safe to delete |
Relative Paths Everywhere
Every script finds files relative to the project folder, never through a path on the author's computer. The first lines of each script establish where the project lives and build every other path from that point, using Python's standard pathlib module: the project root is the folder above the scripts folder, the raw data folder is the root joined with data and raw, and so on. A script written this way runs unchanged whether the folder sits on the author's desktop, the instructor's downloads folder or a server.
The alternative, typing a full path such as the author's user folder followed by the file name, is the most common reason student analyses fail on an instructor's machine. The setup exercise tests for it directly: after writing the scripts, the author copied the whole folder to a different location and to a second computer and ran it again. Both runs worked, and both produced the same row counts, which is the evidence the exercise asks for rather than an assurance.
A Setup Script That Runs From Nothing
The setup script assumes only that Python 3.12 is installed. It creates a virtual environment inside the project folder, so the analysis does not depend on whatever packages the user already has; installs the packages listed in requirements.txt at their exact versions; creates the data/processed and output folders if they do not exist; and finally checks that the four raw data files are present, printing their names and sizes, or stopping with a message naming any file that is missing.
The course specifies the core packages, and the setup records exactly which versions were installed, because the same function can behave differently in different versions. The requirements file pins pandas 2.2.3 for loading and reshaping data, numpy 2.1.3 for numerical work and matplotlib 3.9.2 for figures, together with the packages they depend on. McKinney (2022), whose library the analysis relies on, notes that behavior such as the default data types chosen when reading files has changed between major versions, which is exactly the kind of difference a pinned version prevents.
| Step in setup_env.py | What it does | What it prints |
|---|---|---|
| 1 | Checks the Python version is 3.12 | Python version found |
| 2 | Creates .venv inside the project | Environment path |
| 3 | Installs requirements.txt into .venv | Each package and version |
| 4 | Creates data/processed and output | Folders created or already present |
| 5 | Checks the four raw files exist | File names and sizes, or a stop message |
The Note That Tells a Reader Where to Begin
The README is short on purpose. Its first line says what the project is: an analysis of Riverbend Paddle Co.'s 2025 rental season for BU 451. Its second section gives two commands, one to set up and one to run all scripts in order, and says the whole run takes about a minute on an ordinary laptop. Its third section lists the four raw files and what each contains, with a warning that they must not be edited. Its last section says where results appear, in output, and that output can be deleted and rebuilt at any time.
Everything else a reader might want, such as the meaning of each column, is left to the import script in the next unit, which inspects the files and prints their structure. Keeping the README short, and limited to what a newcomer needs in the first five minutes makes it more likely to be read.
What the Setup Deliberately Leaves Out
The setup does not open or interpret any data beyond checking that the files exist. It does not include a notebook, because notebooks can be run out of order and hide the sequence that produced a result; the scripts run top to bottom in a fixed order instead. It does not store the virtual environment in the submission, since the setup script rebuilds it. And it contains no credentials or paths to any company system. Riverbend Paddle Co. and all four data files are composites made for this course.
References
McKinney, W. (2022). Python for data analysis: Data wrangling with pandas, NumPy, and Jupyter (3rd ed.). O'Reilly Media.
Sandve, G. K., Nekrutenko, A., Taylor, J., & Hovig, E. (2013). Ten simple rules for reproducible computational research. PLOS Computational Biology, 9(10), Article e1003285. https://doi.org/10.1371/journal.pcbi.1003285
Wilson, G., Bryan, J., Cranston, K., Kitzes, J., Nederbragt, L., & Teal, T. K. (2017). Good enough practices in scientific computing. PLOS Computational Biology, 13(6), Article e1005510. https://doi.org/10.1371/journal.pcbi.1005510
BU 451 Unit 1 instructions, in plain terms
The first BU 451 unit asks students to prepare a working environment and project structure before any analysis begins. Prompts typically require a folder layout, a setup script or instructions that create the environment from nothing, a record of installed package versions, relative file paths and a short README. Many instructors test whether the setup runs on another machine, so hard-coded personal paths are a common reason for lost points. The course usually specifies the language and core packages; students record the exact versions installed. The submission is typically the folder itself plus a short written explanation, and a strong one explains each structural choice rather than simply listing files and commands.
Inside the BU 451 Unit 1 example
The sample opens with the case, four data files for a river rental company, and the test the setup must pass. A table lays out the project folder with the contents and editing rule for each path. A section explains how scripts build every path from the project root and how the setup was tested by moving the folder and running it on a second computer. The setup section walks through the script's five steps, with a table of what each prints, and explains why package versions are pinned. A section describes the README's four parts. The final section lists what the setup deliberately leaves out, from notebooks to credentials.
Reading the BU 451 Unit 1 rubric
Herzing's rubric for the setup exercise usually rewards a clear structure, reproducible setup, relative paths, recorded versions and helpful documentation, with written explanation as a final criterion. Structure earns full credit when raw data, processed data, code and output are separated with stated rules. The setup criterion scores highest when the environment can be built from nothing by a script. Relative paths are judged on whether the project runs from any location. Version recording rewards exact pins rather than package names alone. Documentation earns points when a newcomer knows what to do first. Instructors often run the folder themselves, so a demonstrated test on a second machine is strong evidence.
Common BU 451 Unit 1 mistakes, and how to avoid them
Setup exercises often lose points because a script contains a full path to the author's own computer, which fails immediately elsewhere. Another common problem is listing packages without versions, so the analysis may behave differently for the instructor. Students frequently mix raw and processed files in one folder, making it unclear what may be edited. Some rely on notebooks run out of order, hiding the sequence that produced a result. Others write long README files that bury the two commands a reader needs. Many never test the setup anywhere but their own machine. Finally, setting up on a work computer can pull in company settings or credentials that should never appear in a class submission.
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BU 451 Unit 1 questions, answered
Does the exercise need to run on my instructor's machine?
Assume it will be opened somewhere other than where it was written, because that is what the criterion is testing. Relative paths, a stated project root and a data directory that travels with the folder are what make that possible. You cannot control what a marker has installed, so the note should say what the script expects rather than promising it will work everywhere.
Which language or package set should the setup use?
Whatever your section specifies, and the instructions are the only authority on that. Sections of the same course differ, and a submission built around something the classroom never named will be marked against a rubric written for something else. Read the assignment page before installing anything, then record in the script what you actually loaded.
Can I set this up on my work computer?
Where your employer allows it, that is between you and them, but nothing from the machine belongs in the submission. Company software licenses, internal package mirrors, network drive locations and anything sitting behind a corporate login stay out of the folder you hand in. Build the exercise against the file the assignment supplies so the whole thing can leave the building.
Where can I find a free BU 451 Unit 1 sample paper?
Read the full BU 451 Unit 1 environment and setup exercise above without paying anything; it includes the folder table, the five setup steps and margin notes. Using R or another package set instead? Tell us, and a version built for that toolset follows free in about two days.
Does BU 451 Unit 1 need to run on my instructor's computer?
It should. The sample tests exactly that by copying the folder to a second machine and rerunning setup, which is why every path is relative and every package version is pinned.