asdelaney/software_assignment

Building Software

Description

Much work these days is done using software. Analysts need to develop a comfort with building, maintaining, and improving high-quality software, even if they are not primarily employed as a software engineer or developer. This course focuses on equipping students with the skills to build robust software that can be used to answer questions. It focuses on how to effectively write short programs, as part of a small team, in a reproducible way. Software that is built correctly can be used by other teams, not just the person who originally wrote it.

Learning Outcomes

Students will know how to:

1. Create and read configuration files for programs, and know when to use them
2. Describe, use, and write Application Programming Interfaces
   • Reading documentation, writing documentation
   • Using HTTP-based APIs in Python
   • Writing Python APIs
3. Know how to create bug reports and prioritize requests.
4. Proficiently test software, handle errors, and track provenance
5. Know how to create Python packages.

Logistics

Course Contacts

• Instructor: Simeon M Wong, MHSc(BME). [email protected]
• TA: Tong Su. [email protected]

Marking Scheme

Learners are assessed based on the course outcomes, which are demonstrated with the in-class exercises and the summative assignment that will be completed both progressively in class, and at home.

Policies

The course is a live-coding class. Students are expected to follow along with the coding, creating files and folders to navigate and manipulate. Students should be active participants while coding and are encouraged to ask questions throughout. Although slides will be available for students to reference, they should be referenced before or after class, as during class will be dedicated to coding with the instructor.

Folder Structure

Below are the folders contained in this repo with a description of what they contain and information on how to use them.

Assignments

This folder contains the homework and assignments for the class.

• Generally homework is ungraded and due before the next class for feedback from the teaching team
• The summative assignment is due by the end of the course

Lessons

This folder contains materials and references for each lesson, including slides and sample code.

• Classes will generally be a mix of didactic teaching and live-coding
• Learners are encouraged to follow along by typing in code along with the instructor during class
• The live coding exercises are also reproduced in the slides for reference

Acknowledgements

• Michaela Drouillard created the initial version of this course, under the guidance of Rohan Alexander.
• Revised by Simeon Wong for the Data Sciences Institute

Additional Resources

"You are never taught how to build quality software" via Hacker News https://news.ycombinator.com/item?id=38570261

software_assignment's Issues

Test case for compute analysis

Description: # Unit Test for compute_analysis method
def test_compute_analysis():
analysis_instance = Analysis(analysis_config='path/to/analysis_config.yml')
analysis_instance.load_data()
result = analysis_instance.compute_analysis()
assert isinstance(result, float)

Context:
This unit test is designed to verify the functionality of the compute_analysis method in the Analysis class. It ensures that the method correctly loads data, computes the analysis, and returns a result of the expected type (float).

Steps to Reproduce:

Set up the necessary environment for the test.
Execute the provided unit test using a testing framework (e.g., pytest).
Observe the test results.
Expected Behavior:
The test should pass, indicating that the compute_analysis method returns a float.

Adding function for computation and plotting of data.

Subject: Enhancement Request - Add Analysis and Visualization Features

Description:
Enhancement to the existing script by adding a new method (compute_analysis) and related functionality to compute and visualize the average word count of articles in the dataset. Additionally, a new method (plot_data) has been introduced to generate a bar chart visualizing the word counts.

Proposed Changes:
New Method: compute_analysis

Purpose: Compute the average word count of articles in the dataset.
Steps:
Extract word counts from the dataset.
Calculate the average word count.
Visualize word counts with a bar chart.
Save the plot as an image (optional).
Exception Handling: Raise an error if data is not loaded.
New Method: plot_data

Purpose: Generate a bar chart of word counts.
Parameters:
word_counts: List of word counts.
save_path (optional): Path to save the plot image.
Steps:
Create a bar chart.
Set labels and title.
Save the plot if save_path is provided (optional).
Display the plot (optional).
Notify Done: