Course Syllabus


This course builds on the general programming principles of the pre-requisite course and teaches students how to write programs in the context of data science applications. It focuses on leveraging pre-existing libraries to accomplish data retrieval, preparation, prediction, and analysis tasks.

This course is 2.0 credits.

This course provides a better understanding of data programming. If you have signed up for this class, you are most likely driven by curiosity and interest in how data decisions are made (sometimes called data intuition). Possibly, you have a more empathetic approach to how the world works and how problems can be solved. Finally, you have an eye for how society reports and uses data to make impactful decisions1.

Upon completing this course, you will be able to use data-driven programming in Python to handle, format, and visualize data. We will introduce you to data-wrangling techniques, analytical methods, and the grammar of graphics.



Take one of the following:

  • CS 165—Object-Oriented Software Development
  • CS 241—Survey Object-Oriented Programming/Data Structures
  • CSE 110—Programming Building Blocks
  • MATH 335—Data Wrangling, Exploration, and Visualization
  • CSE 350—Data Wrangling and Visualization

Required Resources

No outside resources required.


Course Outcomes (CO)

  1. Use functions, data structures, and other programming constructs efficiently to process data.
  2. Programmatically load data from various types of data sources, including files, databases, and remote services.
  3. Use data manipulation libraries to perform straightforward analysis, produce basic plots, and prepare data for machine learning algorithms.
  4. Use machine learning libraries to discover insights, make predictions, and interpret the success of these algorithms.

Major Assignments

The table below is meant to help you see the relevance of each major assignment as it pertains to the course outcomes (CO).

Major Assignment Description CO#
W03 Submission: Unit 1 Project Complete a case study project by answering each of the grand questions #1, 3
W05 Submission Unit 2 Project Complete a case study project by answering each of the grand questions #1, 2, 3
W07 Submission Unit 3 Project Complete a case study project by answering each of the grand questions #1, 2, 4
W09 Submission Unit 4 Project Complete a case study project by answering each of the grand questions #1, 2, 3
W11 Submission Unit 5 Project Complete a case study project by answering each of the grand questions #1, 3
W13 Submission Unit 6 Project Complete a case study project by answering each of the grand questions #1-4
W14 Assignment: Coding Challenge Answer quiz questions following completion of a coding challenge #1
W14 Final: Finishing the Semester Write a letter requesting your grade for the course by reviewing your work throughout the semester #1–4

Weekly Patterns

The table below displays typical weekly activities, due dates, and activity descriptions. This course is set up in two weeks per unit.

Due Date* Learning Model Activity Title Description
First Week Midweek Prepare Introduction of Project Brief description of the project requirements and grand questions.
First Week Midweek Prepare Study Materials Study materials for the unit to prepare to work on the project.
First Week End-of-Week Prepare and Teach One Another Skill Builder Working in small groups, students work to learn programming skills necessary to help them complete their project.
First Week End-of-Week Ponder Mid-Project Checkpoint Students will complete a quiz measuring their progress and efforts on their project so far.
Second Week Midweek Prepare Grand Question Walkthrough Students will review the walkthrough on one of the grand questions that the instructor provides.
Second Week Midweek Teach One Another Discussion: Data Science Articles Students will find an article that discusses data science and discuss its implications with class members.
Second Week End-of-Week Teach One Another Response Post: Data Science Articles Students will respond to each other's articles.
Second Week End-of-Week Prove Methods and Calculations Quiz Students will complete fact finding and how the code works questions to show their ability to understand the methods needed to complete the project.
Second Week End-of-Week Prove Project Submission Students will complete their project and submit it for grading.

*Set your time zone within user preferences so the dates and times for course activities will display correctly for your time zone.

Learning Model

This course uses the learning model steps by inviting students to prepare through study materials and reviewing instructor walkthroughs. They will then teach one another in small groups as they work on the skill builders and the data science article discussions. Finally, they will ponder on their work in the mid-project checkpoints and prove their abilities through the project submission and the methods and calculations quizzes.



Students are expected to work 6–8 hours a week on this course.

Group Work

Students will be working in small groups to complete the skill builders. These skill builders allow students to see other methods of completing the work and help each other learn. The students will also discuss articles in small groups. One student will provide an article to discuss each unit with their team.

Late Work

As a sign of professionalism and respect, you should complete your work on time. However, your instructor has the discretion to accept late work or extend due dates as appropriate.


Students will have an opportunity to retry their projects once after initial grading. Students may not have more than one retry on each project.

Extra Credit

Since students will be allowed to retry their projects, extra credit will not be allowed.

Specifications Grading

Grading is a nasty side effect of mass learning and academia. We are in a class at a university and will have to manage this side effect. However, we don’t have to let it control our learning, thinking, or this class. Learning and thinking should motivate each activity.

As a team (teacher and student), we have the challenge to become more! We have worked hard to identify the specifications needed for a Python user of the pandas and Altair packages. Our goal is to align your grade with the skill specification you have mastered. In other words, the grade you want will determine how much work you will do. We will not score individual tasks in the class on a percentage scale. If your work meets the specified criteria, you will get full credit.

In a specifications-grading system, all tasks are evaluated on a high-standards pass-or-fail basis using detailed checklists of task requirements and expectations. You earn your letter grade by earning passing marks on a set of tasks. This system provides various choices and is closer to how learning and work occur in the real world. It will be easy for us to tell if work is complete, done in good faith, and consistent with the requirements.

Follow the instructions on the DS 250 Competency page on how specifications grading will occur in this course.

Grading Scale

Letter Grade Percent
A 100%–93%
A- 92%–90%
B+ 89%–87%
B 86%–83%
B- 82%–80%
C+ 79%–77%
C 76%–73%
C- 72%–70%
D+ 69%–67%
D 66%–63%
D- 62%–60%
F 59% and lower

University Policies

Students with Disabilities

BYU-Idaho is committed to providing a working and learning atmosphere that reasonably accommodates qualified persons with disabilities. Reasonable academic accommodations are reviewed for all students who have qualified documented disabilities. Services are coordinated with the student and instructor by BYU-Idaho Disability Services. If you need assistance or feel you have been unlawfully discriminated against on the basis of disability, you may seek resolution through established policy and procedures.

If you have any disability that may impair your ability to complete this course successfully, please contact Disability Services as soon as possible, preferably before the beginning of the semester, in order to ensure that you receive appropriate accommodations.

Disability Services Contact Information:

Other University Policies

Go to the Student Resources module to review the university policies regarding honesty, online etiquette, communication expectations, and so on.

Course Summary:

Date Details Due