Most people would sooner die than think, and most of them do.
-Bertrand Russell-

Overview

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 interested 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 decisions.¹.

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. Specifically, as a successful learner, you will be able to;

1. Use functions, data structures, and other programming constructs efficiently to process and find meaning in 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 charts, and prepare data for machine learning algorithms.
4. Use machine learning libraries to discover insights, make predictions, and interpret the success of these algorithms.
5. Use industry-leading tools to collaborate and share your work.

Principles of DS teaching

The course follows these principles of teaching Data Science²

• Organize the course around a set of diverse projects
• Integrate computing into every aspect of the course
• Teach abstraction, but minimize reliance on mathematical notation
• Structure course activities to realistically mimic a data scientist’s experience
• Demonstrate the importance of critical thinking/skepticism through examples

Competency assumptions

This course focuses on programming with data to find insights. The prerequisite for this course is an introductory programming course in Python (CSE 110)³. We recommend taking CSE 111 before or during the same semester you take this course - especially if programming is complicated for you. We assume that you do know what the Terminal is and how to execute scripts.

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Course materials and structure

This course focuses on building core data science skills. You will learn to program, but you will also learn how to communicate and collaborate with your peers and mentors.

Course communication

How do I talk with my teacher, TA, and other students in this class?

1. We use Slack for most class and one-on-one communication. Don’t email or direct message using I-Learn.
   A. Should I paste code snippets in our class Slack channel to get help? Yes.
   B. Should I ask questions about the projects and the readings in our class Slack channel? Yes.
   C. Should I post random quotes or videos in our class Slack channel? No. Use the #random channel.
2. All assignments are submitted in I-Learn.
   A. Each project submission requires you to submit a short message to the teacher about your work.
   B. We will respond to your message with edits you can make to earn full credit on your resubmit.
   C. Class announcements about the grading of projects are posted in I-Learn.

Online reading materials

• Python for Data Science: A port of R for Data Science using the Python packages pandas and Altair.
• pandas User Guide
• Altair User Guide
• Python Data Science Handbook
• SQL by data.world

Preparation

In my experience, getting lectured training outside of college is even more expensive than it is in college. A week’s worth of training can cost more than a semester of school here at BYUI. Due to this expense, learning how to digest online material gain understanding before going to the expert with questions is a valuable skill to develop. I expect that you have completed the assigned reading material before class begins.

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 we 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/fail basis using detailed checklists of task requirements and expectations⁴. 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.