Week 1 — Questions (8/23–27)¶
The key learning outcomes for this week are:
Ask and refine questions that can be answered with data
Install and run the software required for the course
Write and run basic Python code in a Jupyter notebook
Begin to think about the complexity of meaningful questions
🧐 Content Overview¶
This week has the following deadlines:
This video introduces the course and our learning outcomes for the term.
🎥 Asking Questions¶
In this video, I introduce questions in their broader context of using data to advance goals. I also introduce the idea of operationalization, which will be a key concept throughout the class.
🎥 Questioning Questions¶
We make our operationalizations better by questioning them. What do they capture? Who or what do they prioritize?
In this video, I talk about asking what it takes to improve a metric. It would be more precise to ask what would change a metric; whether increasing or decreasing a metric is an improvement depends on the metric and application context. The metric itself does not improve or get worse without that context.
When I re-record this video for a future revision, I’ll change my language.
📖 Textbook Chapters - Questions¶
The first videos of this class go with chapters 1 and 2 of 📖 Think Like a Data Scientist.
👨🏫 Tuesday Class¶
On Tuesday, we will:
Introduce the class
Start working on defining and clarifying questions
Set expectations for the week and semester
✅ Install Software¶
Make sure you have installed the course software so you can complete the assignment.
🚩 Week 1 Quiz¶
The Week 1 individual quiz (in Canvas) is due at 8am Thursday. It is only over material prior to this point.
You will have 30 minutes from when you start the quiz to complete it. These quizzes are never going to be terribly long; this one is particularly designed to be pretty easy, as an initial quick check and to give you experience with the quiz process.
I recommend watching the remaining videos before class Thursday as well, but they will not be tested over in the quiz.
🎥 📓 Our First Python Notebook¶
This video shows you how to start Jupyter, create a notebook, and run Python code. It also shows you how to prepare a notebook to submit as an assignment.
I have seen cases where the instructions to export to a PDF do not work correctly in Firefox; they result in a mangled PDF file that does not correctly display charts. If you encounter this problem, I have documented some fixes over in the common problems page.
👨🏫 Thursday class¶
In class on Thursday, we will:
Meet our teams
Take the Week 1 team quiz (over the same material as the individual quiz)
Debug software installations
Activity to dive deeper into defining problems and questions
📖 Textbook Chapters - Python¶
The Python material we are working on this week is a subset of the material in chapters 1–4 of the textbook. I don’t expect to you get through all 3 chapters thoroughly this week, and we will be introducing more Python features as we need them throughout the semester. I will note specific chapters and sections relevant to videos in their Resources subsections.
🎥 Data Types and Control Flow¶
This video introduces fundamental Python data types and operations, along with variables and basic control flow.
🎥 Control Structures¶
This video introduces Python control structures and code layout.
🎥 Scientific Python¶
This video introduces NumPy
numpy.ndarray, the fundamental numeric array data structure for scientific computing.
Textbook Ch. 4