Schedule • Grading • Activities • Reading • Individual Homework • Project • Teams • Resources
In this class you'll learn how to think like a data scientist. You'll learn what data scientists do and how they do it. You'll also learn about the contexts in which a data scientist exists. By the end of the course, you should be able to enter any organization and begin to understand the social and technical contexts in which you help make decisions. If you want to be a great data scientist, this is the course for you.
Learning objectives for the course:You should have aspirations to be a data scientist or to work closely with them. Because we'll use data to inform decisions, you should also know:
The prerequisite course, INFO 201 (the Technical Foundations of Informatics), should be suitable preparation for the above. Refer to the INFO 201 online book to refresh your knowledge of the course.
We are available to talk about jobs, careers, graduate school, research, class, taboos, and anything else. Greg's office hours this quarter will be held twice a week, Monday 12:30pm-1:30pm and Wednesday 5:30pm-6:30pm, both at CSE 3rd Floor Breakout area next to the stairs (large whiteboard wall and windowed area). Umang's office hours this quarter are twice weekly ,Tuesday - 11am to 12am (MGH Commons) and Thursday 11 am to 12 am (MGH Commons). Occasionally we need to schedule things over office hours. To guarantee we'll be around, write to us in advance to secure a time.
We will use smartphones and laptops throughout the quarter to facilitate activities and project work in-class. However, research and student feedback clearly shows that using devices on non-class related activities not only harms your own learning, but other students' learning as well. Therefore, I only allow device usage during activities that require devices. At all other times, you should not be using your device. We'll help you remember this by announcing when to bring devices out and when to put them away.
Week 0 — What is data science? | ||
1/3 | Lecture |
Data science: welcome and opportunity
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Week 1 — Decision Making in Data Science | ||
1/8 | Lecture and Lab |
Data science is a process
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1/10 | Lecture |
Understanding domain and applying decision theory
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Week 2 — Framing your analysis | ||
1/15 | No class, holiday |
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1/17 | Lecture |
Framing: Using data and models for decisions and questions
Assigned: Project Milestone 1: Group formation & initial domain understanding. Due |
Week 3 — Modeling concepts; finding data | ||
1/22 | Lecture |
Causal Diagrams and Scoping
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1/22 | Lab |
Using Causal Loop Diagram for Scoping
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1/24 | Lecture |
PM1 Review; Ideating, finding and selecting data sources
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Week 4 — Collecting and Making Sense of Data | ||
1/29 | Lecture |
Visualizing Data
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1/29 | Lab | Lab - Review PM2, Decisions: Focus on Choices |
1/31 | Lecture |
Tidying and cleaning data
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Week 5 — Visualization; Model Fitting | ||
2/5 | Lecture |
Web scraping; Exploratory Data Analysis and PM2 examples
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2/5 | Lab | Web Scraping |
2/7 | Lecture |
Models
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Week 6 — Modeling | ||
2/12 | Lecture | Modeling as a search for "optimal" parameters |
2/12 | Lab | Fitting basic models in R |
2/14 | Lecture |
Evaluating quality of model parameters (fitted models)
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Week 7 — Interpreting models | ||
2/19 | Holiday | |
2/21 | Lecture |
Logistic Regression; Evaluating models with cross-validation
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Week 8 — Understanding Models | ||
2/26 | Lecture |
Logistic Regression; simulating decisions using models and residuals
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2/26 | Lab |
Trying different thresholds for logistic regression and simulating decisions
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2/28 | Lecture | Bias; Simulating decision using models and residuals |
Week 9 — Debugging and Limitations of Models | ||
3/5 | Lecture |
Debugging strategies for R code and Monte Carlo Simulations
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3/7 | Lecture |
Interpreting models and limitations; reflecting on class projects
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Week 10 — Project Fair | 3/13 Tues | Project Fair |
Room EEB037
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Homework 10 (Project and Course Reflection) Due 3/14. |
There are 100 points you can earn in this class:
We will use the iSchool Standard Grading Scale to convert your grade percentage (as shown in Canvas) to a 4.0 scale.
≥ 97% → 4.0 | 90.5 → 3.5 | 83.9 → 3.0 | 78 → 2.5 | 73 → 2.0* | 68 → 1.5 | 62 → 0.9 |
95.7 → 3.9 | 89.2 → 3.4 | 82.6 → 2.9 | 77 → 2.4 | 72 → 1.9 | 67 → 1.4 | 61 → 0.8 |
94.4 → 3.8 | 87.8 → 3.3 | 81.3 → 2.8 | 76 → 2.3 | 71 → 1.8 | 65 → 1.2 | 60 → 0.7*** |
93.1 → 3.7 | 86.5 → 3.2 | 80 → 2.7 | 75 → 2.2 | 70 → 1.7** | 64 → 1.1 | < 60 → 0.0 |
91.8 → 3.6 | 85.2 → 3.1 | 79 → 2.6 | 74 → 2.1 | 69 → 1.6 | 63 → 1.0 | |
*: 2.0 is the minimum grade required for any required INFO course to count towards an informatics degree. **: The UW requires a 1.7 or better for non-degree requirements for undergraduate courses. ***: 0.7 is lowest passing grade in an undergraduate course. |
Late work receives no credit unless you can provide a note from a health care professional or provost documenting the reason for your absence, or you make arrangements with the instructor. However, you can miss up to 3 activities without penalty and without documentation. This should be enough to allow for sickness, unavoidable travel, or other personal matters.
If you miss a reading quiz due to sickness, you can make up the quiz credit by sending a 250-500 word critique of the reading and submitting it to your Google Drive folder within a week of the quiz you missed. Title the Google doc with the class number and "make up quiz". E.g. "2.3 make up quiz" for the make up quiz for week 2 and class 3/wednesday lecture.
Each day in class we'll practice some skill. You'll get 0.5 points if you engage in and complete the activity. How to get credit for the activity will depend on the activity; sometimes being present will be enough, sometimes being to class on time will be enough, and sometimes you'll have to turn something in.
To access the readings, you will do the following:
You should complete your readings and reflection before at the beginning of each lecture (twice a week). The Google Doc in your personal Drive folder is your submission (not using Canvas for readings). Each class, you'll come prepared to discuss the assigned reading.
The day that each reading is due, we'll do the following:
You will receive 0.75 points for completing the reading and reflection before class (on the Google Doc). You will receive up to another 0.25 points for getting the in-class reading quiz correct. We will give partial credit for partially correct answers on the reading quiz, at our discretion. In total, you can receive up to 1 point per reading.
There will be about one individual homework assignment each week, which are separate from reading assignments and project milestones. These will give you practice and feedback on the skills in a narrower context than your project. They will be due on the nearest Sunday.
All homeworks are due by 11:59:00 PM PST on the specified date.
The goal of the individual homework assignments is to check and deepen your understanding of specific concepts which are critical to your understanding of data science.
The project is split across 8 milestones/assignments, each worth a different amount:
All assignments except the Project check-in meeting are due by 11:59:00 PM PST on the specified date.
The goal of the project is for you to practice the process of data science to make or inform a decision, so you can experience the nuances of formulating a good question, setting up process, constraints, and plans in relation to a context. Note, however, that because the timeline for the project is so short, it won't give you a deep, longitudinal experience with data science, nor will it give you practice with massive complexity or scale. I believe these are experiences best left to practice in industry, as they're very difficult to replicate in the artificial setting of school.
Links to Data Science communities at/near UW:
Links to recommended learning resources (most of which are free)
Links to important UW resources: