Welcome to CSE 163: Intermediate Data Programming! π
What is this class? What will I learn?
The world has become data-driven. Domain scientists and industry increasingly rely on data analysis to drive innovation and discovery; this reliance on data is not only restricted to science or business, but also is crucial to those in government, public policy, and those wanting to be informed citizens. As the size of data continues to grow, everyone will need to use powerful tools to work with that data.
This course teaches intermediate data programming. It is a follow on to CSE142 (Computer programming I) or CSE160 (Data Programming).
The course complements CSE143, which focuses more deeply on fundamental programming concepts and the internals of data structures. In contrast, CSE163 emphasizes the efficient use of those concepts for data programming.
In this course, students will learn:
- More advanced programming concepts than in CSE142 or CSE160 including how to write bigger programs with multiple classes and modules.
- How to work with different types of data: tabular, text, images, geo-spatial.
- Ecosystem of data science tools including Jupyter Notebook and various data science libraries including scikit image, scikit learn, and Pandas data frames.
- Basic concepts related to code complexity, efficiency of different types of data structures, and memory management.
Prerequisites and Expectations
This is class is designed as the second introductory programming course that focuses on writing programs that work with data. The prerequisites for the class require students having taken CSE 142 or CSE 160 and the class has been designed to be accessible to students from either of those backgrounds. Students that have taken 143 are welcome to take this class as it will serve as a complement to the material learned in 143 with only minor overlap.
Because this course will have students coming from many different class backgrounds, the first couple of weeks will be pretty different for students depending on what classes they have taken. Here is what we expect students to see in the first weeks based on their background:
- 142: The first two weeks might go pretty fast, but will be doable since you already know all the concepts (loops, conditionals, methods) and you are just learning all the new “words” in Python to use those concepts. This might require a little bit of extra practice early in the quarter so you are familiar with translating all the ideas you have learned in 142 to this new language. The first week has been designed to be a recap of all things 142 so you don’t also have to be learning a ton of new material while learning a new language in the first week.
- 160: The first week will just be a bit of a review for you, but the class will start covering material you haven’t seen before starting in the second week.
- 143: You are in a similar boat as the 142 students, where you know a lot of the concepts but don’t know the Python language. You’ll probably see a few things that you saw in 143 in this class, but I think the new context of processing data in a new language will still keep it new, exciting, and challenging.
If you want to learn more about the course and its policies, please check out our course syllabus.
Feedback
Feedback is always welcome! You can contact the the course staff or submit anonymous feedback.
Registration
Do not email the course staff or instructor requesting an add-code for the course. The course staff do not have any add-codes. Please email ugrad-advisor@uw.edu.
Announcements¶
May 19 Resubmissions (May 4 - May 10) Released
Feedback from last resubmission form processed
See the full announcement on Ed! May 17 A4 Feedback Released
Assignment 4 feedback released.
See the full announcement on Ed! May 17 Resubmission (May 18 - May 24)
Resubmission form for this week.
See the full announcement on Ed!This Week (at a glance)¶
Monday (05/30)
- Memorial Day! No class! Optional lesson here
Tuesday (05/31)
- π Weekly resubmission period closes @ 11:59 pm
- π Checkpoint 8: Special Topics due @ 11:59 pm.
Wednesday (06/01)
- π Lesson 28: Algorithmic Privacy. Due for Lesson EC on Wednesday (06/01) @ 11:59 pm.
- π₯ Class Session @ 11:30 pm in GUG 220.
Thursday (06/02)
- π§βπ« Quiz Section 9: TA's Choice!
- π» Final Project: Deliverables - Code/Report due at 11:59 pm
Friday (06/03)
- π Lesson 29: Victory Lap & Next Steps. Due for Lesson EC on Friday (06/03) @ 11:59 pm.
- π₯ Class Session @ 11:30 pm in GUG 220.
- Last day of class
- π Checkpoint 9: Fairness and Privacy released. Due Monday (06/06) @ 11:59 pm.
Calendar¶
For students not currently in CSE 163, you can view the Lesson material here. For students who are in the class, you can find the link to the relevant lesson below.
Info
This is a rough sketch of the quarter and things are subject to change. We can accurately predict the past, but predicting the future is hard!
Lessons
Anything listed in the “Lesson” materials for a day should be read before attending class that day. We recommend doing all the slides before the “Pause and Think” slide. Each class session will start by reviewing what was in the Lesson and then most time will be spent on working on practice problems in the Lessons. See the syllabus for more info!
Topic | Take-home assessments | Checkpoints | ||
---|---|---|---|---|
Module 0 - Python Fundamentals | ||||
Mon 03/28 | LES 00 Intro to CSE 163; Intro to Python Note: Normally you would complete the Lesson reading portion before class. There is nothing you need to complete before class today! Complete by EOD today for EC. | |||
Wed 03/30 | LES 01 Control Structures | |||
Thu 03/31 | SEC 00 Welcome to Section! resources: handout | |||
Fri 04/01 | LES 02 Strings and Lists | |||
Released A0 I.S. by 11:59 pm Startup | Released CP0 Due 11:59 pm Intro/Review Python | |||
Module 1 - Data Structures and Files | ||||
Mon 04/04 | LES 03 File Processing | |||
Wed 04/06 | LES 04 Data Structures | |||
Thu 04/07 | SEC 01 Python Practice resources: handout | |||
Fri 04/08 | LES 05 CSV Data | |||
Released A1 I.S. by 11:59 pm Primer | Released CP1 Due 11:59 pm Data Structures and Files | |||
Module 2 - Pandas | ||||
Mon 04/11 | LES 06 pandas : DataFrame s | |||
Wed 04/13 | LES 07 pandas : Group-by and Apply | |||
Thu 04/14 | SEC 02 pandas | |||
Fri 04/15 | LES 08 Time Series | |||
Released A2 I.S. by 11:59 pm Pokemon | Released CP2 Due 11:59 pm pandas | |||
Module 3 - Data Science Libraries | ||||
Mon 04/18 | LES 09 Data Visualization | |||
Wed 04/20 | LES 10 Introduction to Machine Learning | |||
Thu 04/21 | SEC 03 Data Science Libraries | |||
Fri 04/22 | LES 11 ML cont.; ML and Society | |||
Released A3 I.S. by 11:59 pm Education | Released CP3 Due 11:59 pm Data Science Libraries | |||
Module 4 - Classes and Objects | ||||
Mon 04/25 | LES 12 Objects | |||
Wed 04/27 | LES 13 More Objects | |||
Thu 04/28 | SEC 04 Classes and Objects | |||
Fri 04/29 | LES 14 Search Prep | |||
Released A4 I.S. by 11:59 pm Search | Released CP4 Due 11:59 pm Classes and Objects | |||
Module 5 - Efficiency | ||||
Mon 05/02 | LES 15 Algorithmic Efficiency | |||
Wed 05/04 | LES 16 Profiling and Program Speed | |||
Thu 05/05 | SEC 05 Office Hours | |||
Fri 05/06 | LES 17 Computer Memory | |||
Released PROJ Due 11:59 pm Proposal | Released CP5 Due 11:59 pm Efficiency | |||
Module 6 - Geospatial data | ||||
Mon 05/09 | LES 18 Geospatial Data | |||
Wed 05/11 | LES 19 Dissolve and Joins | |||
Thu 05/12 | SEC 06 Geospatial Data | |||
Fri 05/13 | LES 20 Indexes / Trees | |||
Released A5 I.S. by 11:59 pm Mapping | Released CP6 Due 11:59 pm Geospatial Data | |||
Module 7 - Images | ||||
Mon 05/16 | LES 21 numpy and Images | |||
Wed 05/18 | LES 22 Convolutions | |||
Thu 05/19 | SEC 07 Images | |||
Fri 05/20 | LES 23 Machine Learning and Images | |||
Released PROJ Due 11:59 pm Deliverables - Report/Code | Released CP7 Due 11:59 pm Images | |||
Module 8 - Special Topics | ||||
Mon 05/23 | LES 24 Hashing | |||
Wed 05/25 | LES 25 Ethics | |||
Thu 05/26 | SEC 08 Statistics and p-hacking | |||
Fri 05/27 | LES 26 Algorithmic Fairness | |||
Released CP8 Due 11:59 pm Special Topics | ||||
Module 9 - Data Science and Society | ||||
Mon 05/30 | HOLIDAY Memorial Day Note: This reading is optional. Everyone already earned a Lesson EC for today, regardless of whether or not you do today's reading. lesson: lesson | |||
Tue 05/31 | ||||
Wed 06/01 | LES 28 Algorithmic Privacy | |||
Thu 06/02 | SEC 09 TA's Choice! | |||
Fri 06/03 | LES 29 Victory Lap & Next Steps | |||
Released CP9 Due 11:59 pm Fairness and Privacy | ||||
Module 10 - Finals Week | ||||
Mon 06/06 | ||||
Tue 06/07 | ||||
Released PROJ Due 2:30 pm Data Science Fair | ||||
Wed 06/08 | CSE 163 Data Science Fair Room TBD (2:30 - 4:20 pm) | |||
Thu 06/09 | ||||
Released PROJ Due 11:59 pm Peer Feedback | ||||
Fri 06/10 | ||||