CSE 163

Victory Lap + Next Steps�

Suh Young Choi�

🎶 Listening to: Pacific Rim soundtrack

💬 Before Class: What was the coolest thing you did or learned this quarter?

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You did it!!

Overview

What is this class?

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Competencies

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1. More advanced programming concepts than in CSE 122 or CSE 160 including how to write bigger programs with multiple classes and modules.�
2. How to work with different types of data: tabular, text, images, geo-spatial, etc.�
3. Ecosystem of data science tools including Jupyter Notebook and various data science libraries including scikit-image, scikit-learn, and pandas data frames.�
4. Basic concepts related to code complexity, efficiency of different types of data structures, and memory management.

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• Foundations of data literacy and technical communication for critical and conscientious data science.

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Competency 1

More advanced programming concepts than in CSE 142 or CSE 160 including how to write bigger programs with multiple classes and modules.

• Testing code
• Data structures: Lists, sets, dictionaries, tuples
• Classes and Objects
• Modules and Packages
• Anonymous functions (lambdas) and functional programming

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Competency 2

How to work with different types of data: tabular, text, images, geo-spatial, etc.

• Tabular
• Unstructured Text
• Geo-spatial
• Images
• Joining data from multiple sources

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Competency 3

Ecosystem of data science tools including Jupyter Notebook and various data science libraries including pandas, matplotlib, and scikit-learn�

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• Jupyter Notebooks
• Pandas
• Seaborn / Matplotlib
• Scikit-learn
• Geopandas
• Numpy
• Imageio

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Competency 4

Basic concepts related to code complexity, efficiency of different types of data structures, and memory management.

• Algorithmic efficiency
• Python language quirks
• Computer memory, objects, and references
• Terminal

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Competency 5

Foundations of data literacy and technical communication for critical and conscientious data science.

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• Reading assignments
• Peer reviews in creative components and presentations
• Writeups and reflection posts
• Ethics discussion and case studies
• Humanistic computing
• Final project/portfolio
• Final reflection (coming soon!)

Data Programming’s Interface with Data Science

While our course is focused as a course in programming, we have a close relationship with the analysis done in data science. We explored how to support data science in many of our discussions of programming and and its impact.

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• Principles of data visualization
• Data literacy and communication
• Machine learning
• Neural networks
• Convolutions
• Positive/Negative impacts of data science and society
• Privacy
• Algorithmic Fairness
• Statistics, hypotheses, and research

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What’s Next?

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Data Programming�

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data programma

Writing out what is given

Data Science�

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data sciens

Knowing what is given

Future Classes

Machine Learning

• CSE/STAT 416
• STAT 435
• INFO 371

Societal Implications of Data Science

• SOC 225
• STAT 303

Data Management

• CSE 414
• INFO 430

Data Visualization

• CSE 412
• INFO 474
• HCDE 411

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Data Science Courses at UW

Data Science Minor - Course Search

Online Resources

The internet is teeming with tutorials and online classes that teach topics in data science and data processing!

• Coursera
• Allen School Course Catalog
• Berkeley Data8 and DS100
• Towards Data Science (on Medium, hit or miss)
• Codecademy

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Projects

It’s not really possible to list out all the things you can do with what you’ve learned in this class. I usually do a project to learn a new tool.

• Learn a new library!
• Data Visualization: Bokeh or Altair
• Natural Language Processing: NLTK or spaCy
• Machine Learning: Tensorflow, Keras, PyTorch
• Images: Open-CV
• Learn a new Language! Python is the most prominent language for data science, but it doesn’t hurt to know more!
• R - Numerical Processing
• Scala - Compatible with Java, nice syntax
• Julia - New and up-and-coming language
• Javascript - Language of the web
• Contribute to open-source libraries!

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You have already done a personal project this quarter!

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Research

You are all in the very unique experience of having some real marketable skills for doing undergrad research!

• You have learned the language and the tools used by many researchers across the university

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• Research opportunities may be found on Interfolio or Handshake, or by cold-emailing (even if it’s scary!)

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• You can also pursue projects of your own, in whatever area is interesting to you!

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Jobs

The field for jobs in computing is rapidly expanding

• A quickly growing subset of these jobs are ones that focus on processing large amounts of data
• Data Scientist is a relatively new term to describe these people

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How to get a job in tech?

• Polish up your resume (perhaps with a cool project you’ve done recently?)
• Career fairs around campus
• Start looking early!
• Cast a broad net and don’t be scared of getting turned down

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Become a CSE 163 TA!

• Teaching is one of the best ways to learn!
• You don’t have to be an expert in Python to be a TA
• Applications will open later this Spring
• Reach out to me or any of the TAs for more advice on preparing for the application and/or interviews!

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BIG Thanks�to the TAs 🎉

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TA Panel

Ask Us Anything!

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Reminders: Final Reflection

• Thursday, March 19 at 8:30am in Gowen 301 (this room!)
• Prompts and rubric can be found on the course website

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• What to bring:
• Writing utensil(s)
• Husky ID
• Water, if needed
• What not to bring:
• Notes / Reference sheets
• Electronic devices (keep them in your backpack)

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🔎 Scavenger Hunt!��What icons from the title slide can you find in the website lessons?