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CSE 163

Victory Lap + Next Steps�

Suh Young Choi�

🎶 Listening to: Queen

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

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Overview

What is this class?

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Competencies

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  1. More advanced programming concepts than in CSE 142 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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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 scikit-image, scikit-learn, and pandas data frames.�

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

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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

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  • Why Python is (sometimes) slow and why that’s okay

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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

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Data Science�

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data sciens

Knowing what is given

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Future Classes

Web Programming: CSE 154

Machine Learning

  • CSE/STAT 416
  • STAT 435
  • INFO 371

Societal Implications of Data Science

  • SOC 225

Data Management

  • CSE 414
  • INFO 445

Data Visualization

  • CSE 412
  • INFO 474
  • HCDE 411

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Data Science Courses at UW

Data Science Minor - Course Search

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Online Resources

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

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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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  • Talk about what you know about processing data in Python, and you’ll be surprised by the number of professors that NEED your help

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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 Autumn
  • Reach out to me or any of the TAs for more advice on preparing for interviews!

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BIG Thanks

to the TAs 🎉

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TA Panel

Ask Us Anything!

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🔎 Scavenger Hunt!��What icons from the title slide can you find throughout Ed?