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CSE 163

Victory Lap & Next Steps

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Arpan Kapoor�Summer 2026��💭Icebreaker (discuss with neighbors):

How are you planning on explore data programming after this course?

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slido.com

#2348882

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Announcements

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  • Resubmission Cycle 6 due Friday, August 21st at 11:59pm
    • HW5 grades have been released!
  • Final Exam on Friday, August 21st at 1:10pm in HRC 155
  • Extra office hours tomorrow and Friday before the exam

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3

You did it!!

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Class Overview

  • More advanced programming concepts than CSE 122 or CSE 160 including how to write bigger programs with multiple classes and modules.
  • How to work with different types of data: tabular, text, images, geo-spatial, etc.
  • 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.
  • Foundations of data literacy and technical communication for critical and conscientious data science

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Data Programming’s Interface with Data Science

  • 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

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

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6

What Next?

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Future Classes

  • Machine Learning
    • CSE/STAT 416 - Intro to Machine Learning
    • STAT 435 - Intro to Statistical Machine Learning
    • INFO 371 - Advanced Methods in Data Science
  • Societal Implications of Data Science
    • SOC 225 - Data and Society
    • STAT 303 - Intro to the Ethics of Algorithmic Decision Making
  • Data Management
    • CSE 414 - Intro to Database Systems (non-majors)
    • INFO 430 - Database Design and Management
  • Data Visualization
    • CSE 412 - Intro to Data Visualization (non-majors)
    • INFO 474 - Interactive Information Visualization
    • HCDE 411 - Information Visualization

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Online Resources

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

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Projects

  • 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!
    • 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 or projects!

It’s not really possible to list all the things you can do with what you’ve learned in this class but learning a new tool through a project is a good way to continue building your skill set

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Research

  • You have learned the language and the tools used by many researchers across the university
  • Research opportunities can be found on Interfolio, Handshake, or by cold-emailing or reaching out to professors.
    • Please be respectful of their time and don’t be discouraged if they do not reply
  • You can also pursue projects of your own, in whatever area is interesting to you!
    • What problems, questions, or topics excite you?
    • What skills do you want to develop?

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

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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
  • Keep an eye out for the application deadlines!
  • Reach out to me or any of your TAs for more advice on preparing for the application and/or interviews!