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The UBC Scientific Software Seminar is inspired by Software Carpentry and its goal is to help students, graduates, fellows and faculty at UBC develop software skills for science.

• What are the learning goals?
  • To develop software skills for managing data files and collaborative software projects with the Bash shell and Git/GitHub
  • To learn Python programming for scientific computing
  • To learn mathematics and statistics applied to data science and machine learning
  • To meet and collaborate with other students and faculty interested in scientific computing
• What software tools are we going to use?
  • Bash Shell: working with the file system from the command line
  • Git/GitHub: managing projects locally from the command line using Git and collaborating online with GitHub
  • Python: datatypes, logic, loops, functions, classes and objects
  • Jupyter Notebooks: execute code with accompanying text, markdown and LaTeX all in the browser
  • SciPy Stack: scientific computing with NumPy, SciPy, matplotlib and pandas
  • scikit-learn: machine learning in Python
• What scientific topics will we study?
  • Calculus, linear algebra, probability and statistics
  • Data wrangling, analysis and visualization
  • Basic machine learning
• Where do we start? What are the prerequisites?
  • Basic calculus, linear algebra, probability and statistics
  • Basic programming experience in any language (in particular, familiarity with logic, loops and functions)
  • No prior experience with the command line is required
  • No prior experience with Git/GitHub is required
• Who is the target audience?
  • Everyone is invited!
  • If the outline above is at your level, perfect! Get ready to write a lot of code!
  • If the outline above seems too intimidating, come anyway! You'll learn things just by being exposed to new tools and ideas, and meeting new people!
  • If you have experience with all the topics outlined above, come anyway! You'll become more of an expert by participating as a helper/instructor!

Summer 2016 will consist of six 2-hour seminars held weekly from mid-July until the end of August:

• Week 1 - Wednesday July 20 - 10am-12pm - IBLC 261 [Notes]
  • JupyterHub
  • Jupyter Notebooks
  • Git/GitHub
• Week 2 - Wednesday July 27 - 10am-12pm - IBLC 261 [Notes]
  • Git/GitHub: clone, push and pull, and collaborate
  • Bash commands for navigating the Linux file system
  • Python datatypes: int, float, str, list, bool
• Week 3 - Wednesday August 3 - 10am-12pm - IBLC 261 [Notes]
  • Bash commands and scripts
  • Python: logic, loops and functions
  • A quick intro to NumPy and matplotlib
• Week 4 - Wednesday August 10 - 10am-12pm - IBLC 261 [Notes]
  • A tour of the SciPy stack: NumPy, SciPy, matplotlib and pandas
• Week 5 - Wednesday August 17 - 10am-12pm - IBLC 185 [Notes]
  • SciPy Example: Fitting Wind Speed Distributions to the Weibull Distribution
  • A tour of scikit-learn (following example notebooks by Gael Varoquaux)
• Week 6 - Wednesday August 24 - 10am-12pm - LSK 201 [Notes]
  • A basic machine learning example:
    • Digits dataset
    • Our own k-nearest neighbors classifier
    • Evaluating the classifier
    • Implementing sklearn.neighbors.KNeighborsClassifier