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WSP 312 — Tips and Tricks for Data Scientists: Optimizing Your Workflow

Quarter: Spring
Day(s): Saturday
Course Format: On-campus course
Duration: 1 day
Date(s): Apr 29
Time: 10:00 am—4:00 pm
Drop Deadline: Apr 22
Unit: 0
Tuition: $285
Instructor(s): Jonathan Whitmore
Limit: 25
Status: Open
On-campus course
10:00 am—4:00 pm
Apr 29
1 day
Drop By
Apr 22
0 Unit
Jonathan Whitmore
In data science, many conceptual and technical skills require deep thinking and understanding. But as a practicing data scientist, you might find that, day-to-day, a substantial amount of time is spent working on data cleaning and other inglorious yet critical tasks. In this one-day workshop, you will learn best practices to optimize your daily workflow.

The workshop will focus on using free and open source tools. In particular, Project Jupyter—encompassing several essential tools (JupyterHub, the newly released JupyterLab, Notebooks, and Notebook extensions)— gives us a flexible and extensible data science environment. We will emphasize how to be productive in this environment, and give a brief overview of how the scientific Python stack (SciPy, pandas, scikit-learn, statsmodels, and more) integrates well with this environment. Through interactive lecture and hands-on work where students will use their own laptops to practice the techniques being taught, we will focus on efficiency in tool usage and best practices for data science teams to collaborate and produce internally reviewed and externally consistent results.

This workshop is designed for practicing data scientists and those just starting to work in the field. Students are required to bring a laptop computer to class.

Due to its short format, this workshop may not be taken for Credit or a Letter Grade.

Jonathan Whitmore, Senior Data Scientist, Silicon Valley Data Science

Jonathan Whitmore received a PhD in physics from UC San Diego and completed an astrophysics postdoc at Swinburne University in Melbourne, Australia. He is the author of the O’Reilly Media screencast Jupyter Notebook for Data Science Teams and appeared in the 3D IMAX astronomy documentary Hidden Universe. He was a 2014 Insight Data Science Fellow and remains an active mentor.

Textbooks for this course:

There are no required textbooks; however, some fee-based online readings may be assigned.