Effective immediately in response to COVID-19, most Technology Training classes will be delivered online until further notice.
In advance of each session, Tech Training will provide you with a Zoom link to your class, along with any required class materials.
Almost every aspect of our lives is now data-driven, and data analysis has emerged as one of the most in-demand professional skills. Experts estimate that millions of jobs in data science might remain vacant for the lack of available talent. The global demand for skilled data scientists is not limited to demand for statisticians or computer scientists, but representative of an ever more pervasive need to understand data in every part of an organization. Well-rounded analysts with domain expertise are critical to performing accurate analysis of big data and making effective decisions with the information gleaned from the analysis.
This requires some experience in software programming, and statistical analysis, as well as exceptional communication skills. This course leverages Python to provide users with a simple yet powerful tool for processing, organizing, examining, structuring, analyzing, visualizing, and consuming data.
Prerequisites:
- A Basic Understanding of Python, successful completion of required quiz
- Ability to install python packages on your computer and set up the Jupyter programming environment
We highly recommend that you complete the quiz hosted on Gradescope, to test your readiness for this workshop. If you score 70% or greater, you are ready to join us.
Go to Gradescope: https://www.gradescope.com/
- Click Sign Up
- Use Course Entry Code: RZRR6V
- Select Stanford University
Otherwise, we highly recommend that you first complete the Python I offered by the Stanford Technology Training program and join us at a future session.
Course participants will have an opportunity to learn:
- How to work with 15 different Data formats
- How to use four different Powerhouse Python Data Analysis Modules:
- Pandas
- SciPy
- NumPy
- Matplotlib
- How to identify sources of useful data
- How to identify the advantages and disadvantages of different data storage types
- How to develop practical skills for:
- Working with publicly available datasets
- Making your own datasets
- Cleaning Data Sets
- Simple Ways to Perform Basic Statistical Analysis on Datasets
- Ways to practice their skills through in class exercises and activities
Participants will also receive a copy of the book: Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter 3rd Edition, by Wes Mckinney
4 Modules -- 3 hours each (over two weeks)
