Session: March 2021
To launch the material for this course, click on the Binder link:
Adjustments may be made to the outline based on student needs. The working plan is as follows:
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DAY 1
- Python Essentials, part 1
- Break and programming exercises
- Pandas Series
- Break and programming exercises
- Pandas DataFrames
- Break and programming exercises
- Review and questions
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DAY 2
- Continuing Python essentials
- Break and programming exercises
- The Ethics of Visualization
- Break and thought exercises
- Visualization using Pandas
- Break and programming exercise
- Advanced Pandas: Groupby and Timeseries
- Break and programming exercise
- Review and questions
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DAY 3
- Seaborn statistical plots
- Break and programming exercises
- Linear and Polynomial Fitting
- Break and programming exercises
- Data Analysis for Machine Learning
- Break and programming exercises
- Review and evaluation
- Cleaning Data for Effective Data Science: Doing the Other 80% of the Work. David Mertz, Packt Publishing, 2021
- Visual Explanations, Edward Tufte, 1997
- Data Visualization: A Practical Introduction, Kieran Healy, 2019
- Python Data Science Handbook, Jake VanderPlas
- Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, Wes McKinney
- Introduction to Machine Learning with Python: A Guide for Data Scientists, Andreas C. Müller & Sarah Guido