Python+Data

Data-Science Basics Unit

Learning Objectives/Outcomes:

Students use CSV data files to populate structures in NumPy (ND arrays) and Pandas (data frames).
Students use those libraries to access, modify, filter, and sort data and clean datasets.
Students must be proficient in the six skills listed in Prerequisite Python skills.

Data Visualization Unit

Learning Objectives/Outcomes:

Students will use matplotlib and seaborn to make varioys types of graphs to represent and analyze multiple datasets.
Students use up to six datasets available through assignment resources or datasets Data.world, Kaggle, Data.gov, Datahub.io, GitHub, Google, etc. listed in Datasets.

Data-Science Statistics Unit

Learning Objectives/Outcomes:

Students will:
- be abble to use scikit to create machine-learning models using datasets from classroom and on-line sources;
- study the underlying mathematics then write code to create linear and higher-order regression models and use them in predictions; and
- write code to create classification and clustering models and use them to detect and to describe patterns in data.

Using Data Responsibly Unit

This unit of Python+Data is adapted from Bootstrap Data Science Unit 5: Using Data Responsibly. There are four sections to the unit covering ethical considerations in the collecting and use of data, issues of data integrity, and testing and ensuring data validity.

Data-Science Algorithms Extension Unit

Use advanced machine-learning algorithms to identify features of data, understand categories of data, and classify data. This unit is meant as an extension unit for students who have completed the previous units to explore the mathematics behind more advanced algorithms.

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