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Data Science with Social Science data: an introduction to Pandas and StatsModels in Python

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This workshop introduces participants to Python’s NumPy, Pandas DataFrames, Matplotlib and StatsModels using an advertising dataset. Participants will use these tools to model (OLS) associations between advertising expenditures and product sales in example data. We will start with an introductory explanation of Anaconda and the Jupyter notebook environment (although not required for the participant, the instructor will be using these tools). We will proceed with topics including: reading data files; creation, indexing and slicing of Pandas DataFrames; creation and handling of Matplotlib objects; and creation and interpretation of models using Python’s StatsModels. Although not required, we recommend that participants have a basic knowledge of Python.

Mixed models in Python

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Mixed models (also known as multilevel models or random effects models) are used in research involving data with repeated measures per observation unit.  The Python statsmodels package is able to fit a variety of mixed models including variance components models, models for longitudinal data, and models with crossed effects.  We will cover the basics of mixed models, then present examples highlighting the capabilities of this Python package.

Data Science with Social Science data: An introduction to Pandas and StatsModels in Python

By |

This workshop introduces participants to Python’s NumPy, Pandas DataFrames, Matplotlib and StatsModels using an advertising dataset. Participants will use these tools to model (OLS) associations between advertising expenditures and product sales in example data. We will start with an introductory explanation of Anaconda and the Jupyter notebook environment (although not required for the participant, the instructor will be using these tools). We will proceed with topics including: reading data files; creation, indexing and slicing of Pandas DataFrames; creation and handling of Matplotlib objects; and creation and interpretation of models using Python’s StatsModels. Although not required, we recommend that participants have a basic knowledge of Python.