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

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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.

Data Processing and Visualization in Python

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This workshop will delve into common data processing and exploration techniques. We will use NumPy, Matplotlib and Pandas to perform data exploration in Python. Among others, we’ll demonstrate how to load data files, sort data, group variables, handle missing values, merge/join datasets and create common plots. Although not required, we recommend all participants to have a basic knowledge of Python.

Web Scraping with Python: Twitter and Google APIs

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This workshop will cover how to obtain data from Twitter and Google APIs. We’ll cover how to search historical tweets and also obtain data from future tweets using the streaming Twitter API. We will also cover the Google Maps API and APIs related to social media among the wide variety of Google APIs. The workshop is intended for users with basic Python knowledge. Anaconda Python 3.5 will be used.

Web Scraping with Python: HTML

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This workshop will provide an overview of how to scrape data from html pages and website APIs using Python. For demonstration purposes, we will scrape sports and weather data. This will mostly be accomplished using the Python requests, beautifulsoup and retry modules. The workshop is intended for users with basic Python knowledge. Anaconda Python 3.5 will be used.

Regular Expressions II

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Regular expressions are perfectly suited for people who like puzzles. Regular expressions are a sequence of characters used to define a search pattern. They are commonly used to do “find” and “find and replace” string operations. They are also used to validate strings like phone numbers, passwords, etc. in data entry. Regular expression capabilities can be found in a variety of programming languages and software like ArcGIS, Java, Javascript, Matlab, Perl, PHP, Python, R, Visual Basic, etc. and some text editors. This workshop will cover more advanced topics like captured groups, backreferences and assertions, as well as practical data problems. The workshop will consist of hands-on example problems. Basic understanding of regular expressions is required. You should be able to understand expressions like “\w{3,}-\d{1,2}-\d{4}“ and “des+e?rt”. The tutorials will be conducted using Python. A basic programming background is helpful but not required for this workshop.

Data science with social science data

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This workshop covers the essential steps to data analysis in Python, using social science data as a case study. The workshop is divided into two parts. The first session includes an introduction to Python’s numpy and Pandas data analysis library. This session requires no previous experience with python. We will cover common steps involved in any data analysis: from loading the data to running a regression and interpreting outcomes.

The second session requires some background knowledge in python provided by the first session. The second session covers more advanced features, from various potential preprocessing steps to using Machine Learning Scikit-learn tools to analyze the data. As in the first session, we will be using an example from the social sciences.

The two sessions will be held in a computer lab and participants will be able to work either individually or in small groups on a few practice exercises.

Data science with social science data

By |

This workshop covers the essential steps to data analysis in Python, using social science data as a case study. The workshop is divided into two parts. The first session includes an introduction to Python’s numpy and Pandas data analysis library. This session requires no previous experience with python. We will cover common steps involved in any data analysis: from loading the data to running a regression and interpreting outcomes.

The second session requires some background knowledge in python provided by the first session. The second session covers more advanced features, from various potential preprocessing steps to using Machine Learning Scikit-learn tools to analyze the data. As in the first session, we will be using an example from the social sciences.

The two sessions will be held in a computer lab and participants will be able to work either individually or in small groups on a few practice exercises.

 

Geospatial analysis with Python

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This workshop will cover basic geospatial analysis in Python. Topics covered will include reading and writing various GIS file formats (shapefile, KML, geojson, csv), geocoding, common geometric operations like finding closest line to a point, point in polygon, spatial indexing and spatial joins etc. The workshop will focus solely on vector data (points, lines, polygons).  The will be mostly accomplished using the Python modules: fiona, shapely, rtree (but not arcpy).

Data management and analysis with Python and Pandas

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Pandas aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python. Topics will include how to read various dat formats (csv, excel, databases, etc), clean, manipulate, analyze, graph and write results to an output file. Real world data will be used. The workshop is intended for users with basic Python knowledge. Anaconda Python 3.5 will be used.

Registration  http://ttc.iss.lsa.umich.edu/ttc/sessions/data-management-and-analysis-with-python-and-pandas/