Monday, March 23

We'll hold a virtual class meeting to look over the DataViz4 and DataViz5 notebooks.

Introduction to Python, Part IV

We'll look at code for DataFrames and merging data sources, and then a linear model.

Introduction to Python, Part III

We'll look at code to visualize linear models using the seaborn library.

Another Python Learning Resource

This is a self-paced Introduction to Python online course that connects to some of IBM's resources (such as their Watson Studio). If you are looking for additional programming guidance and like self-directed learning, give it a try.

https://cognitiveclass.ai/courses/python-for-data-science

Introduction to Python, Part II

Today, we'll look at libraries, methods, and loops.

Colab link in the comments.

Assignment 4: Data Story

Assignment 4 will be the 5 minute data story you present in class on Wednesday, 3/4.

Introduction to Python, Part I

We'll discuss Jupyter notebooks and step through this first notebook on Google's Colaboratory site.

You can interact with the code at Google Colab or you can download the file and open it in your own copy of JupyterLab.

Recipe #3: Scatterplots and Regression Lines

This recipe will show how to use Tableau to explore a linear relationship between two numeric variables. (In Tableau's vocabulary, these are measures rather than dimensions.)

We'll use the following data: https://www.dropbox.com/s/5yxs2rk1gejk9dx/USstates.xlsx?dl=0

1. Open a new workbook in Tableau.

Activity: Dashboard & Narrative

Using the regional religion data, create a visualization and narrative as a dashboard and attach the image to this page. We'll discuss the results.

Information about the data: https://doi.org/10.17605/OSF.IO/SPQBC

Recipe #2: More on Tableau Dashboards

We're going to use the American National Election Study as a data source to demonstrate visual communication with Tableau dashboards.

1. First step is to learn the basics about the data source. Visit the ANES site and try to answer the following questions:
a) Who collects the data?
b) How are the data collected? and,
c) What can we say about the quality of the data source?

You should do this with every data source you consider for your project.

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