Foundational statistical and computational techniques, and recognizing how and when to apply to real scenarios.
Establishing causality and the use of randomization to build an experiment.
Understanding basic programming concepts such as the use of expressions, names, data types, and arrays.
Applying programming skills with arrays, ranges and tables (sort and select data).
Using data visualization techniques for categorical and numerical data.
Developing functions programmatically to work with tables.
Grouping/classifying data by one variable, including joining tables.
Using iteration (loops) and conditional statements for simulation.
Using tables, and simulation to understand core probability concepts.
Exposure to basic models, testing hypotheses.
Comparing distributions, decisions and uncertainty (Visualization and Data Literacy).
A/B Testing and the significance of results.
Estimating by using percentiles, Bootstrap and Confidence Intervals.
Modeling by using the normal distribution and understanding sample means.
Predicting by using:
Correlation and Linear Regression
Method of Least Squares
Regression Inference
Classifying by using Classifiers:
Nearest Neighbor method
Training and Testing
Rows of Tables, implementing the classifier
Decision making: How new data leads to update our predictions and therefore classifiers.
Homework on Unemployment Data
The Federal Reserve Bank of St. Louis publishes data about jobs in the US. Below, we've loaded data on unemployment in the United States. There are many ways of defining unemployment, and our dataset includes two notions of the unemployment rate:
Using python in a jupyter notebook complete the following:
To do this, create a new table called pter_over_time that adds the year array and the pter array to the unemployment table. Label these columns Year and PTER. Then, generate a line plot using one of the table methods you've learned in class.