# TutorialsÂ¶

This is a collection of notebooks with tips and consolidated references for the various Python and Pandas topics that we are discussing.

## Python and DataÂ¶

These notebooks are on basic Python data manipulation:

1. Fun with Numbers

2. Writing and Using Functions

3. Selecting Data

4. Reshaping Data

5. Building Data â€” building up arrays and data series

6. Indexing

7. Missing Data

8. Tricks with Boolean Series

## VisualizationÂ¶

1. Drawing Charts

2. Movie Score Charting Examples â€” example charts used in several videos in ðŸ“…Â Week 3 â€” Presentation (9/6â€“10)

3. Chart Finishing Touches

## Probability and StatisticsÂ¶

1. Empirical Probabilities (demonstration of using boolean series to compute probabilities with empirical data)

2. One Sample T-test and Distribution Comparison

3. Correlation

4. Regressions (goes with Week 8)

5. Logistic Regression

6. Sampling and Testing the Penguins

7. Linear Models with scipy minimize

8. Overfitting Simulation example

## SciKit-Learn and ML ModelsÂ¶

1. SciKit-Learn Logistic Regression

2. SciKit-Learn Pipelines and Regularization â€” also includes a significance test for difference in classifier accuracy, and a decision tree

3. Linear Regression with SciKit-Learn â€” also uses a pipeline and applies standardization

5. Dummy-Coding and Feature Combination with SciKit-Learn Pipelines

6. Another advanced SciKit-Learn pipeline and logistic regression example (on Towards Data Science)

7. K-Means Example (uses the chi-papers data from Week 13)

8. Tuning Hyperparameters

## Specific Data Set ExamplesÂ¶

These are more advanced examples of data manipulation and collection:

1. MovieLens Time Series

2. Sessionization (demonstrates some more advanced aggregation and time-based operations)

3. Spam Filter demonstrates building a spam filter

4. Using the Census describes how to access census data.

5. Fetching CHI Papers creates the chi-papers.csv file from Internet sources.

## Workflow ExampleÂ¶

This example demonstrates a complete Git-based workflow: