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Python for Data Science Essential Training

Video Introducing this tutorial

Introduction

Welcome
What you should know
Getting started with Jupyter
Exercise files

1. Data Munging Basics

Filter and select data
Treat missing values
Remove duplicates
Concatenate and transform data
Group and aggregate data

2. Data Visualization Basics

Create standard line, bar, and pie plots
Define plot elements
Format plots
Create labels and annotations
Create visualizations from time series data
Construct histograms, box plots, and scatter plots

3. Basic Math and Statistics

Use NumPy arithmetic
Generate summary statistics
Summarize categorical data
Parametric methods
Non-parametric methods
Transform dataset distributions

4. Dimensionality Reduction

Introduction to machine learning
Explanatory factor analysis
Principal component analysis (PCA)

5. Outlier Analysis

Extreme value analysis using univariate methods
Multivariate analysis for outlier detection
A linear projection method for multivariate data

6. Cluster Analysis

K-means method
Hierarchical methods
Instance-based learning with k-Nearest Neighbor

7. Network Analysis with NetworkX

Intro to network analysis
Work with graph objects
Simulate a social network
Generate stats on nodes and inspect graphs

8. Basic Algorithmic Learning

Linear regression model
Logistic regression model
Naïve Bayes classifiers

9. Web-based Data Visualizations with Plotly

Create basic charts
Create statistical charts
Create Plotly choropleth maps
Create Plotly point maps

10. Web Scraping with Beautiful Soup

Introduction to Beautiful Soup
Explore NavigatableString objects
Parse data
Web scrape in practice

Conclusion

Next steps