AuthorLillian Pierson, P.E.
- Getting started with Jupyter Notebooks
- Visualizing data: basic charts, time series, and statistical plots
- Preparing for analysis: treating missing values and data transformation
- Data analysis basics: arithmetic, summary statistics, and correlation analysis
- Outlier analysis: univariate, multivariate, and linear projection methods
- Introduction to machine learning
- Basic machine learning methods: linear and logistic regression, Naïve Bayes
- Reducing dataset dimensionality with PCA
- Clustering and classification: k-means, hierarchical, and k-NN
- Simulating a social network with NetworkX
- Creating Plot.ly charts
- Scraping the web with Beautiful Soup
Skill Level Beginner
- [Instructor] Jupyter came with your Anaconda install, so let me show you how to run Jupyter. You just go to the application and launch it and then your console will open up. Right now, these are a bunch of files that I've been working with for the course, but let's look at some notebooks that come with the course. You're going to get all of these notebooks in the Exercises Files. Let's look at the first one. And I just wanted to point out that these are the cells where you code, and when you're coding you just want to make sure that they're set at code so that Jupyter knows to treat them that way.
When you want to run the coding cell, you can either point and click this button here, or you could hit shift enter or control enter. That's the very basics about Jupyter and Notebook that you need for this course.
1. Data Munging Basics
2. Data Visualization Basics
3. Basic Math and Statistics
4. Dimensionality Reduction
Explanatory factor analysis6m 39s
5. Outlier Analysis
6. Cluster Analysis
7. Network Analysis with NetworkX
8. Basic Algorithmic Learning
9. Web-based Data Visualizations with Plotly
10. Web Scraping with Beautiful Soup
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