Join Lillian Pierson, P.E. for an in-depth discussion in this video Exercise files, part of Python for Data Science Essential Training.
- [Instructor] You'll have access to all the Jupyter Notebooks and data sets that I'll be using here in this course. Simply follow the link on the main course page to download them. I'm showing them here from my desktop, but you can place them wherever is convenient for you. Inside of the exercise files you'll find a folder of each chapter in the course, and inside of that you'll find individual files that I used. So here's the IPython Notebook for chapter one section one. Let's get started with the course.
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
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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