If you work as a data scientist at a web company, you'll probably be asked to spend some time analyzing the results of A/B tests. These are basically controlled experiments on a website to measure the impact of a given change. So, let's talk about what A/B tests are and how they work.
- Preface
- Getting Started
- Statistics and Probability Refresher, and Python Practice
- Matplotlib and Advanced Probability Concepts
- A crash course in Matplotlib
- Generating multiple plots on one graph
- Saving graphs as images
- Adjusting the axes
- Adding a grid
- Changing line types and colors
- Labeling axes and adding a legend
- A fun example
- Generating pie charts
- Generating bar charts
- Generating scatter plots
- Generating histograms
- Generating box-and-whisker plots
- Try it yourself
- Covariance and correlation
- Conditional probability
- Bayes' theorem
- Summary
- A crash course in Matplotlib
- Predictive Models
- Machine Learning with Python
- Machine learning and train/test
- Using train/test to prevent overfitting of a polynomial regression
- Bayesian methods - Concepts
- Implementing a spam classifier with Naïve Bayes
- K-Means clustering
- Clustering people based on income and age
- Measuring entropy
- Decision trees - Concepts
- Decision trees - Predicting hiring decisions using Python
- Ensemble learning
- Support vector machine overview
- Using SVM to cluster people by using scikit-learn
- Summary
- Recommender Systems
- More Data Mining and Machine Learning Techniques
- Dealing with Real-World Data
- Bias/variance trade-off
- K-fold cross-validation to avoid overfitting
- Data cleaning and normalisation
- Cleaning web log data
- Normalizing numerical data
- Detecting outliers
- Summary
- Apache Spark - Machine Learning on Big Data
- Testing and Experimental Design
A/B testing concepts
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