Evaluate the performance of your algorithm/model.
Modifying the data to better model predictions.
Combine multiple models into one for better performance.
The two most common types of Machine Learning algorithms.
Predict if a donor will donate again.
Predict survival on the Titanic.
Walkthrough through a simple challenge on house prices.
Predict the operating condition of water pumps in Africa.
Rent cloud servers for more timeconsuming calculations (r4.xlarge server is a good place to start).
Contains most of the important Python packages for Data Science.
Online Course using Python that covers most of the relevant toolkits.
Paid course, but 30 free days upon account creation (enough to complete course).
Successfully used in many Kaggle challenges.
Data processing, implementation, and visualization with example code.
Quick overview over the most important functions.
Plot library that works great with Jupyter.
Open source toolkit for working with text-based data.
An introduction to Data Science and its use as a business asset.
Technical Skills for the Data Science: This emphasizes the practical skills needed throughout the data science process.
Differentiation of Big Data, Machine Learning, Data Science.