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- Learn how to analyze and visualize network data in the R programming language using the tidyverse approach.
- Learn how to analyze business processes in R and extract actionable insights from enormous sets of event data.
- Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.
- Learn how bonds work and how to price them and assess some of their risks using the numpy and numpy-financial packages.
- Learn strategies for answering probability questions in R by solving a variety of probability puzzles.
- Learn to use the Census API to work with demographic and socioeconomic data.
- Learn how to use TensorFlow, a state-of-the-art machine learning framework that specializes in the ability to develop deep learning neural networks.
- Learn to develop a set of principles for your data science and software development projects.
- Learn to visualize multivariate datasets using lattice graphics.
- Learn how to apply advanced dimensionality techniques such as t-SNE and GLRM.
- Publicly release data sets with a differential privacy guarantee.
- Prepare for your upcoming machine learning interview by working through these practice questions that span across important topics in machine learning.
- Learn how to make sense of spatial data and deal with various classes of statistical problems associated with it.
