Analyze time series graphs, use bipartite graphs, and gain the skills to tackle advanced problems in network analytics.

Use RNNs to classify text sentiment, generate sentences, and translate text between languages.

From customer lifetime value, predicting churn to segmentation - learn and implement Machine Learning use cases for Marketing in Python.

Learn efficient techniques in pandas to optimize your Python code.

Learn to load, transform, and transcribe human speech from raw audio files in Python.

Learn about experimental design, and how to explore your data to ask and answer meaningful questions.

The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.

In this course you'll learn techniques for performing statistical inference on numerical data.

Learn the bag of words technique for text mining with R.

Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.

Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.

Learn statistical tests for identifying outliers and how to use sophisticated anomaly scoring algorithms.

Learn to effectively convey your data with an overview of common charts, alternative visualization types, and perception-driven style enhancements.

Learn how to build your very own dashboard by applying all the SQL concepts and functions you have learned in previous courses.

In this course you'll learn the basics of working with time series data.