Explore Firebase Analytics integration for iOS apps to analyze user behavior and app performance.

Learn how to make predictions about the future using time series forecasting in R including ARIMA models and exponential smoothing methods.

Learn how to identify important drivers of demand, look at seasonal effects, and predict demand for a hierarchy of products from a real world example.

Learn how to translate business questions to well-formed analytical questions and select the right analytical solutions.

Get hands-on experience making sound conclusions based on data in this four-hour course on statistical inference in Python.

Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.

Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.

In this course, you'll learn about the concepts of random variables, distributions, and conditioning.

This course covers many aspects of building deep learning models in PyTorch, including neurons and neural networks, and how PyTorch uses differential calculus to train such models and create dynamic computation graphs in deep learning.

This course will teach you the concepts, theory, and implementation of basic statistics, probability, hypothesis testing, and regression analysis required to build and interpret meaningful machine learning models.

Learn to detect fraud with analytics in R.

Develop a freight emission calculator to analyze and optimize transportation emissions. Learn to use data analysis and machine learning to reduce the carbon footprint of freight logistics, maximizing efficiency and sustainability in supply chains.