Predictive Analytics with Alteryx

Artificial Intelligence (AI) has the potential to transform many business sectors and processes. This course will introduce the fundamental concepts behind AI, including what it is and where it's used. We'll also discuss some of the issues surrounding AI, such as ethical and privacy concerns, and consider the possible future developments of AI, including the possible consequences for jobs and even societies. This course is entirely non-technical and requires no prior knowledge of AI.

Artificial Intelligence (AI) has a variety of applications in business, such as text or image analysis, speech recognition, and behavior or sentiment analysis. In this course, we'll explain some of these applications, outlining how they're used, what algorithms they involve, and what results you might expect. However, we'll stop short of coding or implementing any AI solutions, meaning this course will remain relatively non-technical.

Artificial Intelligence techniques can help you predict the key values that help you make important business decisions, but to do so you need to understand what AI models can do this and how to evaluate them. In this course, you'll learn how to predict numeric values using regression techniques.

Not all business situations boil down to numbers. Sometimes you need to predict an outcome that has only a few possible values. In this course, you'll learn how to predict scenarios with a limited number of outcomes using classification techniques.

Learn how AI techniques can discover hidden patterns and trends that are not immediately obvious, but contain valuable insights. In this course, you'll learn how unsupervised learning techniques are used to find clusters and associations in datasets.

This course focuses on the use of linear and logistic regression analysis applied to business use cases. Learn how to deploy and assess the quality of your models and improve their predictive performance.

Help a courier company improve its estimated delivery times. You'll create a linear regression model that uses a dataset on previous deliveries to make more accurate predictions. The goal is to use this model to improve future deliveries.

This course looks at the Alteryx time series tools. You'll use historic data to make univariate and covariate forecasts, and how to compare forecast outputs. Finally, you'll learn the merits and downsides of customizing the model.

This course looks at classification models in Alteryx. You'll use sample data to train and validate a predictive model. You'll see several techniques, then export the preferred models and fit them to new data.

This course covers two common unsupervised learning techniques that can be deployed in Alteryx. The first is market basket analysis which can be used to understand customer behavior. The other is clustering which is used to find underlying patterns in the data in the form of groups.