Predictive Analytics for Business

Welcome to the Predictive Analytics for Business Nanodegree program! In this lesson, you will learn more about the structure of the program and meet the team.

You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.

What to do if you have questions about your account or general questions about the program.

You will apply a framework to work through the problem and build a linear regression model to provide results and a recommendation.

In this course you'll learn strategies for solving problems, non-predictive data analysis, and more.

Select the most appropriate analytical methodology based on the context of the business problem.

Build, validate, and apply linear regression models to solve a business problem

Get hands on practice building a linear regression model.

You will apply a framework to work through the problem and build a linear regression model to provide results and a recommendation.

Understand the most common data types. Understand the various sources of data.

Identify common types of dirty data. Make adjustments to dirty data to prepare a dataset. Identify and adjust for outliers.

Summarize, cross-tabulate, transpose, and reformat data to prepare a dataset for analysis.

Join and union data from different sources and formats.

Get hands on practice cleaning, blending, and preparing a dataset.

A pet store chain is selecting the location for its next store. You will use data preparation techniques to build a robust analytic dataset, then build a predictive model to select the best location.

Select predictor variables to be used in a predictive model.

A pet store chain is selecting the location for its next store. Build a predictive model to select the best location.

Understand the fundamentals of classification modeling and how it differs from modeling numeric data

Build logistic regression and decision tree models. Use stepwise to automate predictor variables selection. Score and compare models and interpret the results.

Build and compare forest and boosted models and interpret their results. Score and compare models and interpret the results.

A bank recently received an influx of loan applications. You will build and apply a classification model to provide a recommendation on which loan applicants the bank should lend to.