Predictive Analytics for Business

Orientation

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.

Getting Help

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

Get Help with Your Account

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

Predicting Diamond Prices

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

The Analytical Problem

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

Selecting an Analytical Framework

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

Linear Regression

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

Practice Project

Get hands on practice building a linear regression model.

Predicting Catalog Demand

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

Understanding Data

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

Data Issues

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

Data Formatting

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

Data Blending

Join and union data from different sources and formats.

Practice Project (Data Wrangling)

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

Create an Analytical 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.

Selecting Predictor Variables

Select predictor variables to be used in a predictive model.

Practice Project Select Location of a New Pet Store

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

Classification Problems

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

Binary Classification Models

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

Non-Binary Classification Models

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

Predicting Default Risk

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.

A/B Testing Fundamentals

Understand the fundamentals of A/B testing, including selecting target and control units and variables and the duration of a test.

Randomized Design Tests

Select test and control variables and understand the importance of sample size. Design a randomized design A/B test and analyze the results.

Matched Pair Design Tests

Match test units to control units. Design a matched pair design A/B test and analyze the results.

Matched Pair Practice

Use trend and seasonality as control variables for a matched pair design A/B test.

A/B Test a New Menu Launch

A chain of coffee shops is considering launching a new menu. You will design and analyze an A/B test and write up a recommendation on whether the chain should introduce the new menu.

Fundamentals of Time Series Forecasting

In this lesson you’ll learn what attributes make data a time series. You’ll also learn the key components used in time series forecasting, such as seasonality, trends, and cyclical patterns.

ETS Models

In this lesson you’ll learn how to build and use ETS models. ETS stands for error, trend, and seasonality, and are the three inputs in ETS models.

ARIMA Models

In this lesson you’ll learn how to build and use ARIMA models. ARIMA stands for autoregressive, integrated, moving average, which are the inputs for ARIMA models.

Analyzing and Visualizing Results

This lesson will demonstrate how to interpret the various results from time series model.

Practice Project Forecast Video Game Sales

A video game producer is planning production levels. You will use time series forecasting models to forecast monthly demand and provide a recommendation to help match supply to demand.

Segmentation Fundamentals

Understand the difference between localization, standardization, and segmentation

Preparing Data for Clustering

Scale data to prepare a dataset for cluster modeling. Select variables to include based on the business context.

Variable Reduction

Use principal components analysis (PCA) to reduce the number of variables for cluster model

Clustering Models

Select the appropriate number of clusters. Build and apply a k-centroid cluster model.

Validating and Applying Clusters

Validate the results of a cluster model. Visualize and communicate the results of a cluster model.

Data Visualizations in Tableau

In this lesson, you will learn how to make visualizations in Tableau. Get excited - it is about to get awesome!

Segmentation Practice Project

A retail store chain wants to expand to other countries. You will build a clustering model to segment countries to determine which countries are most similar to the U.S. in order to determine the best

Combining Predictive Techniques

After completing the project, you will feel comfortable combining predictive techniques and delivering results

Basic SQL

In this section, you will gain knowledge about SQL basics for working with a single table. You will learn the key commands to filter a table in many different ways.

SQL Joins

In this lesson, you will learn how to combine data from multiple tables together.

SQL Aggregations

In this lesson, you will learn how to aggregate data using SQL functions like SUM, AVG, and COUNT. Additionally, CASE, HAVING, and DATE functions provide you an incredible problem solving toolkit.

SQL Subqueries & Temporary Tables

In this lesson, you will be learning to answer much more complex business questions using nested querying methods - also known as subqueries.

SQL Data Cleaning

Cleaning data is an important part of the data analysis process. You will be learning how to perform data cleaning using SQL in this lesson.

[Advanced] SQL Window Functions

Compare one row to another without doing any joins using one of the most powerful concepts in SQL data analysis: window functions.

[Advanced] SQL Advanced JOINs & Performance Tuning

Learn advanced joins and how to make queries that run quickly across giant datasets. Most of the examples in the lesson involve edge cases, some of which come up in interviews.

Intro to Data Visualization

In this lesson, you will get a glimpse at different data visualizations and when they are used.

Design

In this lesson, you will learn about visual encodings, and best practices for building data visualizations.

Data Visualizations in Tableau

In this lesson, you will learn how to make visualizations in Tableau. Get excited - it is about to get awesome!

Making Dashboards & Stories in Tableau

In this lesson, you will learn from a Tableau expert, and start putting together your own dashboards and stories.

Take 30 Min to Improve your LinkedIn

Find your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.