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Predictive Analytics for Business

Nanodegree Program

Learn to clearly define business issues, prepare and clean data, and implement a variety of predictive modeling techniques.

Learn to clearly define business issues, prepare and clean data, and implement a variety of predictive modeling techniques.

Built in collaboration with

Alteryx

This program is no longer available.

We recommend this related program:

Data Preparation with Alteryx

2 weeks

Beginner

  • Intermediate

  • 2 months

  • Last Updated December 17, 2024

Skills you'll learn:

Data storytellingTableau data pane

Prerequisites:

Basic spreadsheet useBasic descriptive statistics

Intermediate

2 months

Last Updated December 17, 2024

Skills you'll learn:

Data storytelling • Tableau data pane • Tableau map-based visualizations • Tableau interactive dashboards

Prerequisites:

Basic spreadsheet use • Basic descriptive statistics

Courses In This Program

Course 1 3 hours

Welcome to the program

Lesson 1

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.

Lesson 2

Getting Help

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

Lesson 3

Get Help with Your Account

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

Lesson 4 • Project

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.

Course 2 1 week

Problem Solving with Analytics

The course begins with an introduction to analytical frameworks, helping learners structure their data analysis approach. It then dives into linear regression, providing hands-on experience in building, interpreting, and refining models to uncover meaningful insights from data. By the end, students will be equipped with practical skills to apply analytical thinking and statistical modeling to real-world scenarios.

Lesson 1

The Analytical Problem

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

Lesson 2

Selecting an Analytical Framework

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

Lesson 3

Linear Regression

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

Lesson 4

Practice Project

Get hands on practice building a linear regression model.

Lesson 5 • Project

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.

Course 3 2 weeks

Data Wrangling

Lesson 1

Understanding Data

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

Lesson 2

Data Issues

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

Lesson 3

Data Formatting

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

Lesson 4

Data Blending

Join and union data from different sources and formats.

Lesson 5

Practice Project (Data Wrangling)

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

Lesson 6 • Project

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.

Lesson 7

Selecting Predictor Variables

Select predictor variables to be used in a predictive model.

Lesson 8

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.

Course 4 1 week

Classification Models

Lesson 1

Classification Problems

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

Lesson 2

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.

Lesson 3

Non-Binary Classification Models

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

Lesson 4 • Project

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.

Course 5 1 week

A/B Testing

Lesson 1

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.

Lesson 2

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.

Lesson 3

Matched Pair Design Tests

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

Lesson 4

Matched Pair Practice

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

Lesson 5 • Project

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.

Course 6 1 week

Time Series Forecasting

Time series forecasting is a powerful analytical tool. In this course, learners will find our how ETS and ARIMA models are used to forecast data and how they deal with trends and seasonality.

Lesson 1

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.

Lesson 2

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.

Lesson 3

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.

Lesson 4

Analyzing and Visualizing Results

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

Lesson 5

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.

Course 7 3 weeks

Segmentation and Clustering

Lesson 1

Segmentation Fundamentals

Understand the difference between localization, standardization, and segmentation

Lesson 2

Preparing Data for Clustering

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

Lesson 3

Variable Reduction

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

Lesson 4

Clustering Models

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

Lesson 5

Validating and Applying Clusters

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

Lesson 6

Data Visualizations in Tableau

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

Lesson 7

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

Lesson 8 • Project

Combining Predictive Techniques

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

(Optional) Course 8 3 weeks

SQL Lessons

Lesson 1

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.

Lesson 2

SQL Joins

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

Lesson 3

SQL Aggregations

In this lesson, you will learn how to aggregate data using SQL functions

Lesson 4

SQL Subqueries & Temporary Tables

In this lesson, you will learn about subqueries, a fundamental advanced SQL topic. This lesson will walk you through the appropriate applications of subqueries, the different types of subqueries, and review subquery syntax and examples.

Lesson 5

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.

Lesson 6

SQL Window Functions

Window functions allow users to compare one row to another without doing any joins using one of the most powerful concepts in SQL data analysis.

Lesson 7

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.

(Optional) Course 9 4 weeks

Data Visualization with Tableau

Learn to apply sound design and data visualization principles to the data analysis process. Learn how to use analysis and visualizations in Tableau to tell a story with data.

Lesson 1

Data Visualization Fundamentals

In this lesson you learn to evaluate the quality of data visualizations and build high quality visualizations, starting with the fundamentals of data dashboards.

Lesson 2

Design Principles

In this lesson you learn to implement the best design practices, and to use the most appropriate chart for a particular situation.

Lesson 3

Creating Visualizations in Tableau

This lesson teaches you how to build data visualizations in Tableau using data hierarchies, filters, groups, sets, and calculated fields, as well as create map-based data visualizations in Tableau.

Lesson 4

Telling Stories with Tableau

In this lesson you learn how to build interactive Tableau dashboards and tell impactful stories using data.

Taught By The Best

Photo of Tony Moses

Tony Moses

Instructor

Tony Moses is a Solutions Engineer at Alteryx, Inc. He works with customers to help develop plans to solve complex business problems around data preparation, geospatial analysis and predictive analytics.

Photo of Rod Light

Rod Light

Instructor

Rod Light is a Solutions Engineer Practice Lead at Alteryx, where he helps customers and prospects design data analytics solutions for their businesses using Alteryx.

Photo of Maureen Wolfson

Maureen Wolfson

Instructor

Maureen Wolfson is a Solution Engineer at Alteryx, Inc. She has more than 20 years of data analysis expertise specializing in data, customer and geospatial analysis.

Photo of Ben Burkholder

Ben Burkholder

Instructor

Ben Burkholder is a senior solution engineer at Alteryx, Inc. In this role he works extensively with clients to help develop plans to solve complex business problems around data preparation, geospatial analysis, and predictive analytics.

Photo of Patrick Nussbaumer

Patrick Nussbaumer

Instructor

Patrick Nussbaumer is Technical Activation Director at Alteryx, Inc. Prior to Alteryx, Patrick has spent the past 20 years in a variety of roles focused on data analysis, telecommunications, and financial services industries.

Student Reviews

Average Rating: 4.7 Stars

291 Reviews

Fawziah a.

December 22, 2022

excellent experience

Areej A.

December 12, 2022

So far so good

Phanindra P.

September 6, 2022

Ab testing and Segmentation need more focus

Abdulaziz A.

July 30, 2022

I'm Enjoyed

Gazwan N.

July 30, 2022

great

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