
2 weeks
Beginner
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
2 weeks
Beginner
Intermediate
2 months
Last Updated December 17, 2024
Skills you'll learn:
Prerequisites:
Intermediate
2 months
Last Updated December 17, 2024
Skills you'll learn:
Prerequisites:
Course 1 • 3 hours
Lesson 1
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
You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.
Lesson 3
What to do if you have questions about your account or general questions about the program.
Lesson 4 • Project
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
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
In this course you'll learn strategies for solving problems, non-predictive data analysis, and more.
Lesson 2
Select the most appropriate analytical methodology based on the context of the business problem.
Lesson 3
Build, validate, and apply linear regression models to solve a business problem
Lesson 4
Get hands on practice building a linear regression model.
Lesson 5 • Project
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
Lesson 1
Understand the most common data types. Understand the various sources of data.
Lesson 2
Identify common types of dirty data. Make adjustments to dirty data to prepare a dataset. Identify and adjust for outliers.
Lesson 3
Summarize, cross-tabulate, transpose, and reformat data to prepare a dataset for analysis.
Lesson 4
Join and union data from different sources and formats.
Lesson 5
Get hands on practice cleaning, blending, and preparing a dataset.
Lesson 6 • Project
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
Select predictor variables to be used in a predictive model.
Lesson 8
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
Lesson 1
Understand the fundamentals of classification modeling and how it differs from modeling numeric data
Lesson 2
Build logistic regression and decision tree models. Use stepwise to automate predictor variables selection. Score and compare models and interpret the results.
Lesson 3
Build and compare forest and boosted models and interpret their results. Score and compare models and interpret the results.
Lesson 4 • Project
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
Lesson 1
Understand the fundamentals of A/B testing, including selecting target and control units and variables and the duration of a test.
Lesson 2
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
Match test units to control units. Design a matched pair design A/B test and analyze the results.
Lesson 4
Use trend and seasonality as control variables for a matched pair design A/B test.
Lesson 5 • Project
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 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
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
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
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
This lesson will demonstrate how to interpret the various results from time series model.
Lesson 5
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
Lesson 1
Understand the difference between localization, standardization, and segmentation
Lesson 2
Scale data to prepare a dataset for cluster modeling. Select variables to include based on the business context.
Lesson 3
Use principal components analysis (PCA) to reduce the number of variables for cluster model
Lesson 4
Select the appropriate number of clusters. Build and apply a k-centroid cluster model.
Lesson 5
Validate the results of a cluster model. Visualize and communicate the results of a cluster model.
Lesson 6
In this lesson, you will learn how to make visualizations in Tableau. Get excited - it is about to get awesome!
Lesson 7
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
After completing the project, you will feel comfortable combining predictive techniques and delivering results
(Optional) Course 8 • 3 weeks
Lesson 1
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
In this lesson, you will learn how to combine data from multiple tables together.
Lesson 3
In this lesson, you will learn how to aggregate data using SQL functions
Lesson 4
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
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
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
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
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
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
In this lesson you learn to implement the best design practices, and to use the most appropriate chart for a particular situation.
Lesson 3
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
In this lesson you learn how to build interactive Tableau dashboards and tell impactful stories using data.
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.
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.
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.
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.
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.
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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