Predictive Analytics for Business
Learn to apply predictive analytics and business intelligence to solve real-world business problems.
Predictive Analytics for Business Overview
This Nanodegree program prepares you for a career in predictive analytics, and enables you to master a scientific approach to solving problems with data. You’ll build fluency in two leading
software packages: Alteryx, a tool that enables you to prepare, blend, and analyze data quickly; and Tableau, a powerful data visualization tool.
Over the course of the program, you’ll learn to:
• Create mental models to clearly define business issues
• Visualize and prepare data to improve efficacy of predictive models
• Identify and implement a variety of predictive modeling techniques
What is predictive analytics used for?
Predictive analytics are used to determine customer responses or purchases, as well as promote cross-sell opportunities. Predictive models help businesses attract, retain and grow their most profitable customers. Improving operations. Many companies use predictive models to forecast inventory and manage resources.
Predictive Analytics for Business Nanodegree includes:
Real-world projects from industry experts
With real world projects and immersive content built in partnership with top tier companies, you’ll master the tech skills companies want.
Get a knowledgeable mentor who guides your learning and is focused on answering your questions, motivating you and keeping you on track.
Personal career coach and career services
You’ll have access to career coaching sessions, interview prep advice, and resume and online professional profile reviews to help you grow in your career.
Flexible learning program
Get a custom learning plan tailored to fit your busy life. Along with easy monthly payments you can learn at your own pace and reach your personal goals.
Course 1: Problem Solving with Advanced Analytics
In this course, we give you a framework to help you organize and plan your analytical approach. We also introduce
both simple Linear Regression and Multiple Linear Regression.
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 and use it to build a predictive model to select the best location.
LESSON ONE The Problem Solving Framework
• Learn a structured framework for solving problems with advanced analytics
Selecting an Analytical Methodology
• Select the most appropriate analytical methodology based on the context of the business problem
LESSON THREE Linear Regression • Build, validate, and apply linear regression models to solve a business problem
Course 2: Data Wrangling
Data Wrangling is at the core of all data activity. In this course you learn how to work with different data
types,dirty data, and outliers. You will also learn how to reformat data and join data from different sources
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
and use it to build a predictive model to select the best location.
LESSON ONE Understanding Data
• Understand the most common data types
• Understand the various sources of data
LESSON TWO Data Issues
• Identify common types of dirty data
• Make adjustments to dirty data to prepare a dataset
• Identify and adjust for outliers
LESSON THREE Data Formatting • Summarize, cross-tabulate, transpose, and reformat data to
prepare a dataset for analysis
LESSON FOUR Data Blending • Join and union data from different sources and formats
Course 3: Classification Models
Classification models are a powerful tool for business analyst. In this course, you learn more about
binary and non-binary classification models and how to use them to drive business insights.
Predict Loan 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.
LESSON ONE Classification Problems • Understand the fundamentals of classification modeling
and how it differs from modeling numeric data
LESSON TWO Binary Classification
• Build logistic regression and decision tree models
• Use stepwise to automate predictor variables selection
• Score and compare models and interpret the results
LESSON THREE Non-Binary
• Build and compare forest and boosted models and interpret their results
• Score and compare models and interpret the results
Course 4: A/B Testing
Helping businesses make the best decisions is an essential part of Business Analysis. Planning and executing the analysis of an AB test allow you to provide confident recommendations. In this course, you learn how to create, execute, and analyze an AB test.
A/B Test a 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.
LESSON ONE A/B Testing
• Understand the fundamentals of A/B testing, including selecting target and control units and variables and the duration of a test
LESSON TWO Randomized Design
• Select test and control variables and understand the i
importance of sample size
• Design a randomized design A/B test and analyze the
LESSON THREE Matched Pair Design
• Match test units to control units
• Design a matched pair design A/B test and analyze the
LESSON FOUR Matched Pair Practice • Use trend and seasonality as control variables for a matched pair design A/B test
Course 5: Time Series Forecasting
Time Series Forecasting is a powerful analytical tool. In this course, you learn how ETS and ARIMA models are used to forecast data and how they deal with trends and seasonality. These skills will be evaluated in the final
Forecast Video Game Demand
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.
Fundamentals of Time Series Forecasting
• Understand trend, seasonal, and cyclical behavior of time series data
ETS Models • Use time series decomposition plots
• Build out an ETS model in Alteryx
• Stationarize data through differencing, a process that prepares data for ARIMA modeling
• Build out an ARIMA model in Alteryx
Analyzing and Visualizing Results
• Use holdout samples to compare models and select the best one for a business problem
• Visualize your forecasts through various plots
Course 6: Segmentation and Clustering
Segmentation and Clustering are effective methods for finding patterns in your data. In this course, you learn
how to prepare data to be clustered appropriately and interpret results.
Combine Predictive Techniques
A grocery store chain is planning a significant expansion. You will use multiple analytical techniques to provide recommendations on how to expand. After completing the project, you will feel comfortable combining predictive techniques and delivering results to complex business problems.
LESSON ONE Segmentation Fundamentals
• Understand the difference between localization, standardization, and segmentation
LESSON TWO Preparing Data for
• Scale data to prepare a dataset for cluster modeling
• Select variables to include based on the business context
LESSON THREE Variable Reduction • Use principal components analysis (PCA) to reduce the number of variables for cluster model
LESSON FOUR Clustering Models • Select the appropriate number of clusters
• Build and apply a k-centroid cluster model
LESSON FIVE Validating and Applying
• Validate the results of a cluster model
• Visualize and communicate the results of a cluster model
LESSON SIX Creating Visualization With Tableau
• Become proficient in basic Tableau functionality, including
charts, filters, hierarchies, etc.
• Create calculated fields in Tableau
Patrick Nussbaumer, Ben Burkholder, Maureen Wolfson, Rod Light and Tony Moses
Know the instructors
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.
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.
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.
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.
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