- Predictive Analytics for Business Nanodegree
- Content
- Part 01 : Welcome to the program
- Part 02 : Problem Solving with Analytics
- Part 03 : Data Wrangling
- Part 04 : Classification Models
- Part 05 : Career Support
- Part 06 : A/B Testing
- Part 07 : Time Series Forecasting
- Part 08 : Segmentation and Clustering
- Part 09 (Elective): SQL Lessons
- Part 10 (Elective): Tableau Lessons
- Content
Predictive Analytics for Business Nanodegree
助教微信
udacity公众号
Nanodegree key: nd008t
Version: 3.0.0
Locale: en-us
Learn to clearly define business issues, prepare and clean data, and implement a variety of predictive modeling techniques.
Content
Part 01 : Welcome to the program
Module 01: Welcome to the ND
- Lesson 01: OrientationWelcome 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.
- Concept 01: Welcome
- Concept 02: Syllabus
- Concept 03: Frequently Asked Questions
- Concept 04: Meet Miriam
- Concept 05: Projects and Progress
- Concept 06: How Does Project Submission Work?
- Concept 07: Integrity and Mindset
- Concept 08: How Do I Find Time for My Nanodegree?
- Concept 09: Access the Career Portal
- Concept 10: Final Tips
- Concept 11: Software Licenses
- Concept 12: Downloading Alteryx
- Concept 13: Text: Installing Tableau
- Lesson 02: Knowledge, Community, and CareersYou are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.
- Lesson 03: Get Help with Your AccountWhat to do if you have questions about your account or general questions about the program.
- Lesson 04: Predicting Diamond PricesYou will apply a framework to work through the problem and build a linear regression model to provide results and a recommendation.Project Description - Predicting Diamond PricesProject Rubric - Predicting Diamond Prices
- Lesson 01: OrientationWelcome 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.
Module 01: Problem Solving with Analytics
- Lesson 01: The Analytical ProblemIn this course you’ll learn strategies for solving problems, non-predictive data analysis, and more.
- Concept 01: Program Hosts - Course Overview
- Concept 02: Course Introduction
- Concept 03: The Problem Solving Framework
- Concept 04: Business Issue Understanding
- Concept 05: Data Understanding
- Concept 06: Data Preparation
- Concept 07: Analysis and Modeling
- Concept 08: Validation
- Concept 09: Presentation and Visualization
- Lesson 02: Selecting an Analytical FrameworkSelect the most appropriate analytical methodology based on the context of the business problem.
- Concept 01: Selecting an Analytical Methodology
- Concept 02: Non-Predictive Business Problems
- Concept 03: Classifying Business Problems
- Concept 04: Predictive Business Problems
- Concept 05: Data Poor Business Problems
- Concept 06: Data Rich Business Problems
- Concept 07: Numeric & Non-Numeric Outcomes
- Concept 08: Numeric or Classification Quiz
- Concept 09: Introduction to Numeric Models
- Concept 10: Introduction to Non-Numeric Models
- Concept 11: Determining Appropriate Models Quiz
- Concept 12: Model Selection Assessment
- Concept 13: Lesson Summary
- Lesson 03: Linear RegressionBuild, validate, and apply linear regression models to solve a business problem
- Concept 01: The Business Problem
- Concept 02: Approaching the Business Problem
- Concept 03: Data Understanding Quiz
- Concept 04: Data Understanding Solution
- Concept 05: The Problem Solving Framework
- Concept 06: Introduction to Linear Regression
- Concept 07: Linear Equations in Google Sheets
- Concept 08: Linear Regression Validation
- Concept 09: Simple Linear Regression Quiz
- Concept 10: Simple Linear Regression Solution
- Concept 11: Introduction to Multiple Linear Regression
- Concept 12: Multiple Linear Regression Concepts
- Concept 13: Multiple Linear Regression with Excel
- Concept 14: Multiple Linear Regression Validation
- Concept 15: Linear Regression with Categorical Variables
- Concept 16: Dummy Variable Quiz
- Concept 17: Introduction to Alteryx
- Concept 18: Downloading Alteryx
- Concept 19: Alteryx Walkthrough
- Concept 20: Alteryx Tutorials
- Concept 21: Building your First Model in Alteryx
- Concept 22: Running the Model
- Concept 23: Interpreting Linear Regression Results
- Concept 24: Evaluating an Equation
- Concept 25: Evaluating an Equation Solution
- Concept 26: Analysis Summary
- Concept 27: Course Recap
- Concept 28: Learning Summary
- Lesson 04: Practice ProjectGet hands on practice building a linear regression model.
- Lesson 05: Predicting Catalog DemandYou will apply a framework to work through the problem and build a linear regression model to provide results and a recommendation.Project Description - Predicting Catalog DemandProject Rubric - Predicting Catalog Demand
- Lesson 01: The Analytical ProblemIn this course you’ll learn strategies for solving problems, non-predictive data analysis, and more.
Module 01: Data Preparation
- Lesson 01: Understanding DataUnderstand the most common data types. Understand the various sources of data.
- Concept 01: Program Hosts - Course Overview
- Concept 02: Course Intro
- Concept 03: Lesson Introduction
- Concept 04: Before You Get Started
- Concept 05: Structure of Data
- Concept 06: Three Types of Data Structure
- Concept 07: Classify Data
- Concept 08: Data Sources - Files
- Concept 09: Course Outline
- Concept 10: Data Sources - File Example
- Concept 11: Data Sources - File Example Continued
- Concept 12: Alteryx Exercise
- Concept 13: Alteryx Exercise - Solution
- Concept 14: Data Sources - Databases
- Concept 15: Data Sources - Web-based Sources
- Concept 16: Data Sources - Web-scraping Exercise
- Concept 17: Data Sources - Web-scraping Solution
- Concept 18: Introduction to Data Types
- Concept 19: Data Types
- Concept 20: Identify Data Types Exercise
- Concept 21: Data Types in Alteryx
- Concept 22: Data Types Exercise in Alteryx
- Concept 23: Data Types Exercise in Alteryx Solution
- Concept 24: Wrap Up
- Lesson 02: Data IssuesIdentify common types of dirty data. Make adjustments to dirty data to prepare a dataset. Identify and adjust for outliers.
- Concept 01: Lesson Introduction
- Concept 02: Interview - Importance of Data Cleaning
- Concept 03: Dirty Data
- Concept 04: Examples of Dirty Data
- Concept 05: Dirty Data - Parsing
- Concept 06: Dirty Data - Parsing Example in Alteryx
- Concept 07: Parsing a Phone Number
- Concept 08: Parsing a Phone Number Solution
- Concept 09: Dirty Data - Extra Characters
- Concept 10: Dirty Data - Extra Characters Example
- Concept 11: Dirty Data - Extra Characters Exercise
- Concept 12: Dirty Data - Extra Characters Solution
- Concept 13: Dirty Data - Duplicate Data
- Concept 14: Dirty Data - Duplicate Data Example
- Concept 15: Deduping - Exercise
- Concept 16: Deduping - Solution
- Concept 17: Missing Data
- Concept 18: What Does Missing Data Look Like?
- Concept 19: Why Do We Care About Missing Data?
- Concept 20: Dealing with Missing Data - Deletion Exercise
- Concept 21: Dealing with Missing Data - Deletion Solution
- Concept 22: Effect of Deletion on Model
- Concept 23: Dealing with Missing Data - Deletion Exercise 2
- Concept 24: Dealing with Missing Data - Deletion Solution 2
- Concept 25: Imputation
- Concept 26: Dealing with Missing Data - Imputation
- Concept 27: Dealing with Missing Data - Imputation Exercise
- Concept 28: Dealing with Missing Data - Imputation Solution
- Concept 29: Advanced Methods for Dealing with Missing Data
- Concept 30: Missing Data Factors to Consider
- Concept 31: Introduction to Outliers
- Concept 32: Interview - Importance of Catching Outliers
- Concept 33: What is an Outlier?
- Concept 34: Why Do We Care About Outliers?
- Concept 35: Effect of Outliers on Our Model
- Concept 36: Effect of Outliers - Exercise
- Concept 37: Effect of Outliers - Solution
- Concept 38: Identifying Outliers
- Concept 39: Identifying Outliers Exercise
- Concept 40: Dealing with Outliers
- Concept 41: Outliers Quiz 1
- Concept 42: Outliers Quiz 2
- Concept 43: Outliers Quiz 3
- Concept 44: Wrap Up
- Lesson 03: Data FormattingSummarize, cross-tabulate, transpose, and reformat data to prepare a dataset for analysis.
- Concept 01: Lesson Introduction
- Concept 02: Transposing Data
- Concept 03: Transposing in Alteryx
- Concept 04: Transposing - Exercise
- Concept 05: Transposing - Solution
- Concept 06: Aggregating Data
- Concept 07: Aggregating Data - Example
- Concept 08: Aggregating Data - Exercise
- Concept 09: Aggregating Data - Solution
- Concept 10: Cross Tabulation
- Concept 11: Cross Tabulation - Example
- Concept 12: Cross Tabulation - Exercise
- Concept 13: Cross Tabulation - Solution
- Concept 14: Wrap Up
- Lesson 04: Data BlendingJoin and union data from different sources and formats.
- Concept 01: Lesson Introduction
- Concept 02: Unioning Datasets
- Concept 03: Union - Example
- Concept 04: Union - Exercise
- Concept 05: Union - Solution
- Concept 06: Joining Datasets
- Concept 07: Joining Datasets - Example
- Concept 08: Joining Datasets - Exercise
- Concept 09: Joining Datasets - Solution
- Concept 10: Fuzzy Matching
- Concept 11: Fuzzy Matching Continued
- Concept 12: Fuzzy Matching - Example
- Concept 13: Fuzzy Matching - Exercise
- Concept 14: Fuzzy Matching - Solution
- Concept 15: Spatial Matching
- Concept 16: Spatial Blending
- Concept 17: Spatial Blending - Example
- Concept 18: Spatial Blending - Example Continued
- Concept 19: Spatial Blending - Exercise
- Concept 20: Spatial Blending - Solution
- Concept 21: Wrap Up
- Concept 22: Closing Remarks
- Concept 23: Learning Summary
- Lesson 05: Practice ProjectGet hands on practice cleaning, blending, and preparing a dataset.
- Lesson 06: Create an Analytical DatasetA 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.Project Description - Create an Analytical DatasetProject Rubric - Create an Analytical Dataset
- Lesson 01: Understanding DataUnderstand the most common data types. Understand the various sources of data.
Module 02: Selecting Predictor Variables
- Lesson 01: Selecting Predictor VariablesSelect predictor variables to be used in a predictive model.
- Concept 01: Overview
- Concept 02: Choosing Predictor Variables
- Concept 03: Selecting Predictor Variables - Quiz
- Concept 04: Selecting Predictor Variables - Solution
- Concept 05: Non-Duplicate Predictor Variables
- Concept 06: Predictor Variables - Correlation
- Concept 07: Predictor Variables - Correlation Continued
- Concept 08: Correlation Plots
- Concept 09: Correlation Plots in Alteryx
- Concept 10: Predictor Variables - Correlation Quiz
- Concept 11: Preparing to Model
- Concept 12: Preparing to Model in Alteryx
- Concept 13: Data Preparation Solution - Counting Null Values
- Concept 14: Data Preparation - Quiz
- Concept 15: Data Preparation Solution - Visualizing Data
- Concept 16: Data Preparation Solution - Dealing with Null Values
- Concept 17: Preparing to Model Categorical Variables
- Concept 18: Preparing to Model Categorical Variables in Alteryx
- Concept 19: Wrap Up
- Lesson 02: Practice Project Select Location of a New Pet StoreA pet store chain is selecting the location for its next store. Build a predictive model to select the best location.
- Lesson 01: Selecting Predictor VariablesSelect predictor variables to be used in a predictive model.
Module 01: Lessons
- Lesson 01: Classification ProblemsUnderstand the fundamentals of classification modeling and how it differs from modeling numeric data
- Lesson 02: Binary Classification ModelsBuild logistic regression and decision tree models. Use stepwise to automate predictor variables selection. Score and compare models and interpret the results.
- Concept 01: Binary Classification Problems
- Concept 02: Logistic Regression
- Concept 03: Logistic Regression - Continued
- Concept 04: Logistic Regression - Example
- Concept 05: Logistic Regression - Quiz
- Concept 06: Logistic Regression - Solution
- Concept 07: Logistic Regression - Stepwise
- Concept 08: Logistic Regression - Stepwise in Alteryx
- Concept 09: Logistic Regression - Stepwise Quiz
- Concept 10: Logistic Regression - Stepwise Solution
- Concept 11: Validating Models
- Concept 12: Logistic Regression - Stepwise Validation
- Concept 13: Introduction to Decision Tree Modeling
- Concept 14: Decision Tree - Example
- Concept 15: Decision Tree - Models in Alteryx
- Concept 16: Decision Tree - Results
- Concept 17: Decision Tree - Quiz
- Concept 18: Decision Tree Solution
- Concept 19: Decision Tree - Validation
- Concept 20: Introduction to Model Comparison
- Concept 21: Model Comparison - Example
- Concept 22: Scoring the Model
- Concept 23: Scoring the Model - Quiz 1
- Concept 24: Scoring the Model - Example
- Concept 25: Scoring the Model - Quiz 2
- Concept 26: Wrap Up
- Concept 27: Learning Summary
- Lesson 03: Non-Binary Classification ModelsBuild and compare forest and boosted models and interpret their results. Score and compare models and interpret the results.
- Concept 01: Non-Binary Classification Problems
- Concept 02: Decision Tree
- Concept 03: Decision Tree - Quiz
- Concept 04: Decision Tree - Solution
- Concept 05: Decision Tree - Validation
- Concept 06: Forest Model
- Concept 07: Forest Model Example
- Concept 08: Build a Forest Model
- Concept 09: Forest Model Results
- Concept 10: Build a Forest Model Continued
- Concept 11: Forest Model - Quiz
- Concept 12: Forest Model Validation - Quiz (Hidden)
- Concept 13: Forest Model Validation
- Concept 14: Forest Model Outro
- Concept 15: Learning Summary
- Concept 16: Boosted Model
- Concept 17: Boosted Model - Build Model
- Concept 18: Boosted Model - Results
- Concept 19: Boosted Model - Observe Results
- Concept 20: Boosted Model - Validation
- Concept 21: Boosted Model Outro
- Concept 22: Model Comparison
- Concept 23: Score the Missing Data - Quiz
- Concept 24: Score the Missing Data - Solution
- Concept 25: Wrap Up
- Concept 26: Learning Summary
- Lesson 04: Predicting Default RiskA 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.Project Description - Predicting Default RiskProject Rubric - Predicting Default Risk
- Concept 01: Project Overview
- Concept 02: Project Details
- Concept 03: Supporting Materials
- Concept 04: Tips
- Concept 05: Verify Answers for Project
- Concept 06: Model Comparison Tool Errors
Part 05 : Career Support
LinkedIn is widely used by tech recruiters to find and evaluate job candidates. It’s also where you can meet interesting people in your field. Get personalized feedback on how to improve your profile! (You may elect to opt out of this project.)
Module 01: Career Support
- Lesson 01: Take 30 Min to Improve your LinkedInFind your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.Project Description - Improve Your LinkedIn ProfileProject Rubric - Improve Your LinkedIn Profile
- Concept 01: Get Opportunities with LinkedIn
- Concept 02: Use Your Story to Stand Out
- Concept 03: Why Use an Elevator Pitch
- Concept 04: Create Your Elevator Pitch
- Concept 05: Use Your Elevator Pitch on LinkedIn
- Concept 06: Create Your Profile With SEO In Mind
- Concept 07: Profile Essentials
- Concept 08: Work Experiences & Accomplishments
- Concept 09: Build and Strengthen Your Network
- Concept 10: Reaching Out on LinkedIn
- Concept 11: Boost Your Visibility
- Concept 12: Up Next
Part 06 : A/B Testing
- Lesson 01: Take 30 Min to Improve your LinkedInFind your next job or connect with industry peers on LinkedIn. Ensure your profile attracts relevant leads that will grow your professional network.Project Description - Improve Your LinkedIn ProfileProject Rubric - Improve Your LinkedIn Profile
Module 01: A/B Testing
- Lesson 01: A/B Testing FundamentalsUnderstand the fundamentals of A/B testing, including selecting target and control units and variables and the duration of a test.
- Concept 01: Overview
- Concept 02: Welcome to AB Testing
- Concept 03: Units
- Concept 04: Units Quiz
- Concept 05: Treatment and Control Groups
- Concept 06: Experimental and Control Variables
- Concept 07: Variables
- Concept 08: Control Variables
- Concept 09: Testing Correlation
- Concept 10: Lurking Variables
- Concept 11: Experimental Design
- Concept 12: Experimental Design Quiz
- Concept 13: Experiment Duration
- Concept 14: Experiment Duration Quiz
- Concept 15: Conclusion
- Lesson 02: Randomized Design TestsSelect test and control variables and understand the importance of sample size. Design a randomized design A/B test and analyze the results.
- Concept 01: Intro to Randomized Design
- Concept 02: Selecting Variables in an Experiment
- Concept 03: Control Variables Quiz
- Concept 04: Control Variables Solution
- Concept 05: Experiment Design and Setup
- Concept 06: Identify the Control Variables
- Concept 07: Sample Size
- Concept 08: Preparing the Data for Analysis
- Concept 09: Analyzing the Results
- Concept 10: Analyzing the Results Example
- Concept 11: Performing a T-test Quiz
- Concept 12: Performing a T-test Solution
- Concept 13: Analyzing Results in Alteryx
- Concept 14: Analyzing Results Quiz
- Concept 15: Conclusion
- Lesson 03: Matched Pair Design TestsMatch test units to control units. Design a matched pair design A/B test and analyze the results.
- Concept 01: Introduction to Matched Pair Design
- Concept 02: Selecting Treatment Units
- Concept 03: Selecting Control Units
- Concept 04: Selecting Control Units Quiz
- Concept 05: Selecting Control Units Solution
- Concept 06: Selecting One Control Unit for a Treatment Unit
- Concept 07: Selecting Multiple Control Units for a Treatment Unit
- Concept 08: Matching Stores Example
- Concept 09: Matched Pairing Quiz
- Concept 10: Matched Pairing Solution
- Concept 11: Analyzing the Results Overview
- Concept 12: Paired T-test Quiz
- Concept 13: Analyzing the Results with Alteryx
- Concept 14: Interpreting Results
- Concept 15: Analyzing Matched Pair Design Quiz
- Concept 16: Analyzing Matched Pair Design Solution
- Concept 17: Conclusion
- Concept 18: Learning Summary
- Lesson 04: Matched Pair PracticeUse trend and seasonality as control variables for a matched pair design A/B test.
- Concept 01: Introduction
- Concept 02: Pricing Elasticity Analysis Problem
- Concept 03: Select Treatment Units
- Concept 04: Select Discrete Control Variables
- Concept 05: Select Continuous Control Variables Quiz
- Concept 06: Select Continuous Control Variables
- Concept 07: Select Continuous Control Variables
- Concept 08: Prepare for Test Quiz
- Concept 09: Run Test
- Concept 10: Filter & Calculate Date Fields
- Concept 11: Weekly Store Traffic Data
- Concept 12: Create Discrete Data Table
- Concept 13: Store List Data
- Concept 14: Sales Data Quiz
- Concept 15: Sales Data Solution
- Concept 16: Preparing Control and Treatment Units
- Concept 17: Performing the Analysis
- Concept 18: Performing the Analysis Quiz
- Concept 19: Performing the Analysis Solution
- Concept 20: Conclusion
- Lesson 05: A/B Test a New Menu LaunchA 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.Project Description - A/B Test a New Menu LaunchProject Rubric - A/B Test a New Menu Launch
- Concept 01: Project Overview
- Concept 02: Project Details
- Concept 03: Supporting Materials
- Concept 04: Tips
- Concept 05: Verify Project Answer
Part 07 : Time Series Forecasting
- Lesson 01: A/B Testing FundamentalsUnderstand the fundamentals of A/B testing, including selecting target and control units and variables and the duration of a test.
Module 01: Time Series Forecasting
- Lesson 01: Fundamentals of Time Series ForecastingIn 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.
- Concept 01: Welcome to Time Series Forecasting
- Concept 02: Introduction to Time Series
- Concept 03: The Business Problem
- Concept 04: Time Series Fundamentals Quiz
- Concept 05: Simple Forecasting Methods
- Concept 06: Time Series Components
- Concept 07: Trend
- Concept 08: Trends Quiz
- Concept 09: Seasonality
- Concept 10: Seasonality Plot
- Concept 11: Cyclical Patterns
- Concept 12: Seasonal or Cyclical Quiz
- Concept 13: Seasonal or Cyclical Solution
- Concept 14: Outro
- Lesson 02: ETS ModelsIn 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.
- Concept 01: Introduction to ETS Models
- Concept 02: Time Series Decomposition
- Concept 03: Identifying Additive or Multiplicative Terms
- Concept 04: Time Series Scenarios
- Concept 05: Simple Exponential Smoothing
- Concept 06: Simple Exponential Smoothing Quiz
- Concept 07: Next Few Methods
- Concept 08: Holt’s Linear Trend Method
- Concept 09: Exponential Trend Method
- Concept 10: Damped Trend Methods
- Concept 11: Holt-Winters Seasonal Method
- Concept 12: Overview So Far
- Concept 13: Constructing an ETS Model
- Concept 14: Constructing an ETS Model Quiz
- Concept 15: Constructing an ETS Model Solution
- Concept 16: Learning Summary
- Lesson 03: ARIMA ModelsIn 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.
- Concept 01: Introduction to ARIMA Models
- Concept 02: ARIMA Models
- Concept 03: Stationarity
- Concept 04: Stationary vs Non-Stationary Quiz
- Concept 05: Differencing
- Concept 06: Differencing Quiz
- Concept 07: Differencing Solution
- Concept 08: Autocorrelation Function Plot
- Concept 09: Partial Autocorrelation Function Plot
- Concept 10: Autoregressive Component
- Concept 11: Moving Average Component
- Concept 12: ACF and PACF Plots Quiz
- Concept 13: Integrated Component
- Concept 14: Seasonal ARIMA Models
- Concept 15: Seasonal Differencing
- Concept 16: Seasonal Differencing Quiz
- Concept 17: Seasonal AR and MA Terms
- Concept 18: Constructing an ARIMA Model
- Concept 19: Constructing an ARIMA Model Quiz
- Concept 20: Constructing an ARIMA Model Solution
- Concept 21: Learning Summary
- Lesson 04: Analyzing and Visualizing ResultsThis lesson will demonstrate how to interpret the various results from time series model.
- Concept 01: Analyzing and Visualizing Forecasting Results
- Concept 02: Holdout Sample
- Concept 03: Residual Plots
- Concept 04: Visualizing Results
- Concept 05: Calculating Error
- Concept 06: Interpreting Measures of Error
- Concept 07: Interpreting Error
- Concept 08: Akaike Information Criterion (AIC)
- Concept 09: Choosing the Best Model
- Concept 10: Confidence Intervals
- Concept 11: Outro
- Lesson 05: Practice Project Forecast Video Game SalesA 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.
- Concept 01: Project Overview
- Concept 02: Project Details
- Concept 03: Supporting Materials
- Concept 04: Verify a Project Answer
- Concept 05: Project Solution
Part 08 : Segmentation and Clustering
- Lesson 01: Fundamentals of Time Series ForecastingIn 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.
Module 01: Lessons
- Lesson 01: Segmentation FundamentalsUnderstand the difference between localization, standardization, and segmentation
- Concept 01: Welcome to the Course
- Concept 02: Standardization vs. Localization
- Concept 03: Grouping Exercise
- Concept 04: Grouping Exercise 2
- Concept 05: Defining Segmentation and Clustering
- Concept 06: Distance
- Concept 07: Distance Quiz
- Concept 08: Examples for Uses of Clustering
- Concept 09: Unsupervised Learning
- Concept 10: Business Problem Introduction
- Lesson 02: Preparing Data for ClusteringScale data to prepare a dataset for cluster modeling. Select variables to include based on the business context.
- Concept 01: Data Preparation Introduction
- Concept 02: Getting the Right Data
- Concept 03: Selecting Data Based on Objectives
- Concept 04: Examples of Selecting Data Based on Objectives
- Concept 05: Predetermined Bias in Transactional Data
- Concept 06: Selecting Data Quiz
- Concept 07: Data Types in Clustering
- Concept 08: Data Quality
- Concept 09: Scaling
- Concept 10: Scaling Quiz
- Concept 11: Data Prep Exercise
- Concept 12: Transforming Variables
- Concept 13: Visualizing the Data
- Concept 14: Lesson Summary
- Lesson 03: Variable ReductionUse principal components analysis (PCA) to reduce the number of variables for cluster model
- Concept 01: Lesson Introduction
- Concept 02: Variable Reduction
- Concept 03: Variable Reduction Example
- Concept 04: Factor Analysis and PCA Overview
- Concept 05: PCA Details
- Concept 06: PCA Practice
- Concept 07: PCA Practice Continued
- Concept 08: PCA
- Concept 09: PCA Results
- Concept 10: Visualizing PCA Results
- Concept 11: Visualizing PCA Exercise
- Concept 12: PCA Results 2
- Concept 13: Evaluating PCA Results
- Concept 14: Finishing Off the PCA Data
- Concept 15: Lesson Summary
- Lesson 04: Clustering ModelsSelect the appropriate number of clusters. Build and apply a k-centroid cluster model.
- Concept 01: Clustering Techniques Introduction
- Concept 02: Hierarchical Clustering
- Concept 03: K-Centroid Clustering
- Concept 04: Comparison of the Two Methods
- Concept 05: How Many Clusters?
- Concept 06: Subjectivity in Selecting Number of Clusters
- Concept 07: How Many Clusters - Hierarchy
- Concept 08: How Many Clusters - K-Centroid
- Concept 09: Cluster Validation in Alteryx
- Concept 10: Cluster Validation
- Concept 11: Selecting the Number of Clusters Quiz
- Concept 12: Creating Clusters using K-Centroid
- Concept 13: Creating the Cluster Model
- Concept 14: Interpreting Cluster Results
- Concept 15: Applying the Model
- Concept 16: Wrap Up
- Lesson 05: Validating and Applying ClustersValidate the results of a cluster model. Visualize and communicate the results of a cluster model.
- Concept 01: Lesson Introduction
- Concept 02: The Iterative Nature of Clustering
- Concept 03: External Validation
- Concept 04: Validating Through Visualization
- Concept 05: Validating Through Visualization 2
- Concept 06: Validating Through Visualization 3
- Concept 07: Communicating the “Story” and Ongoing Testing
- Concept 08: Conclusion
- Concept 09: Learning Summary
- Lesson 06: Data Visualizations in TableauIn this lesson, you will learn how to make visualizations in Tableau. Get excited - it is about to get awesome!
- Concept 01: New to Data Viz?
- Concept 02: Video: What is Tableau?
- Concept 03: Text: Installing Tableau
- Concept 04: Video: How This Lesson Is Structured?
- Concept 05: Text: Outline of Topics Covered
- Concept 06: Commas vs Periods
- Concept 07: Video: Connecting to Data
- Concept 08: Text: Connecting to Data Recap
- Concept 09: Quiz: Connecting to Data
- Concept 10: Video: Combining Data
- Concept 11: Text: Combining Data Recap
- Concept 12: Quiz: Combining Data
- Concept 13: Video: What Can You Create In Tableau?
- Concept 14: Video: Worksheets
- Concept 15: Text: Worksheets
- Concept 16: Quiz: Worksheets
- Concept 17: Text: Saving to Tableau Public
- Concept 18: Video: Aggregations
- Concept 19: Text: Aggregations
- Concept 20: Quiz: Aggregations
- Concept 21: Video: Hierarchies
- Concept 22: Text: Hierarchies
- Concept 23: Quiz: Hierarchies
- Concept 24: Video: Marks & Filters
- Concept 25: Text: Marks & Filters I
- Concept 26: Quiz: Marks & Filters I
- Concept 27: Text: Marks & Filters II
- Concept 28: Quiz: Marks & Filters II
- Concept 29: Video: Show Me
- Concept 30: Text: Show Me
- Concept 31: Quiz: Show Me
- Concept 32: Video: Small Multiples & Dual Axis
- Concept 33: Text: Small Multiples & Dual Axis
- Concept 34: Text: Map Configuration
- Concept 35: Quiz: Small Multiples
- Concept 36: Quiz: Dual Axis
- Concept 37: Video: Groups & Sets
- Concept 38: Text: Groups & Sets
- Concept 39: Quiz: Groups
- Concept 40: Quiz: Sets
- Concept 41: Video: Calculated Fields
- Concept 42: Text: Calculated Fields
- Concept 43: Quiz: Calculated Fields
- Concept 44: Video: Table Calculations
- Concept 45: Text: Table Calculations
- Concept 46: Quiz: Table Calculations
- Concept 47: Text: Recap
- Concept 48: Video: What’s Next?
- Lesson 07: Segmentation Practice ProjectA 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 01: Segmentation FundamentalsUnderstand the difference between localization, standardization, and segmentation
Module 02: Capstone
- Lesson 01: Combining Predictive TechniquesAfter completing the project, you will feel comfortable combining predictive techniques and delivering results Project Description - Combining Predictive TechniquesProject Rubric - Combining Predictive Techniques
Module 01: SQL
- Lesson 01: Basic SQLIn 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.
- Concept 01: Video: SQL Introduction
- Concept 02: Video: The Parch & Posey Database
- Concept 03: Video + Text: The Parch & Posey Database
- Concept 04: Quiz: ERD Fundamentals
- Concept 05: Text: Map of SQL Content
- Concept 06: Video: Why SQL
- Concept 07: Video: How Databases Store Data
- Concept 08: Text + Quiz: Types of Databases
- Concept 09: Video: Types of Statements
- Concept 10: Statements
- Concept 11: Video: SELECT & FROM
- Concept 12: Your First Queries in SQL Workspace
- Concept 13: Solution: Your First Queries
- Concept 14: Formatting Best Practices
- Concept 15: Video: LIMIT
- Concept 16: Quiz: LIMIT
- Concept 17: Solution: LIMIT
- Concept 18: Video: ORDER BY
- Concept 19: Quiz: ORDER BY
- Concept 20: Solutions: ORDER BY
- Concept 21: Video: ORDER BY Part II
- Concept 22: Quiz: ORDER BY Part II
- Concept 23: Solutions: ORDER BY Part II
- Concept 24: Video: WHERE
- Concept 25: Quiz: WHERE
- Concept 26: Solutions: WHERE
- Concept 27: Video: WHERE with Non-Numeric Data
- Concept 28: Quiz: WHERE with Non-Numeric
- Concept 29: Solutions: WHERE with Non-Numeric
- Concept 30: Video: Arithmetic Operators
- Concept 31: Quiz: Arithmetic Operators
- Concept 32: Solutions: Arithmetic Operators
- Concept 33: Text: Introduction to Logical Operators
- Concept 34: Video: LIKE
- Concept 35: Quiz: LIKE
- Concept 36: Solutions: LIKE
- Concept 37: Video: IN
- Concept 38: Quiz: IN
- Concept 39: Solutions: IN
- Concept 40: Video: NOT
- Concept 41: Quiz: NOT
- Concept 42: Solutions: NOT
- Concept 43: Video: AND and BETWEEN
- Concept 44: Quiz: AND and BETWEEN
- Concept 45: Solutions: AND and BETWEEN
- Concept 46: Video: OR
- Concept 47: Quiz: OR
- Concept 48: Solutions: OR
- Concept 49: Text: Recap & Looking Ahead
- Lesson 02: SQL JoinsIn this lesson, you will learn how to combine data from multiple tables together.
- Concept 01: Video: Motivation
- Concept 02: Video: Why Would We Want to Split Data Into Separate Tables?
- Concept 03: Video: Introduction to JOINs
- Concept 04: Text + Quiz: Your First JOIN
- Concept 05: Solution: Your First JOIN
- Concept 06: Text: ERD Reminder
- Concept 07: Text: Primary and Foreign Keys
- Concept 08: Quiz: Primary - Foreign Key Relationship
- Concept 09: Text + Quiz: JOIN Revisited
- Concept 10: Video: Alias
- Concept 11: Quiz: JOIN Questions Part I
- Concept 12: Solutions: JOIN Questions Part I
- Concept 13: Video: Motivation for Other JOINs
- Concept 14: Video: LEFT and RIGHT JOINs
- Concept 15: Text: Other JOIN Notes
- Concept 16: LEFT and RIGHT JOIN
- Concept 17: Solutions: LEFT and RIGHT JOIN
- Concept 18: Video: JOINs and Filtering
- Concept 19: Quiz: Last Check
- Concept 20: Solutions: Last Check
- Concept 21: Text: Recap & Looking Ahead
- Lesson 03: SQL AggregationsIn 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.
- Concept 01: Video: Introduction to Aggregation
- Concept 02: Video: Introduction to NULLs
- Concept 03: Video: NULLs and Aggregation
- Concept 04: Video + Text: First Aggregation - COUNT
- Concept 05: Video: COUNT & NULLs
- Concept 06: Video: SUM
- Concept 07: Quiz: SUM
- Concept 08: Solution: SUM
- Concept 09: Video: MIN & MAX
- Concept 10: Video: AVG
- Concept 11: Quiz: MIN, MAX, & AVG
- Concept 12: Solutions: MIN, MAX, & AVG
- Concept 13: Video: GROUP BY
- Concept 14: Quiz: GROUP BY
- Concept 15: Solutions: GROUP BY
- Concept 16: Video: GROUP BY Part II
- Concept 17: Quiz: GROUP BY Part II
- Concept 18: Solutions: GROUP BY Part II
- Concept 19: Video: DISTINCT
- Concept 20: Quiz: DISTINCT
- Concept 21: Solutions: DISTINCT
- Concept 22: Video: HAVING
- Concept 23: HAVING
- Concept 24: Solutions: HAVING
- Concept 25: Video: DATE Functions
- Concept 26: Video: DATE Functions II
- Concept 27: Quiz: DATE Functions
- Concept 28: Solutions: DATE Functions
- Concept 29: Video: CASE Statements
- Concept 30: Video: CASE & Aggregations
- Concept 31: Quiz: CASE
- Concept 32: Solutions: CASE
- Concept 33: Text: Recap
- Lesson 04: SQL Subqueries & Temporary TablesIn this lesson, you will be learning to answer much more complex business questions using nested querying methods - also known as subqueries.
- Concept 01: Video: Introduction
- Concept 02: Video: Introduction to Subqueries
- Concept 03: Video + Quiz: Write Your First Subquery
- Concept 04: Solutions: Write Your First Subquery
- Concept 05: Text: Subquery Formatting
- Concept 06: Video: More On Subqueries
- Concept 07: Quiz: More On Subqueries
- Concept 08: Solutions: More On Subqueries
- Concept 09: Quiz: Subquery Mania
- Concept 10: Solution: Subquery Mania
- Concept 11: Video: WITH
- Concept 12: Text + Quiz: WITH vs. Subquery
- Concept 13: Quiz: WITH
- Concept 14: Solutions: WITH
- Concept 15: Video: Subquery Conclusion
- Lesson 05: SQL Data CleaningCleaning data is an important part of the data analysis process. You will be learning how to perform data cleaning using SQL in this lesson.
- Concept 01: Video: Introduction to SQL Data Cleaning
- Concept 02: Video: LEFT & RIGHT
- Concept 03: Quiz: LEFT & RIGHT
- Concept 04: Solutions: LEFT & RIGHT
- Concept 05: Video: POSITION, STRPOS, & SUBSTR
- Concept 06: Quiz: POSITION, STRPOS, & SUBSTR - AME DATA AS QUIZ 1
- Concept 07: Solutions: POSITION, STRPOS, & SUBSTR
- Concept 08: Video: CONCAT
- Concept 09: Quiz: CONCAT
- Concept 10: Solutions: CONCAT
- Concept 11: Video: CAST
- Concept 12: Quiz: CAST
- Concept 13: Solutions: CAST
- Concept 14: Video: COALESCE
- Concept 15: Quiz: COALESCE
- Concept 16: Solutions: COALESCE
- Concept 17: Video + Text: Recap
- Lesson 06: [Advanced] SQL Window FunctionsCompare one row to another without doing any joins using one of the most powerful concepts in SQL data analysis: window functions.
- Concept 01: Video: Introduction to Window Functions
- Concept 02: Video: Window Functions 1
- Concept 03: Quiz: Window Functions 1
- Concept 04: Solutions: Window Functions 1
- Concept 05: Quiz: Window Functions 2
- Concept 06: Solutions: Window Functions 2
- Concept 07: Video: ROW_NUMBER & RANK
- Concept 08: Quiz: ROW_NUMBER & RANK
- Concept 09: Solutions: ROW_NUMBER & RANK
- Concept 10: Video: Aggregates in Window Functions
- Concept 11: Quiz: Aggregates in Window Functions
- Concept 12: Solutions: Aggregates in Window Functions
- Concept 13: Video: Aliases for Multiple Window Functions
- Concept 14: Quiz: Aliases for Multiple Window Functions
- Concept 15: Solutions: Aliases for Multiple Window Functions
- Concept 16: Video: Comparing a Row to Previous Row
- Concept 17: Quiz: Comparing a Row to Previous Row
- Concept 18: Solutions: Comparing a Row to Previous Row
- Concept 19: Video: Introduction to Percentiles
- Concept 20: Video: Percentiles
- Concept 21: Quiz: Percentiles
- Concept 22: Solutions: Percentiles
- Concept 23: Video: Recap
- Lesson 07: [Advanced] SQL Advanced JOINs & Performance TuningLearn 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.
- Concept 01: Video: Introduction to Advanced SQL
- Concept 02: Text + Images: FULL OUTER JOIN
- Concept 03: Quiz: FULL OUTER JOIN
- Concept 04: Solutions: FULL OUTER JOIN
- Concept 05: Video: JOINs with Comparison Operators
- Concept 06: Quiz: JOINs with Comparison Operators
- Concept 07: Solutions: JOINs with Comparison Operators
- Concept 08: Video: Self JOINs
- Concept 09: Quiz: Self JOINs
- Concept 10: Solutions: Self JOINs
- Concept 11: Video: UNION
- Concept 12: Quiz: UNION
- Concept 13: Solutions: UNION
- Concept 14: Video: Performance Tuning Motivation
- Concept 15: Video + Quiz: Performance Tuning 1
- Concept 16: Video: Performance Tuning 2
- Concept 17: Video: Performance Tuning 3
- Concept 18: Video: JOINing Subqueries
- Concept 19: Video: SQL Completion Congratulations
Part 10 (Elective): Tableau Lessons
- Lesson 01: Basic SQLIn 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.
Module 01: Data Visualization
- Lesson 01: Intro to Data VisualizationIn this lesson, you will get a glimpse at different data visualizations and when they are used.
- Concept 01: Data Visualization Introduction
- Concept 02: Why Do We Use Data Visualizations?
- Concept 03: Motivation for Data Visualization
- Concept 04: Further Motivation
- Concept 05: Data Types Review
- Concept 06: Identifying Data Types
- Concept 07: Univariate Plots
- Concept 08: Univariate Plots
- Concept 09: Scatter Plots
- Concept 10: Quizzes On Scatter Plots
- Concept 11: Correlation Coefficients
- Concept 12: Correlation Coefficient Quizzes
- Concept 13: Line Plots
- Concept 14: What is the Question?
- Concept 15: What About with More Than Two Variables?
- Concept 16: Multiple Variables Quiz
- Concept 17: Why Data Dashboards
- Concept 18: Introduction to Data Dashboards
- Concept 19: Quiz On Visual Encodings
- Concept 20: Recap
- Concept 21: What’s Next?
- Lesson 02: DesignIn this lesson, you will learn about visual encodings, and best practices for building data visualizations.
- Concept 01: Introduction
- Concept 02: Lesson Overview
- Concept 03: Exploratory vs. Explanatory Analyses
- Concept 04: Quiz: Exploratory vs. Explanatory
- Concept 05: What Makes a Bad Visual?
- Concept 06: What Experts Say About Visual Encodings
- Concept 07: Chart Junk
- Concept 08: Data Ink Ratio
- Concept 09: Design Integrity
- Concept 10: Bad Visual Quizzes (Part I)
- Concept 11: Bad Visual Quizzes (Part II)
- Concept 12: Text: Effective Explanatory Visual Recap
- Concept 13: Using Color
- Concept 14: Designing for Color Blindness
- Concept 15: Shape, Size, & Other Tools
- Concept 16: General Design Tips
- Concept 17: Good Visual
- Concept 18: Tell A Story
- Concept 19: Same Data, Different Stories
- Concept 20: Quizzes on Data Story Telling
- Concept 21: Recap
- Concept 22: Onwards!
- Lesson 03: Data Visualizations in TableauIn this lesson, you will learn how to make visualizations in Tableau. Get excited - it is about to get awesome!
- Concept 01: Video: What is Tableau?
- Concept 02: Text: Installing Tableau
- Concept 03: Video: How This Lesson Is Structured?
- Concept 04: Text: Outline of Topics Covered
- Concept 05: Commas vs Periods
- Concept 06: Video: Connecting to Data
- Concept 07: Text: Connecting to Data Recap
- Concept 08: Quiz: Connecting to Data
- Concept 09: Video: Combining Data
- Concept 10: Text: Combining Data Recap
- Concept 11: Quiz: Combining Data
- Concept 12: Video: What Can You Create In Tableau?
- Concept 13: Video: Worksheets
- Concept 14: Text: Worksheets
- Concept 15: Quiz: Worksheets
- Concept 16: Text: Saving to Tableau Public
- Concept 17: Video: Aggregations
- Concept 18: Text: Aggregations
- Concept 19: Quiz: Aggregations
- Concept 20: Video: Hierarchies
- Concept 21: Text: Hierarchies
- Concept 22: Quiz: Hierarchies
- Concept 23: Video: Marks & Filters
- Concept 24: Text: Marks & Filters I
- Concept 25: Quiz: Marks & Filters I
- Concept 26: Text: Marks & Filters II
- Concept 27: Quiz: Marks & Filters II
- Concept 28: Video: Show Me
- Concept 29: Text: Show Me
- Concept 30: Quiz: Show Me
- Concept 31: Video: Small Multiples & Dual Axis
- Concept 32: Text: Small Multiples & Dual Axis
- Concept 33: Text: Map Configuration
- Concept 34: Quiz: Small Multiples
- Concept 35: Quiz: Dual Axis
- Concept 36: Video: Groups & Sets
- Concept 37: Text: Groups & Sets
- Concept 38: Quiz: Groups
- Concept 39: Quiz: Sets
- Concept 40: Video: Calculated Fields
- Concept 41: Text: Calculated Fields
- Concept 42: Quiz: Calculated Fields
- Concept 43: Video: Table Calculations
- Concept 44: Text: Table Calculations
- Concept 45: Quiz: Table Calculations
- Concept 46: Text: Recap
- Concept 47: Video: What’s Next?
- Lesson 04: Making Dashboards & Stories in TableauIn this lesson, you will learn from a Tableau expert, and start putting together your own dashboards and stories.
- Concept 01: Video: Communicating With Your Data
- Concept 02: Video + Text: What’s Ahead?
- Concept 03: Video: Hierarchies with Trina
- Concept 04: Quiz: Hierarchies with Trina
- Concept 05: Video: Building Dashboards & Stories with Trina
- Concept 06: Text: General Notes for Building Data Dashboards with Trina
- Concept 07: Text: General Notes for Building Stories
- Concept 08: Quiz: Building Dashboards & Stories with Trina
- Concept 09: Video: Extra Practice with Dashboards
- Concept 10: Quiz: Extra Practice with Dashboards
- Concept 11: Text: Lesson Recap
- Concept 12: Video: Congratulations!
- Lesson 01: Intro to Data VisualizationIn this lesson, you will get a glimpse at different data visualizations and when they are used.
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