Program Overview
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.
Introduction to the Business Analytics1:03
Access the Career Portal
How Do I Find Time for My Nanodegree?00:00
Predicting Diamond Prices
02. Introduction to Predictive Models1:26
03. Example Project
04. Project Details
05. Supporting Materials
Project Description – Predicting Diamond Prices
Project Rubric – Predicting Diamond Prices
The Analytical Problem Solving Framework
02. Course Introduction1:43
03. The Problem Solving Framework0:33
04. Business Issue Understanding1:47
05. Data Understanding1:31
06. Data Preparation1:47
07. Analysis and Modeling1:34
08. Validation1:11
09. Presentation and Visualization1:18
Selecting an Analytical Methodology
02. Non-Predictive Business Problems 100:00
02. Non-Predictive Business Problems 200:00
02. Non-Predictive Business Problems 300:54
02. Non-Predictive Business Problems 400:45
03. Classifying Business Problems
03. Classifying Business Problems
04. Predictive Business Problems0:51
04. Predictive Business Problems 20:21
04. DR vs DP Quiz
05. Data Poor Business Problems0:49
06. Data Rich Business Problems0:47
07. Numeric Non-Numeric Outcomes00:00
08. Numeric or Classification Quiz
09. Introduction to Numeric Models00:00
10. Introduction to Non-Numeric Models00:00
11. Determining Appropriate Models Quiz
12. Model Selection Assessment
12. Quiz
13. Lesson Summary00:00
Linear Regression
01. The Business Problem00:00
02. Approaching the Business Problem00:00
03. Data Understanding Quiz
04. Data Understanding Solution1:42
05. The Problem Solving Framework00:00
06. Introduction to Linear Regression00:00
07. Linear Equations in Google Sheets00:00
08. Linear Regression Validation00:00
08. Linear Regression Validation 200:00
09. Simple Linear Regression Quiz
09. Quiz
10. Simple Linear Regression Solution00:00
11. Introduction to Multiple Linear Regression00:00
12. Multiple Linear Regression Concepts00:00
13. Multiple Linear Regression with Excel00:00
13. Multiple Linear Regression with Excel 200:00
13. Multiple Linear Regression with Excel00:00
14. Multiple Linear Regression Validation00:00
15. Linear Regression with Categorical Variables
16. Dummy Variable Quiz
16. Quiz
17. Introduction to Alteryx2:51
18. Downloading Alteryx
19. Alteryx Walkthrough
20. Alteryx Tutorials
21. Building your First Model in Alteryx2:48
22. Running the Model0:48
23. Interpreting Linear Regression Results
24. Evaluating an Equation
25. Evaluating an Equation Solution
26. Analysis Summary00:00
27. Course Recap00:00
28. Learning Summary
Practice Project
01. Practice Project Intro
02. New Alteryx Tools00:00
02.2 New Alteryx Tools00:00
02.3 New Alteryx Tools00:00
03. Practice Project Details
04. Practice Project Solution
05. Solution Walkthrough
06. Input and Visualize Data00:00
06.2 Input and Visualize Data00:00
07. Build the Model00:00
08. Score the Model
09. On to the Project!
Predicting Catalog Demand
01. Introducing the Project00:00
02. Project Overview
03. Project Details
04. Supporting Materials
05. Tips
06. Verify Answers for Project
07. Typical Reasons Submissions are not Passed
08. Rubric Help
Project Description – Predicting Catalog Demand
Project Rubric – Predicting Catalog Demand
Understanding Data
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
together.
01. Program Hosts – Course Overview00:00
02. Course Intro00:00
03. Lesson Introduction00:00
04. Before You Get Started
05. Structure of Data00:00
06. Three Types of Data Structure00:00
07. Classify Data
07. Classify Data
07.2 Classify Data
07.2 Classify Data
07.3 Classify Data
07.3 Classify Data
07.4 Classify Data
07.4 Classify Data
08. Data Sources – Files00:00
09. Course Outline00:00
10. Data Sources – File Example00:00
11. Data Sources – File Example Continued00:00
12. Alteryx Exercise
13. Alteryx Exercise – Solution00:00
14. Data Sources – Databases00:00
15. Data Sources – Web-based Sources00:00
15.2 Data Sources – Web-based Sources00:00
16. Data Sources – Web-scraping Exercise
17. Data Sources – Web-scraping Solution00:00
18. Introduction to Data Types00:00
19. Data Types00:00
20. Identify Data Types Exercise
20. Identify Data Types Exercise
21. Data Types in Alteryx00:00
22. Data Types Exercise in Alteryx
23. Data Types Exercise in Alteryx Solution00:00
24. Wrap Up00:00
Data Issues
01. Lesson Introduction00:00
02. Interview – Importance of Data Cleaning00:00
03. Dirty Data00:00
04. Examples of Dirty Data00:00
05. Dirty Data – Parsing00:00
06. Dirty Data – Parsing Example in Alteryx00:00
07. Parsing a Phone Number00:00
08. Parsing a Phone Number Solution
09. Dirty Data – Extra Characters00:00
10. Dirty Data – Extra Characters Example00:00
11. Dirty Data – Extra Characters Exercise
12. Dirty Data – Extra Characters Solution00:00
13. Dirty Data – Duplicate Data00:00
14. Dirty Data – Duplicate Data Example00:00
15. Deduping – Exercise
16. Deduping – Solution00:00
17. Missing Data00:00
18. What Does Missing Data Look Like00:00
19. Why Do We Care About Missing Data00:00
20. Dealing with Missing Data – Deletion Exercise
20. Dealing with Missing Data – Deletion Exercise
21. Dealing with Missing Data – Deletion Solution00:00
22. Effect of Deletion on Model00:00
23. Dealing with Missing Data – Deletion Exercise 2
24. Dealing with Missing Data – Deletion Solution 200:00
25. Imputation00:00
26. Dealing with Missing Data – Imputation00:00
27. Dealing with Missing Data – Imputation Exercise
28. Dealing with Missing Data – Imputation Solution00:00
29. Advanced Methods for Dealing with Missing Data00:00
30. Missing Data Factors to Consider
31. Introduction to Outliers00:00
32. Interview – Importance of Catching Outliers00:00
33. What is an Outlier00:00
34. Why Do We Care About Outliers00:00
35. Effect of Outliers on Our Model
36. Effect of Outliers – Exercise
37. Effect of Outliers – Solution00:00
38. Identifying Outliers00:00
39. Identifying Outliers Exercise
40. Dealing with Outliers00:00
41. Outliers Quiz 1
42. Outliers Quiz 2
43. Outliers Quiz 3
44. Wrap Up00:00
Data Formatting
01. Lesson Introduction00:00
02. Transposing Data00:00
04. Transposing – Exercise
03. Transposing in Alteryx00:00
05. Transposing – Solution00:00
06. Aggregating Data00:00
07. Aggregating Data – Example00:00
08. Aggregating Data – Exercise00:00
08. Aggregating Data – Exercise
09. Aggregating Data – Solution00:00
10. Cross Tabulation00:00
11. Cross Tabulation – Example00:00
12. Cross Tabulation – Exercise
13. Cross Tabulation – Solution00:00
14. Wrap Up00:00
Data Blending
01. Lesson Introduction00:00
02. Unioning Datasets00:00
03. Union – Example00:00
04. Union – Exercise
05. Union – Solution00:00
06. Joining Datasets00:00
07. Joining Datasets – Example00:00
08. Joining Datasets – Exercise
09. Joining Datasets – Solution00:00
10. Fuzzy Matching00:00
11. Fuzzy Matching Continued00:00
12. Fuzzy Matching – Example00:00
13. Fuzzy Matching – Exercise
14. Fuzzy Matching – Solution00:00
15. Spatial Matching
16. Spatial Blending00:00
17. Spatial Blending – Example00:00
18. Spatial Blending – Example Continued00:00
19. Spatial Blending – Exercise
20. Spatial Blending – Solution00:00
21. Wrap Up00:00
22. Closing Remarks00:00
23. Learning Summary
Practice Project
01. Practice Project Intro
02. Practice Project Details
03. Practice Project Solution
04. Data Understanding
05. Data Formatting and Blending
06. Data Cleaning
07. Outliers
Create an Analytical Dataset
01. Introducing Project 200:00
02. Project Overview
03. Project Details
04. Supporting Materials
05. Project Checklist
Project Description – Create an Analytical Dataset
Project Rubric – Create an Analytical Dataset
Selecting Predictor Variables
01. Overview
02. Choosing Predictor Variables00:00
03. Selecting Predictor Variables – Quiz
04. Selecting Predictor Variables – Solution
05. Non-Duplicate Predictor Variables00:00
06. Predictor Variables – Correlation1:20
07. Predictor Variables – Correlation Continued00:00
08. Correlation Plots00:00
09. Correlation Plots in Alteryx00:00
10. Predictor Variables – Correlation Quiz
10. Predictor Variables – Correlation Quiz
10.2 Predictor Variables – Correlation Quiz
10.2 Predictor Variables – Correlation Quiz
11. Preparing to Model00:00
12. Preparing to Model in Alteryx00:00
14. Data Preparation – Quiz
13. Data Preparation Solution – Counting Null Values00:00
15. Data Preparation Solution – Visualizing Data00:00
16. Data Preparation Solution – Dealing with Null Values00:00
17. Preparing to Model Categorical Variables00:00
18. Preparing to Model Categorical Variables in Alteryx00:00
19. Wrap Up
Select Location of a New Petstore
01. Project Overview
02. Project Details
03. Supporting Materials
Project Description – Select Location of a New Petstore
Project Rubric – Select Location of a New Petstore
Intro to Data Visualization
01. Data Visualization Introduction00:00
02. Why Do We Use Data Visualizations00:00
03. Motivation for Data Visualization
03. Quiz
04. Further Motivation00:00
05. Data Types Review00:00
06. Practice identifying data types
07. Univariate Plots00:00
08. Univariate Plots
09. Scatter Plots00:00
10. Quizzes On Scatter Plots
10. Quizzes On Scatter Plots
10.2 Quizzes On Scatter Plots
10.2 Quizzes On Scatter Plots
10.3 Quizzes On Scatter Plots
10.3 Quizzes On Scatter Plots
11. Correlation Coefficients00:00
12. Correlation Coefficient Quizzes
13. Line Plots00:00
14. What is the Question00:00
15. What About with More Than Two Variables00:00
16. Multiple Variables Quiz
16. Multiple Variables Quiz
17. Why Data Dashboards00:00
18. Introduction to Data Dashboards00:00
19. Quiz On Visual Encodings
19. Quiz On Visual Encodings
20. Recap
21. What’s Next00:00
Design
01. Introduction00:00
02. Lesson Overview00:00
03. Exploratory vs. Explanatory Analyses00:00
04. Quiz: Exploratory vs. Explanatory
05. What Makes a Bad Visual00:00
06. What Experts Say About Visual Encodings00:00
07. Chart Junk00:00
08. Data Ink Ratio00:00
09. Design Integrity00:00
10. Bad Visual Quizzes (Part I)
10. Bad Visual Quizzes (Part I)
10.2 Bad Visual Quizzes (Part I)
10.2 Bad Visual Quizzes (Part I)
11. Bad Visual Quizzes (Part II)
11. Bad Visual Quizzes (Part II)
11.2 Bad Visual Quizzes (Part II)
11.2 Bad Visual Quizzes (Part II)
11.3 Bad Visual Quizzes (Part II)
12. Text Effective Explanatory Visual Recap
13. Using Color00:00
14. Designing for Color Blindness00:00
15. Shape, Size, Other Tools00:00
16. General Design Tips00:00
17. Good Visual
18. Tell A Story00:00
19. Same Data, Different Stories00:00
20. Quizzes on Data Story Telling
20. Quizzes on Data Story Telling
20.2 Quizzes on Data Story Telling
20.2 Quizzes on Data Story Telling
20.3 Quizzes on Data Story Telling
21. Recap
22. Onwards!00:00
Data Visualizations in Tableau
01. Video What is Tableau00:00
02. Text Installing Tableau00:00
03. Video How This Lesson Is Structured00:00
04. Text Outline of Topics Covered
05. Commas vs Periods
06. Video Connecting to Data1:32
07. Text Connecting to Data Recap
08. Quiz Connecting to Data
09. Video Combining Data0:43
10. Text Combining Data Recap
08. Quiz: Connecting to Data
11. Quiz Combining Data
12. Video What Can You Create In Tableau00:00
13. Video Worksheets2:15
14. Text Worksheets
15. Quiz Worksheets
15. Quiz: Worksheets
16. Text Saving to Tableau Public
17. Video Aggregations00:00
18. Text Aggregations
19. Quiz Aggregations
19. Quiz: Aggregations
20. Video Hierarchies00:00
21. Text Hierarchies
22. Quiz Hierarchies
23. Video Marks Filters00:00
24. Text Marks Filters I
25. Quiz Marks Filters I
25. Quiz Marks Filters I
26. Text Marks Filters II
27. Quiz Marks Filters II
28. Video Show Me00:00
29. Text Show Me
30. Quiz Show Me
30. Quiz Show Me
31. Video Small Multiples Dual Axis00:00
32. Text Small Multiples Dual Axis
33. Text Map Configuration
34. Quiz Small Multiples
34. Quiz Small Multiples
35. Quiz Dual Axis
36. Video Groups Sets00:00
37. Text Groups Sets
38. Quiz Groups
39. Quiz Sets
39. Quiz Sets
40. Video Calculated Fields00:00
41. Text Calculated Fields
42. Quiz Calculated Fields
42. Quiz: Calculated Fields
43. Video Table Calculations00:00
44. Text Table Calculations
45. Quiz Table Calculations
45. Quiz: Table Calculations
46. Text Recap
47. Video What’s Next00:00
02.2 Text Installing Tableau00:00
Making Dashboards Stories in Tableau
01. Video Communicating With Your Data00:00
02. Video + Text What’s Ahead00:00
03. Video Hierarchies with Trina00:00
04. Quiz Hierarchies with Trina
04. Quiz Hierarchies with Trina
04.2 Quiz Hierarchies with Trina
04.2 Quiz: Hierarchies with Trina
04.3 Quiz Hierarchies with Trina
04.3 Quiz: Hierarchies with Trina
05. Video Building Dashboards Stories with Trina00:00
06. Text General Notes for Building Data Dashboards with Trina
07. Text General Notes for Building Stories00:00
08. Quiz: Building Dashboards & Stories with Trina
09. Video Extra Practice with Dashboards00:00
10. Quiz Extra Practice with Dashboards
11. Text Lesson Recap
12. Video Congratulations!00:00
Visualizing Movie Data
01. Introducing Project 300:00
02. Project Overview
03. Project Details
04. Supporting Materials
05. Tableau Public Tableau Desktop
Project Description – Visualize Movie Data
Project Rubric – Visualize Movie Data
Classification Problems
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.
01. Overview
02. Lesson Introduction00:00
03. Course Outline00:00
05. Classification Examples00:00
06. Binary vs Non-Binary – Exercise
07. Binary vs Non-Binary – Solution00:00
08. Wrap Up
Binary Classification Models
01. Binary Classification Problems00:00
02. Logistic Regression00:00
03. Logistic Regression – Continued00:00
04. Logistic Regression – Example00:00
05. Logistic Regression – Quiz
06. Logistic Regression – Solution00:00
07. Logistic Regression – Stepwise00:00
08. Logistic Regression – Stepwise in Alteryx00:00
09. Logistic Regression – Stepwise Quiz
10. Logistic Regression – Stepwise Solution00:00
11. Validating Models00:00
12. Logistic Regression – Stepwise Validation00:00
13. Introduction to Decision Tree Modeling00:00
14. Decision Tree – Example00:00
15. Decision Tree – Models in Alteryx00:00
16. Decision Tree – Results00:00
16. Decision Tree – Results 200:00
17. Decision Tree – Quiz
18. Decision Tree Solution
19. Decision Tree – Validation00:00
20. Introduction to Model Comparison00:00
21. Model Comparison – Example00:00
22. Scoring the Model00:00
23. Scoring the Model – Quiz 1
23. Scoring the Model – Quiz 1
24. Scoring the Model – Example00:00
25. Scoring the Model – Quiz 2
26. Wrap Up
27. Learning Summary
Non-Binary Classification Models
01. Non-Binary Classification Problems00:00
02. Decision Tree00:00
03. Decision Tree – Quiz
04. Decision Tree – Solution00:00
05. Decision Tree – Validation00:00
06. Forest Model00:00
07. Forest Model Example00:00
08. Build a Forest Model00:00
09. Forest Model Results00:00
10. Build a Forest Model Continued00:00
11. Forest Model – Quiz 1
11. Forest Model – Quiz 1
11. Forest Model – Quiz 2
11. Forest Model – Quiz 2
12. Forest Model Validation – Quiz (Hidden)
13. Forest Model Validation00:00
14. Forest Model Outro00:00
15. Learning Summary
16. Boosted Model00:00
17. Boosted Model – Build Model00:00
18. Boosted Model – Results00:00
19. Boosted Model – Observe Results00:00
20. Boosted Model – Validation00:00
21. Boosted Model Outro00:00
22. Model Comparison00:00
23. Score the Missing Data – Quiz
24. Score the Missing Data – Solution00:00
25. Wrap Up00:00
26. Learning Summary
Predicting Default Risk
01. Project Overview
02. Project Details
03. Supporting Materials
04. Tips
05. Verify Answers for Project
06. Model Comparison Tool Errors
Project Description – Predicting Default Risk
Project Rubric – Predicting Default Risk
AB Testing Fundamentals
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.
01. Overview
02. Welcome to AB Testing00:00
03. Units00:00
04. Units Quiz
05. Treatment and Control Groups00:00
06. Experimental and Control Variables00:00
07. Variables
08. Control Variables00:00
09. Testing Correlation00:00
10. Lurking Variables00:00
11. Experimental Design00:00
12. Experimental Design Quiz
13. Experiment Duration00:00
14. Experiment Duration Quiz
15. Conclusion00:00
Randomized Design Tests
01. Intro to Randomized Design00:00
02. Selecting Variables in an Experiment00:00
03. Control Variables Quiz
04. Control Variables Solution00:00
05. Experiment Design and Setup00:00
06. Identify the Control Variables00:00
07. Sample Size00:00
08. Preparing the Data for Analysis00:00
09. Analyzing the Results00:00
10. Analyzing the Results Example00:00
11. Performing a T-test Quiz
12. Performing a T-test Solution
13. Analyzing Results in Alteryx00:00
14. Analyzing Results Quiz
15. Conclusion00:00
Matched Pair Design Tests
01. Introduction to Matched Pair Design00:00
02. Selecting Treatment Units00:00
03. Selecting Control Units00:00
04. Selecting Control Units Quiz
05. Selecting Control Units Solution
06. Selecting One Control Unit for a Treatment Unit00:00
07. Selecting Multiple Control Units for a Treatment Unit00:00
08. Matching Stores Example00:00
09. Matched Pairing Quiz
10. Matched Pairing Solution
11. Analyzing the Results Overview00:00
12. Paired T-test Quiz
13. Analyzing the Results with Alteryx00:00
14. Interpreting Results00:00
15. Analyzing Matched Pair Design Quiz
16. Analyzing Matched Pair Design Solution00:00
17. Conclusion00:00
18. Learning Summary
Matched Pair Practice
01. Introduction00:00
02. Pricing Elasticity Analysis Problem00:00
03. Select Treatment Units
04. Select Discrete Control Variables00:00
05. Select Continuous Control Variables Quiz
06. Select Continuous Control Variables
07. Select Continuous Control Variables00:00
08. Prepare for Test Quiz
09. Run Test00:00
10. Filter Calculate Date Fields00:00
11. Weekly Store Traffic Data00:00
12. Create Discrete Data Table00:00
13. Store List Data00:00
14. Sales Data Quiz
15. Sales Data Solution00:00
16. Preparing Control and Treatment Units00:00
16. Preparing Control and Treatment Units 200:00
16. Preparing Control and Treatment Units 300:00
17. Performing the Analysis00:00
18. Performing the Analysis Quiz
19. Performing the Analysis Solution
20. Conclusion00:00
AB Test a New Menu Launch
01. Project Overview
02. Project Details
03. Supporting Materials
04. Tips
05. Verify Project Answer
Project Description – AB Test a New Menu Launch
Project Rubric – AB Test a New Menu Launch
Fundamentals of 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 project.
01. Welcome to Time Series Forecasting00:00
02. Introduction to Time Series00:00
03. The Business Problem00:00
04. Time Series Fundamentals Quiz
05. Simple Forecasting Methods00:00
06. Time Series Components00:00
07. Trend00:00
08. Trends Quiz
08. Trends Quiz
08.2 Trends Quiz
08.2 Trends Quiz
08.3 Trends Quiz
08.3 Trends Quiz
08.4 Trends Quiz
08.4 Trends Quiz
08.5 Trends Quiz
08.5 Trends Quiz
09. Seasonality00:00
10. Seasonality Plot00:00
11. Cyclical Patterns00:00
12. Seasonal or Cyclical Quiz
12. Seasonal or Cyclical Quiz
12.2 Seasonal or Cyclical Quiz
12.2 Seasonal or Cyclical Quiz
12.3 Seasonal or Cyclical Quiz
12.3 Seasonal or Cyclical Quiz
12.4 Seasonal or Cyclical Quiz
12.4 Seasonal or Cyclical Quiz
13. Seasonal or Cyclical Solution
14. Outro00:00
ETS Models
01. Introduction to ETS Models00:00
02. Time Series Decomposition00:00
03. Identifying Additive or Multiplicative Terms00:00
04. Time Series Scenarios
05. Simple Exponential Smoothing
06. Simple Exponential Smoothing Quiz00:00
06. Simple Exponential Smoothing Quiz
07. Next Few Methods
08. Holt’s Linear Trend Method00:00
09. Exponential Trend Method00:00
10. Damped Trend Methods00:00
11. Holt-Winters Seasonal Method00:00
12. Overview So Far
13. Constructing an ETS Model00:00
14. Constructing an ETS Model Quiz
15. Constructing an ETS Model Solution
16. Learning Summary
ARIMA Models
01. Introduction to ARIMA Models00:00
02. ARIMA Models00:00
03. Stationarity00:00
04. Stationary vs Non-Stationary Quiz
05. Differencing00:00
06. Differencing Quiz
07. Differencing Solution00:00
08. Autocorrelation Function Plot00:00
09. Partial Autocorrelation Function Plot00:00
10. Autoregressive Component00:00
11. Moving Average Component00:00
12. ACF and PACF Plots Quiz
12. ACF and PACF Plots Quiz
12.2 ACF and PACF Plots Quiz
12.2 ACF and PACF Plots Quiz
13. Integrated Component00:00
14. Seasonal ARIMA Models00:00
15. Seasonal Differencing00:00
16. Seasonal Differencing Quiz
17. Seasonal AR and MA Terms00:00
18. Constructing an ARIMA Model00:00
19. Constructing an ARIMA Model Quiz
20. Constructing an ARIMA Model Solution
21. Learning Summary
Analyzing and Visualizing Results
01. Analyzing and Visualizing Forecasting Results00:00
02. Holdout Sample00:00
03. Residual Plots00:00
04. Visualizing Results00:00
05. Calculating Error00:00
06. Interpreting Measures of Error00:00
07. Interpreting Error
07. Interpreting Error
08. Akaike Information Criterion (AIC)00:00
09. Choosing the Best Model
10. Confidence Intervals
11. Outro
Forecast Video Game Sales
01. Project Overview
02. Project Details
03. Supporting Materials
Project Description – Forecast Video Game Demand
Project Rubric – Forecast Video Game Demand
04. Verify a Project Answer
Segmentation Fundamentals
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.
01. Welcome to the Course00:00
02. Standardization vs. Localization00:00
03. Grouping Exercise00:00
04. Grouping Exercise 200:00
05. Defining Segmentation and Clustering00:00
06. Distance00:00
08. Examples for Uses of Clustering00:00
09. Unsupervised Learning00:00
10. Business Problem Introduction00:00
07. Distance Quiz
Preparing Data for Clustering
01. Data Preparation Introduction0:43
02. Getting the Right Data1:38
03. Selecting Data Based on Objectives00:00
04. Examples of Selecting Data Based on Objectives00:00
05. Predetermined Bias in Transactional Data00:00
06. Selecting Data Quiz
07. Data Types in Clustering00:00
08. Data Quality00:00
09. Scaling00:00
10. Scaling Quiz
11. Data Prep Exercise00:00
12. Transforming Variables00:00
13. Visualizing the Data00:00
14. Lesson Summary00:00
Variable Reduction
01. Lesson Introduction00:00
02. Variable Reduction00:00
03. Variable Reduction Example00:00
04. Factor Analysis and PCA Overview00:00
04. Factor Analysis and PCA Overview 200:00
05. PCA Details00:00
07. PCA Practice Continued
08. PCA00:00
09. PCA Results00:00
10. Visualizing PCA Results00:00
11. Visualizing PCA Exercise
12. PCA Results 200:00
14. Finishing Off the PCA Data00:00
15. Lesson Summary00:00
06. PCA Practice
13. Evaluating PCA Results
Clustering Models
01. Clustering Techniques Introduction00:00
02. Hierarchical Clustering00:00
03. K-Centroid Clustering00:00
04. Comparison of the Two Methods00:00
05. How Many Clusters00:00
06. Subjectivity in Selecting Number of Clusters00:00
07. How Many Clusters – Hierarchy00:00
08. How Many Clusters – K-Centroid00:00
09. Cluster Validation in Alteryx00:00
10. Cluster Validation00:00
11. Selecting the Number of Clusters Quiz
12. Creating Clusters using K-Centroid00:00
13. Creating the Cluster Model00:00
14. Interpreting Cluster Results00:00
15. Applying the Model00:00
16. Wrap Up00:00
Validating and Applying Clusters
01. Lesson Introduction00:00
02. The Iterative Nature of Clustering00:00
03. External Validation00:00
04. Validating Through Visualization00:00
05. Validating Through Visualization 200:00
06. Validating Through Visualization 300:00
07. Communicating the Story and Ongoing Testing00:00
08. Conclusion00:00
09. Learning Summary
Segmenting the Countries of the World
01. Project Overview
02. Project Details
03. Supporting Materials
Project Description – Segmenting the Countries of the World
Project Rubric – Segmenting the Countries of the World
Combining Predictive Techniques
01. Project Overview
02. Task 1 Store Format
03. Task 2 New Stores
04. Task 3 Forecasting
05. Supporting Materials
06. Tips
Project Description – Combining Predictive Techniques
Project Rubric – Combining Predictive Techniques
01. Selecting an Analytical Methodology
Methodology Map
The Analytical Problem Solving framework helps systematically work through a business problem; however, it doesn’t help us understand which methodology to use.
The Methodology Map will help us as we decide which methodology to use to solve a business problem, and is meant to be used as we work our way through the Problem Solving Framework.
MM
